PHYSIOLOGICAL REVIEWS
Vol. 81, No. 2, April 2001
Printed in U.S.A.
Proprioception From a Spinocerebellar Perspective
G. BOSCO AND R. E. POPPELE
Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
http://physrev.physiology.org
0031-9333/01 $15.00 Copyright © 2001 the American Physiological Society
540
540
540
540
540
540
541
541
541
541
541
542
542
542
542
543
544
544
544
544
544
545
546
546
546
547
547
548
548
548
548
550
550
550
551
552
552
553
553
553
554
554
555
555
555
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
I. Introduction
A. Definition of proprioception
B. Sensory receptor basis
II. Importance of Proprioceptive Feedback for Motor Actions
A. Feed-forward control of directed movements
B. Loss of proprioception leads to direction and amplitude errors
C. Proprioception may be necessary to develop and calibrate internal models
D. Historical frameworks for proprioception organization
III. Spinal Proprioception: Historical Overview
A. Introduction of microelectrode technology
B. Emphasis on monosynaptic sensory connections
C. Topological projections and localized receptive fields
D. Receptor specificity and sensory integration
IV. A Model to Study Spinal Proprioception: The Dorsal Spinocerebellar Tract
A. Dorsal spinocerebellar tract projects proprioceptive information centrally
B. Location and morphology of DSCT neurons
V. Early Studies of Dorsal Spinocerebellar Tract
A. Electrical stimulation reveals direct sensory connections
B. Functional framework based on receptor type and localization
VI. Natural Stimulation
A. Responses to localized muscle and cutaneous stimulation
B. Recognition of potential complexity of DSCT circuitry
C. Functional framework remains focused on receptor type and localization
VII. Polysynaptic Pathways
A. Widespread sensory convergence onto DSCT
B. Further evidence for polysynaptic pathways
C. Localized stimuli affect most of the DSCT population
D. Connectivity of DSCT neurons outside Clarke’s column
VIII. Parallel Distributed Network
A. DSCT circuitry resembles a parallel distributed network
B. Similarity with other spinal sensory networks
IX. Information Encoded by Dorsal Spinocerebellar Tract Activity
A. Nonlinear interactions require a normal behavioral repertoire for study
B. New functional framework based on whole limb rather than localized parameters
C. DSCT neurons encode a linear representation of foot position and movement
X. Reference Frames to Formalize Relationships
A. Muscle- and joint-based reference frames
B. Limb- versus joint-based reference frames and biomechanical factors
XI. Interaction Between Biomechanical and Neural Factors
A. Joint-angle covariance reduces limb degrees of freedom
B. Implications of joint-angle covariance for motor behavior
C. Implications of joint-angle covariance for sensory representations
XII. Reference Frames for Dorsal Spinocerebellar Tract Activity
A. Can we distinguish between end point- and joint-based reference frames?
B. Evidence for a kinematic-based reference frame
XIII. How Does Dorsal Spinocerebellar Tract Relate to Higher Order Sensory Structures: Evidence From
Sensory Cortex and Cerebellum
A. Broad directional tuning
B. Representation of global limb parameters
C. A common coordinate system for encoding spatial information
D. Kinematic and kinetic representations
556
556
556
557
558
539
540
G. BOSCO AND R. E. POPPELE
XIV. Significance of Kinematic-Based Reference Frames for Dorsal Spinocerebellar Tract Activity
A. A basic kinematic reference frame for proprioception
B. Alternative proposal for proprioceptive feedback in cerebellar regulation of posture
XV. Summary
A. Role of limb biomechanics in global limb representations
B. Significance of kinematic-based representations
Volume 81
559
559
559
560
560
560
I. INTRODUCTION
A. Definition of Proprioception
The term proprioception was coined by Sherrington
(299) to describe the sensory information contributing to
a sense of self position and movement. The relationship of
proprioception to unconscious or more automatic functions has traditionally distinguished it from kinesthesis, or
the conscious sense of position and movement. Proprioception has also been associated with a distinct class of
sensory receptors, most notably those found in the muscles and related deep tissues, while kinesthesis has been
more closely associated with joint and cutaneous receptors (45, 78, 127). The framework we propose here considers how proprioceptive sensory information is organized as it relates to a sense of body position and
movement, but without any judgement about which sensory receptors may or may not be involved.
B. Sensory Receptor Basis
Current views of proprioception at the spinal level
have, however, been strongly influenced by our understanding about the sensory receptors that contribute to it (for
reviews, see Refs. 146, 199, 290). Some of these, the muscle
spindles for example, have been studied extensively so the
information they transmit to the nervous system is known in
great detail (for example, see Refs. 77, 137, 138, 196, 218, 219,
272, 277–280, 296, 325). This knowledge, combined with our
knowledge of receptor terminations and functional connectivity, has led to a general understanding of spinal proprio-
ception as a central expression of the sensory receptor input
which in turn details local parameters like muscle length and
muscle force. The intent of this review is to examine this
perspective in an historical context, and through that examination to provide a different framework for understanding
spinal proprioception that may be more in tune with current
views of sensorimotor processing in other central nervous
system (CNS) structures.
II. IMPORTANCE OF PROPRIOCEPTIVE
FEEDBACK FOR MOTOR ACTIONS
A. Feed-Forward Control of Directed Movements
Contemporary thinking about motor control has it that
many actions belonging to a daily repertoire of motor activity may actually be controlled in a feed-forward manner, i.e.,
without a direct contribution from sensory feedback (85,
159, 162, 176, 209, 229, 333). Thus a loss of proprioceptive
feedback does not prevent movement or the ability to carry
out motor tasks. Polit and Bizzi (268, 269) demonstrated this
by showing that deafferented monkeys could perform a
reach-to-target task successfully without visual aid. However, the behavior was not normal, suggesting further that
sensory information may play an important regulatory role
even for such over-learned movements as these.
B. Loss of Proprioception Leads to Direction
and Amplitude Errors
This issue has now been investigated in a number of
different behavioral contexts and with diverse experimental
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
Bosco, G., and R. E. Poppele. Proprioception From a Spinocerebellar Perspective. Physiol Rev 81: 539 –568,
2001.—This review explores how proprioceptive sensory information is organized at spinal cord levels as it relates
to a sense of body position and movement. The topic is considered in an historical context and develops a different
framework that may be more in tune with current views of sensorimotor processing in other central nervous system
structures. The dorsal spinocerebellar tract (DSCT) system is considered in detail as a model system that may be
considered as an end point for the processing of proprioceptive sensory information in the spinal cord. An analysis
of this system examines sensory processing at the lowest levels of synaptic connectivity with central neurons in the
nervous system. The analysis leads to a framework for proprioception that involves a highly flexible network
organization based in some way on whole limb kinematics. The functional organization underlying this framework
originates with the biomechanical linkages in the limb that establish functional relationships among the limb
segments. Afferent information from limb receptors is processed further through a distributed neural network in the
spinal cord. The result is a global representation of hindlimb parameters rather than a muscle-by-muscle or
joint-by-joint representation.
April 2001
SPINAL PROPRIOCEPTION
C. Proprioception May Be Necessary to Develop
and Calibrate Internal Models
A variant on the traditional reaching task was used in
another study with a similar group of patients to show
that a lack of proprioceptive information might also be
responsible for deficits in interjoint coordination (107,
287). More recently, Sainburg et al. (286) speculated further that “control of intersegmental dynamics is normally
dependent on proprioceptive information to update and
maintain neural representations of the musculoskeletal
system.” They found that sensory feedback was also required for adaptation to novel intersegmental dynamics.
Thus, in agreement with other studies of this genre (64,
110, 183, 297, 317), they concluded “the nervous system
uses sensory information to develop and recalibrate internal models of the musculoskeletal system itself.”
These studies, among an increasingly large number
on this subject, clearly outline the importance of a proprioception both as a continuous feedback process and
also as a component in the central processing of voluntary
movements (see Refs. 126, 161, 168, 222, 223, 262, 273 for
reviews). Although many of the higher order sensory
functions contributing to internal models or intersegmental coordination have been attributed to supraspinal
structures like the cerebellum (22, 109, 175, 294, 319, 320),
we would like to consider here that an organizational
framework for proprioception found at spinal cord levels
may also be consistent with such functions.
D. Historical Frameworks for
Proprioception Organization
In developing this framework we begin with an overview from a mostly historical perspective that outlines
some of the assumptions and observations that drove the
research and some of the conclusions that were drawn
from them. We show that at least three organizational
schemes, considered fundamental to spinal proprioception, were developed over a period of nearly 50 years,
beginning with the introduction of the microelectrode.
The earliest organizational model regarded primarily the
monosynaptic connections between sensory receptors
and spinal neurons, leading to a characterization of proprioception based primarily on muscle receptors and localized receptive fields. A later model that developed
largely from the use of natural stimulation and a closer
attention to polysynaptic pathways still focused on local
muscle-based or joint-based representations. A third organizational model, and one we focus on in the latter half
of this review, developed from the observation that spinal
proprioception may also be organized in terms of global
parameters representing the whole limb.
III. SPINAL PROPRIOCEPTION:
HISTORICAL OVERVIEW
A. Introduction of Microelectrode Technology
Much of the early work on spinal mechanisms begun
by Sherrington and his colleagues and contemporaries
(62, 192, 204) was concerned with global aspects of reflex
behavior and how sensory and motor mechanisms contributed to them. The emphasis was on behavioral relevance, and it developed from Sherrington’s concept that
reflexes formed a basis for a hierarchical organization of
the sensorimotor system. This top-down investigative approach was largely superceded however following the
introduction of the microelectrode in the 1950s. The technological breakthrough provided by the microelectrode
allowed investigators to examine functional anatomy in
great detail at the single-cell level, and it thereby focused
research efforts toward the behavior of individual neurons and the specific synaptic interactions between them.
B. Emphasis on Monosynaptic
Sensory Connections
Although the microelectrode opened many new avenues of inquiry, it was at the same time an effective
technological barrier to a further understanding of neuronal circuitry. The single-cell recording techniques of the
time were simply unable to deal effectively with multisynaptic connectivity and large-scale integrative circuits.
Therefore, great emphasis was placed on monosynaptic
connections, and their functional relevance was elevated
to the virtual exclusion of the more complex circuitry.
Indeed, it seemed logical that the most direct connections
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
approaches. Arm movements, particularly reaching movements, have continued to provide a preferred experimental
paradigm for investigating the contributions of sensory information in guiding voluntary motor actions (106, 167, 168).
For example, studies by the Ghez laboratory on deafferented
patients lacking proprioceptive feedback have provided a
number of relevant observations. These patients made large
directional and amplitude errors when performing a reaching task without visual feedback. The errors were associated
with differences in velocity and acceleration for movements
in different directions and, according to the authors’ interpretation, might have resulted from a failure to take the
inertial properties of the limb into account in programming
the initial trajectory (113). Because direction and amplitude
errors were partially compensated by allowing patients to
view their arm before or during the movement, the authors
proposed that visual feedback could assist proprioception
or even substitute for it in some way by updating an internal
model of the arm. (105).
541
542
G. BOSCO AND R. E. POPPELE
were the relevant ones and that less direct or indirect
connections were meant only to modify and/or modulate.
C. Topological Projections and Localized
Receptive Fields
This precise muscle-by-muscle organization may not,
however, represent a general organizational pattern. For
example, even simple reflex behavior involving more than
one joint shows clear interactions across joints (189).
Some of these interactions may result from specific reflex
pathways such as those linking muscles acting at different
joints (49, 239, 242, 244, 295), or the excitation of extensor
motoneurons throughout the hindlimb by ankle extensor
afferents (125). Heteronymous interactions may also involve presynaptic mechanisms as suggested by studies of
afferent-induced modulation of the soleus H reflex during
passive cycling movements (230, 231) and stretch of the
quadriceps muscle (232).
D. Receptor Specificity and Sensory Integration
The idea that the selective sensitivity or specificity of
sensory receptors carries over to the CNS has also influenced how experimental results have been interpreted.
This principle is emphasized by the concept of sensory
modality, which may be thought of as a class of sensations sharing a common stimulus quality. Although such
qualities may not precisely correspond to the specificity
of sensory receptors, it is often viewed in those terms. For
example, until relatively recently, it was considered well
established that position sense and kinesthesia are attributable to joint receptors rather than to muscle receptors
(e.g., Ref. 236). Joint-angle sensation was believed to
result from the central processing of information from
joint receptors that specifically encode joint angle (48, 82,
241, 331). However, it is now clear that muscle spindles,
once thought to encode exclusively individual muscle
lengths, are also major contributors to the kinesthetic
sense of position and movement (46, 51, 219 –221, 326,
327). This example illustrates how an ensemble of information from diverse sensory receptors may contribute to
a given “modality.”
IV. A MODEL TO STUDY SPINAL
PROPRIOCEPTION: THE DORSAL
SPINOCEREBELLAR TRACT
A. Dorsal Spinocerebellar Tract Projects
Proprioceptive Information Centrally
One of the major central projections of the muscle
receptors is over spinocerebellar systems. Thus the spinocerebellar neurons may be considered as a kind of end
point for the processing of proprioceptive sensory information at the lowest levels in the nervous system where
the synaptic connectivity between sensory receptors and
central neurons can be studied. Consequently, with the
advent of the microelectrode, spinocerebellar neurons
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
Two basic concepts from sensory physiology also
contributed significantly to the formulation of a general
view about the central organization of proprioception.
The concepts were the topographical projections of sensory information to and within the CNS, and receptor or
modality specificity within the CNS (235, 236, 265, 314).
As a consequence of the importance placed on these
concepts, much of the effort to understand spinal proprioception was directed at the receptors themselves and at
elucidating their specific connections in the spinal cord.
The result has been that relatively little attention was paid
to understanding whatever composite information might
be represented by ensembles of diverse receptors. Moreover, this receptor-specific approach persisted even
though most of the synaptic targets of the sensory receptors were found to receive convergent inputs from receptors having different locations and serving different modalities (1, 2, 23, 52, 60, 65, 71, 89, 148, 182, 234, 247, 256,
289). Some better known examples are the convergence
of different types of cutaneous receptors onto “wide dynamic range” cells of the spinothalamic system (226) and
the convergence of muscle spindle and tendon organ
afferents onto common sets of interneurons in specific
reflex pathways (224).
Nevertheless, the concept of topological projections
has provided a useful framework for understanding function in a number of sensory systems. In cases where the
sensory receptors exist in a two-dimensional array, such
as the retina or the skin, central organizations can be
readily analyzed in terms of a topological mapping that
conserves nearest neighbor relationships (e.g., Refs. 43,
44). It has been tempting, therefore, to extend this concept to proprioception, even though its receptors may not
be so simply arrayed. Muscle receptors for example are
distributed throughout the three-dimensional bulk of individual muscles, and their distribution is not uniform
across muscles. Nevertheless, some simple reflex organizations suggested that an appropriate “topology” for proprioception might be defined by the organization of muscles about a single joint, i.e., the agonists and antagonists.
For example, it is well established that spinal motoneurons receive direct synaptic input from muscle spindle
receptors in the muscle innervated by the motoneuron as
well as from synergistic agonists (for reviews, see Refs.
47, 93, 128, 218). Moreover, a consistent reciprocal relationship between receptors and their projections to antagonist muscles is also well established. (141, 193; for
reviews, see Refs. 19, 139).
Volume 81
April 2001
SPINAL PROPRIOCEPTION
oception (13–15, 198, 202). A parallel system serving the
forelimbs includes the direct cuneocerebellar and rostral
spinocerebellar tracts (118, 258) and other indirect pathways via the lateral reticular nucleus and the inferior
olive.
B. Location and Morphology of DSCT Neurons
The cells of origin of the DSCT are classically described as residing in Clarke’s column in Rexed’s lamina
VII of the lumbar and thoracic spinal cord segments (Fig.
1A; Refs. 30, 195, 255, 314, see also Ref. 257 for a review).
However, the development of anatomical tracers like
wheat germ agglutinin (WGA)-horseradish peroxidase
(HRP) led to the identification of several other groups of
neurons that also belong to the DSCT. These cells are
located throughout the intermediate and dorsal laminae
of the thoracic and lumbosacral segments of the spinal
cord (72, 210, 214, 217). DSCT axons terminate as mossy
FIG. 1. Neurons of the dorsal spinocerebellar tract (DSCT). A: camera lucida reconstruction of a Clarke’s column
DSCT neuron filled intracellularly with horseradish peroxidase (HRP). These are large multipolar neurons with a
dendritic arborization that extends beyond the boundaries of Clarke’s column (dashed line). [From Walmsley and Nicol
(324).] B: muscle receptor axons form giant synaptic terminals on Clarke’s column neurons. The morphology of a giant
synapse is illustrated by this reconstruction.[Modified from Szentagothai and Albert (315).] C: the giant synapses give rise
to large amplitude unitary excitatory postsynaptic potentials (EPSPs). An example is illustrated in this intracellular
recording from a DSCT neuron, showing a train of 2- to 4-mV unitary EPSPs evoked by a 50-g load placed on the soleus
tendon. [From Eide et al. (74).] D: maximal stimulation of the low-threshold afferents in a muscle nerve may activate
from 10 to 20 giant synapses synchronously. The record illustrated here shows a 20-mV maximal EPSP evoked in the
same neuron as C by stimulation of the gastrocnemius-soleus nerve. [From Eide et al. (76).]
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
became a particularly attractive target for study along
with the spinal motoneurons, since both were large cells
and accessible to intracellular recording, and they were
both direct targets for sensory input from muscle receptors.
Two major groups of spinocerebellar neurons contribute to dorsal and ventral spinocerebellar tracts (DSCT
and VSCT, respectively), and together they provide the
major direct sensory projection from the hindlimbs and
lower part of the trunk to the cerebellum. Sensory information from the hindlimbs is also relayed by indirect
spinoreticulocerebellar pathways and through at least
two olivocerebellar pathways, the direct spinoolivocerebellar and the indirect spinoreticuloolivocerebellar. We
consider here only the dorsal spinocerebellar neurons,
since they are most likely to represent the purely sensory
aspects of the proprioceptive information directed centrally. The ventral spinocerebellar neurons have been
shown to encode some form of motor or premotor signal
derived from descending pathways in addition to propri-
543
544
G. BOSCO AND R. E. POPPELE
fibers in the cerebellar cortex of lobules I–V in the anterior lobe and in the posterior lobe vermis and paramedian
lobe (117, 213, 215, 216). In addition to the cortical projection, there is recent evidence that DSCT fibers also
terminate in the medial and interpositus cerebellar nuclei
(211, 212).
V. EARLY STUDIES OF DORSAL
SPINOCEREBELLAR TRACT
A. Electrical Stimulation Reveals Direct
Sensory Connections
B. Functional Framework Based on Receptor Type
and Localization
For over a decade, electrical activation of afferent
input was the primary “functional” means used to examine the connectivity patterns between peripheral nerves
and DSCT neurons. Despite the anatomical complexity of
Clarke’s column shown to contain both large multipolar
projection neurons (Fig. 1A) and interneurons (194, 282),
most of the electrophysiological research focused on the
shortest latency responses to nerve stimulation. These
studies led to an elaborate set of divisions and subclasses
based on both modality and receptor specificity (201).
According to this classification, the DSCT consisted of
proprioceptive and exteroceptive subdivisions, each being further parceled into subclasses depending on the
main type of afferent input. Thus neurons in the proprio-
ceptive subdivision were shown to receive monosynaptic
excitation either from muscle spindles (primary or group
Ia afferents, with minor convergence from secondary or
group II afferents) or Golgi tendon organ (group Ib) afferents with a high degree of spatial discrimination (70,
200, 203). Another three groups of DSCT neurons were
distinguished within the exteroceptive subdivision. One
group was activated from touch and pressure receptors in
the skin, another from slowly adapting pressure receptors
in the footpads, and the third by receptors that elicited
flexion reflexes (133, 178, 201, 334).
Although little attention was paid to polysynaptic
pathways, it was nevertheless evident from even the earliest studies (191) that single shock electrical stimulation
evoked substantial long-latency responses. Because many
of these responses were inhibitory, they were viewed as
another example of the well-known surround inhibition
that was shown to provide a sharpening of point-to-point
resolution through contrast enhancement in the visual
system (132, 190, 191). This interpretation helped to reinforce the notion of a functional organization based on
precise topological projections. In fact, reviewing the current status of research on spinocerebellar systems in
1965, Oscarsson concluded: “ . . . DSCT [neurons] carry
information about proprioceptive and exteroceptive
events with high degree of spatial discrimination . . . used
in the fine coordination of posture and movement of the
individual limb” (257).
Figure 2 summarizes some of the main points of this
proposed organization. It shows a number of direct monosynaptic connections from specific receptor types (bold
arrows) and a few interneurons providing mostly disynaptic inhibition.
VI. NATURAL STIMULATION
A. Responses to Localized Muscle and
Cutaneous Stimulation
Later studies departed from the electrical stimulation
paradigm by employing more “natural” stimulation such
as the stretch of isolated muscles. The aim of these studies was to provide a more functional description of the
specific connectivity patterns between sensory afferents
and DSCT neurons. However, the focus was still on specific stimuli. For example, work by Jansen and Rudjord
and co-workers (153, 156) contributed to a further distinction within the “proprioceptive” division between neurons
receiving inputs from only primary or secondary muscle
spindle endings. Intracellular responses to muscle activation (Fig. 1C) also allowed an analysis of the functional
convergence of afferents from a specific muscle and its
effect on DSCT firing (152). The analysis suggested that
the convergence increased the fidelity of the information
transmission.
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
DSCT studies that began in the 1950s gave rise to a
model for central proprioception that consisted of spinal
projection neurons relaying receptor-specific and highly
localized sensory information to higher centers that integrate the information (in this case, to the cerebellum).
The earliest results showed that the synapses between muscle receptors and DSCT neurons, unlike those
with motoneurons, were “giant synapses” (see Fig. 1B,
Ref. 315) that could faithfully follow high-frequency repetitive stimulation of low-threshold muscle sensory fibers (Ia afferents; Refs. 132, 225). It was therefore reasonable to presume that transmission at this muscle
receptor-DSCT cell synapse comprised a high-fidelity relay. This interpretation was further supported by later
intracellular recordings showing that stimulation of these
same afferents evoked larger amplitude and longer duration excitatory postsynaptic potentials (EPSPs) in DSCT
neurons than in motoneurons (see Fig. 1, C and D; Refs.
59, 70, 74 –76, 225). Thus most of the earlier research on
the DSCT was based on the assumption that the direct,
monosynaptic input from sensory receptors was the only
functionally relevant input.
Volume 81
April 2001
545
SPINAL PROPRIOCEPTION
Attempts were also made using muscle stretch to
characterize the interaction between excitation and inhibition from various muscles onto individual DSCT neurons (151, 154, 155). These studies concentrated on the
cellular mechanisms of inhibition and concluded that
both presynaptic and postsynaptic mechanisms were involved.
A more in-depth analysis of the cutaneous input to
the DSCT was also attempted by Mann (205), who extended the earlier observations by Lundberg and Oscarsson through the use of natural cutaneous stimulation to
characterize receptive fields and receptor specificity. He
found that only about one-fifth of the recorded neurons
were strictly cutaneous, and their receptive fields were
not discretely organized, being larger proximally and occasionally being “broken receptive fields.” A small fraction of cells received a combination of cutaneous and
deep (interpreted as muscle) inputs, and interestingly, the
cutaneous and deep receptive fields were often not congruent. Although he did not study the inhibitory effects on
DSCT discharges systematically, he did note that cutaneous inhibitory fields had irregular shapes that were usually eccentric to and not surrounding the excitatory fields.
He also found that 20% of the DSCT neurons recorded in
this study could not be activated by any localized peripheral stimulus (mute cells).
B. Recognition of Potential Complexity
of DSCT Circuitry
Mann (206) pointed out the potential complexity of
DSCT organization emerging from this study again in a
later review. Although he did not dispute the classification
originally proposed by Lundberg and Oscarsson (see Fig.
2), he did raise concerns about oversimplifications that
were made about the functional organization of the DSCT.
Mann concluded the review by remarking: “The DSCT has
received a good deal of attention, perhaps because of its
accessibility and presumed simplicity. The accessibility is
real, but its simplicity may be brought into question . . . A
clear separation of cutaneous and muscle DSCT is no
[longer] possible . . . Thus it seems useful to think of the
DSCT as a mixed tract rather than to invent new terminology for the cutaneous subdivision, confusing an already somewhat muddy taxonomic picture”. Mann also
questioned the specificity of DSCT neurons originally
classified as group Ia and group II: “Coded in the discharge of group Ia and group II DSCT cells is the length of
a muscle under stretch, and the coded information has
been altered little by the imposition of the synapse . . . It
is well documented however, that receptors of more than
one muscle group can cause a given cell to discharge
leading one to question how well the length information
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
FIG. 2. The first organizational
scheme proposed for the dorsal spinocerebellar tract circuitry proposed by Lundberg and Oscarsson in the 1960s and later
reviewed by Mann in 1971 (206). They proposed two major subdivisions: a proprioceptive division (A) and an exteroceptive
division (B). This proposed organization
resulted from the emphasis placed on
monosynaptic connections and was based
on the use of electrical stimulation of peripheral nerves and the responses they
evoked in DSCT neurons. The sensory receptor origins of the afferents in the muscle nerves stimulated electrically were estimated from the stimulus thresholds. The
lowest threshold group Ia afferents were
from muscle spindle primary endings, and
the group Ib afferents were from Golgi
tendon organs. The higher threshold group
II afferents were from muscle spindle secondary endings. The inhibitory responses
observed with this paradigm were accounted for in the organizational scheme
by including inhibitory interneurons to
provide sign changes between sensory receptors and the DSCT neurons. Responses
to more intense stimuli that also evoke
flexion reflex responses were more complex and were accounted for in the proposed circuit by an oligosynaptic pathway
from the sensory receptors referred to as
flexor reflex afferents (FRA) to the DSCT
neurons in the exteroceptive division.
546
G. BOSCO AND R. E. POPPELE
Volume 81
for one muscle can be isolated from the DSCT discharge
when another muscle is stretched simultaneously.”
C. Functional Framework Remains Focused
on Receptor Type and Localization
VII. POLYSYNAPTIC PATHWAYS
A. Widespread Sensory Convergence Onto DSCT
This relay model was challenged when it could be
shown that converging polysynaptic pathways play an
extensive role in the activity of the DSCT neurons. The
issue was revisited through a series of studies that introduced more quantitative analytical techniques employing
a sensitive cross-correlation analysis (180, 249) to characterize DSCT responses to electrical stimulation of hindlimb peripheral nerves (179). The results of this analysis
showed that most DSCT neurons respond to stimulation
of flexor and extensor nerves in the proximal and distal
hindlimb. This implied a much larger pattern of convergence than was previously assumed from intracellular
recordings, and it contributed to the finding that the most
FIG. 3. Cross-correlation analysis revealed a variety of responses to
peripheral nerve stimulation and a greater degree of convergence than
had been seen with intracellular recording. Extracellular action potential recordings from a single DSCT neuron in response to electrical
stimulation of muscle nerves were used to generate cross-correlograms
to quantify the poststimulus time probability density of spike occurrence. These records show the time course of excitability changes from
the mean firing level (dashed lines) following single shocks to the
sensory axons in four different muscle nerves. They illustrate a convergence of sensory input from receptors in the hamstring and quadriceps
muscles proximally and the gastrocnemius-soleus and anterior tibial
muscles distally. A: type 1 (monosynaptic) response to hamstring nerve
stimulation. B: type 2 (inhibitory) response with long duration evoked by
quadriceps nerve stimulation. C: type 3 response (oligosynaptic excitatory) response to anterior tibial nerve stimulation that differs from the
type I monosynaptic response by its longer latency and often longer
duration. D: a very weak type 2 (inhibitory) response with a short
duration evoked by stimulation of the gastrocnemius-soleus muscle
nerve. [Redrawn from Knox et al. (179).]
common DSCT responses, accounting for ⬃80% of the
total responses, were polysynaptically mediated. Figure 3
shows examples from a single cell reported in that study
illustrating long-duration inhibitory (type 2) and longlatency excitatory (type 3) responses.
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
Although Mann’s review had the merit of pointing out
limitations in the classical approach and the resulting view
of the DSCT, it ultimately failed to provide a clear alternative
functional framework. In fact, the traditional thinking about
sensory projections was also evident in the interpretations
given to results from a pioneering behavioral study by Arshavsky et al. (12) in which they recorded DSCT activity
during treadmill locomotion in decerebrate cats. For example, they observed that at least 50% of the DSCT neurons
were active in more than one phase of the step cycle, but
they attributed this behavior to receptor input from biarticular muscles and did not consider the possibility of convergence mechanisms from multiple hindlimb muscles. Consequently, they concluded that “our findings confirm the view,
based on the study of the afferent connexions of DSCT
neurones, that the DSCT transmits information about the
activity (i.e., the phase and the strength of the contraction)
of separate muscles or of a few synergists.”
Thus the use of natural stimulation did not lead to a
fundamentally different model of spinocerebellar organization because it was still subject to the same methodological limitations and to the same basic assumptions.
The focus on isolated stimuli failed to acknowledge an
integrative role for the spinal circuitry, so the implied
function of the DSCT was to relay a high-fidelity copy of
highly localized sensory information to the cerebellum.
Any integrative function required for appropriate sensorimotor integration was the exclusive purview of the cerebellum and other central structures.
April 2001
547
SPINAL PROPRIOCEPTION
B. Further Evidence for Polysynaptic Pathways
By the early 1980s, the role of interneuronal networks in the function of DSCT neurons had become more
apparent. The available evidence for some of the circuitry
underlying the polysynaptic organization of spinocerebellar systems was summarized in a review by Bloedel and
Courville (28). Their concept of the spinocerebellar circuitry now included both excitatory and inhibitory interneurons interposed between the sensory receptors and
the DSCT projection cells as summarized in Figure 4.
The issue of polysynaptic convergence also became the
“leit motif” of a long series of experiments by Osborn and
Poppele (248 –254) that spanned almost a decade. In these
studies, DSCT responses to several types of sensory stimulation, from electrical nerve stimulation to passive ankle
joint movements, were analyzed with a variety of quantitative techniques to gain insight about the functional significance of the pattern of sensory convergence in the DSCT.
The results of these studies led to several new observations about the organization of the DSCT circuitry.
First, they found that muscle contraction is the most
potent single stimulus and inhibition the most common
response to that stimulus (248). Then, by extrapolating
from large population samples, they showed that ⬎60% of
the DSCT population representing sensory input from the
entire hindlimb responds to stimulation of primary affer-
ents from a single muscle group (the gastrocnemius-soleus), and mostly via polysynaptic pathways. Thus not only
did the DSCT neurons appear to be strongly modulated by
muscle force, but also “the information carried by the
DSCT is not discretely organized muscle by muscle.” They
went on to observe that “the role of integrating afferent
information from the various muscles and joints of the
limb no longer seems to be the exclusive province of the
cerebellum, but also appears to be shared by spinal centers such as the DSCT” (250).
C. Localized Stimuli Affect Most of the
DSCT Population
Although each separate stimulus seems to affect a
substantial fraction of the DSCT population, more complex stimuli like joint rotations can affect nearly the entire
population (88%, Ref. 46). Thus it became evident that
even relatively localized stimuli such as single joint movements have a widespread effect when they activate a
variety of receptor types simultaneously (e.g., cutaneous,
muscular, and joint receptors). Furthermore, the response patterns evoked by these more comprehensive
stimuli were distributed differently across the population
than were those evoked by more discrete stimuli. This
was evident in the comparison of responses to stretch of
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
FIG. 4. An organizational scheme for
the dorsal spinocerebellar tract circuitry
proposed by Bloedel and Courville (28)
to synthesize the evidence available in
1981. In contrast to the earlier proposal
illustrated in Figure 2, this proposal details a number of oligosynaptic pathways
to account for nonmonosynaptic effects
and for the results from experiments utilizing natural stimulation such as muscle
stretch in place of electrical stimulation.
Except for the added complexity of the
circuit diagram, however, the proposal
recognized the same basic organization
as the earlier proposal with a proprioceptive (A) and cutaneous (B) division. For
clarity of presentation, here we omitted
from the diagram the monosynaptic convergence from group Ia and group II afferents that is shown in Figure 2 and the
presynaptic inhibition of the monosynaptic pathways. [Redrawn from Bloedel
and Courville (28).]
548
G. BOSCO AND R. E. POPPELE
D. Connectivity of DSCT Neurons Outside
Clarke’s Column
The increasing evidence that various neuronal subgroups residing outside Clarke’s column also contribute
axons to the DSCT (119, 210, 211) suggests further that
these subpopulations might also have distributed connectivity patterns. Two studies in the late 1980s provided
careful examinations of the inputs to these non-Clarke’s
column neurons. One study characterized the sensory
input to DSCT neurons located caudal to Clarke’s column
in laminae V and VI of the lumbar spinal cord (11). These
authors found an extensive mono- and disynaptic pattern
of convergence from electrically stimulated muscle as
well as cutaneous and joint nerves compatible with a
major integrative role for these neurons. A similar experimental approach was used by Edgley and Jankowska
(73) to investigate the connectivity of more rostral dorsal
horn spinocerebellar neurons. These neurons were found
to receive extensive monosynaptic convergence from
group II muscle afferents, as well as from joint and cutaneous afferents. Unlike the neurons studied by Aoyama et
al. (11), however, they did not seem to receive input from
group I muscle afferents.
VIII. PARALLEL DISTRIBUTED NETWORK
A. DSCT Circuitry Resembles a Parallel
Distributed Network
Rather than suggesting a specific functional organization, the large-scale sensory convergence onto DSCT
neurons and associated polysynaptic circuitry resembled
instead a parallel distributed neural network having a
widespread interconnectivity. The analogy to a neural
network was reinforced by the results from a set of
experiments using a variety of stimuli including muscle
stretch or contraction and joint ankle flexion/extension
(253). Cells that responded similarly to one of these stimuli generally showed different response profiles for other
stimuli. In fact, it was not possible to predict a cell’s
response to one type of stimulus from its response to
another. This argued strongly against any clear-cut functional distinctions among DSCT neurons and led to a
proposed scheme in which divergent projections from
sensory receptors (input layer) project onto various interneuronal pathways comprising a “hidden layer” in a neuronal network. The weighted pattern of convergence of
sensory information from the hidden layer to the individual units of the output layer (the DSCT neurons) determined their firing properties (253; Fig. 5).
B. Similarity With Other Spinal Sensory Networks
More generally, the parallel distributed organization
proposed for the DSCT circuitry resembles analogous
distributed networks that have been proposed for the
control of various types of spinal reflexes. The scratch
reflex in the turtle is one example in which distributed
activity of broadly tuned propriospinal interneurons coordinate two forms of scratch reflex behavior (rostral or
pocket scratch) (25, 26). The network controlling this
reflex behavior contains shared components with the circuitry devoted to the control of swimming behavior, and
it might also be distributed bilaterally (83, 84, 307, 308).
Another example is the wiping reflex in the frog (108) in
which an irritating stimulus on the frog skin can evoke a
precise foot wipe directed at the stimulus using different
limb configurations that depend on stimulus location.
Stimulating boundary areas between two adjacent wiping
zones can elicit the limb configuration of either zone with
similar probability, suggesting that a distributed organization might also govern this behavior (171, 172).
A similar distributed network scheme has also been
proposed for the control of cat locomotion (274). Although it is well established that some specific receptor
types may play quite specific roles in the timing and
excitation of locomotion phases (8, 9, 27, 54, 66, 92, 115,
122, 130, 131, 224, 264, 266, 275), the evidence for task-
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
the gastrocnemius-soleus with those evoked by passive
ankle flexion (254). The two stimuli were designed so they
stretched the muscles identically, but they elicited different response components in the population. This result
implied that converging input from nonmuscle receptors
or from other muscles acting at the ankle joint can modify
the activity of DSCT cells so that the same local stimulus,
in this case stretch of gastrocnemius-soleus, can evoke a
different behavior in another context. It was a clear demonstration that DSCT responses do not merely reflect the
activity in specific classes of sensory receptors.
Although the polysynaptic model of spinal proprioception that emerged from these studies seemed more
difficult to place in a functional framework than the simpler direct-relay models, it did seem to be more consistent
with the anatomy of the system. For example, Walmsley
and Nicol (324) argued against a topographical arrangement of muscle input within the DSCT based on a study of
muscle projections using intracellular recording and
WGA-HRP filling. They found that neurons activated
monosynaptically from ankle extensors were scattered
throughout Clarke’s column, and they generally received
converging input from more than one muscle. Along this
same line, Zytnicki et al. (339) found a large heterogeneity
in the responses evoked in DSCT neurons by contraction
of the anterior tibialis muscle. A convergence of cutaneous sensory inputs was also implied by the results of Kim
et al. (177), who examined responses to stimuli applied to
the skin of the cat footpad.
Volume 81
April 2001
SPINAL PROPRIOCEPTION
549
dependent reflex reversals (7, 67, 68, 90, 118, 258, 263, 309;
see also Refs. 63, 261, 285, 336 for reviews) and positive
force feedback (10, 111, 115, 129, 263, 275, 276) motivated
a proposal for a much less specific organization. This
organizational scheme developed from the notion of a
parliamentary principle originally introduced by Bässler
(20, 21), which was an alternative to the idea that distinct
interneuronal groups might control distinct phases of the
locomotion cycle. It was based on experimental evidence
from the stick insect showing that interneurons are all active
throughout the phases of locomotion. From this observation, Bässler conceptualized a population-coding scheme for
the control of locomotion whereby each interneuron contributed with its level of activity to all the locomotion phases
in a manner analogous to the way a member of parliament
contributes to a decision with a vote.
The control scheme for mammalian locomotion proposed by Prochaska (274) is similar to this in principle,
since it postulates that weighted contributions from var-
ious spinal interneuronal network modules might govern
locomotion and reflex behavior in general (see Fig. 6).
Prochaska (274) attempts to account for reflex variability
using the formalism of fuzzy logic, wherein behaviors are
selected according to a set of imprecisely defined or
“fuzzy” rules relating specific sensory variables to specific
motor actions. One basis for such circuit interactions
might be the interneurons that are shared among different
spinal circuits. In fact, it is likely that a number of interneurons contribute to the control of more than one motor
behavior, for example, posture and locomotion (41, 42,
121, 140, 142, 312, 313, or for a detailed review, see Ref.
147). Interneurons may also be shared between reflex and
ascending systems. At least one class of inhibitory interneurons that makes synaptic contact with both DSCT
neurons and motoneurons (134) may provide for a coupling of afferent information processing between the two
pathways. On the other hand, collaterals of primary sensory afferents directed to either DSCT or spinal motoneu-
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
FIG. 5. An organizational scheme for the DSCT circuitry proposed by Osborn and Poppele (253) as a parallel
distributed network. Rather than the discrete circuits proposed in earlier schemes, this proposal used a neural
network to represent the diversity of response behavior observed in large populations of DSCT cells. The distinction
between cutaneous and proprioceptive subdivisions of DSCT is abandoned in this proposal in favor of a networklike connectivity that included sensory input from muscle, joint, and cutaneous afferents. All of these sensory inputs
converge on excitatory and inhibitory circuits that provide variously weighted connections to the DSCT neurons.
The experimental results indicated that the excitatory and inhibitory pathways each gave rise to either short latency
(early) and longer latency (late) responses. The synaptic input to the DSCT neurons was proposed to come from
these circuits as well as from direct monosynaptic projections from receptors. Various weightings of the synaptic
connections are illustrated in the diagram by different-sized arrows. The analogy to a neural network was that the
sensory receptors acted as an “input layer” having a highly convergent and divergent connectivity with a “hidden
layer” circuitry, which in turn has a convergent and divergent connectivity with an “output layer” of DSCT cells.
[Modified from Osborn and Poppele (253).]
550
G. BOSCO AND R. E. POPPELE
Volume 81
rons may also be under differential supraspinal (cortical)
control via presynaptic inhibition (135, 136, 149, 150, 197).
Differential presynaptic control of different sensory afferent branches might, therefore, represent a mechanism by
which the activity of the two networks could also be
uncoupled. Thus even these relatively simple mechanisms
can lead to a number of possible outcomes.
IX. INFORMATION ENCODED BY DORSAL
SPINOCEREBELLAR TRACT ACTIVITY
A. Nonlinear Interactions Require a Normal
Behavioral Repertoire for Study
Even though the parallel distributed network organization of the DSCT bears an obvious resemblance to
spinal organization schemes that have been recently
proposed to account for reflex behavior, this analogy
did not help to provide any clear functional insights.
Neither did the experimental paradigms used to study
the DSCT, since they addressed primarily questions of
connectivity. Nevertheless, it was generally supposed
that a functional model would eventually result from
these studies. In particular, it was assumed that if a
sufficient number of selective (i.e., well controlled) and
discrete stimuli were tested, then a synthesis of resulting observations would ultimately define the total func-
tional organization. However, the experimental results
proved this assumption to be invalid because it failed to
account for the nonlinear interactions among stimuli. It
is unlikely therefore that the functional significance of
the circuitry can be fully appreciated unless the stimuli
engage a sufficiently complete set of peripheral mechanisms in the same spatial and temporal order as occurs in normal behavior.
B. New Functional Framework Based on Whole
Limb Rather Than on Localized Parameters
The need to adequately engage sensory input was particularly well illustrated for the DSCT circuitry by an experiment in which DSCT responses to bidirectional, single-joint
rotations were compared with those for bidirectional perturbations of the whole limb (33). The result was that neurons responding to ankle-only flexion with the same time
course of activity showed a variety of response waveforms
to ankle-only extension, whereas bidirectional limb movements consistently produced opposite response waveforms
for oppositely directed limb movements (see Fig. 7). Thus
only when the sensory input from the whole limb was activated in a normal behavioral pattern did the responses of
this system show a clear and consistent relationship to the
stimulation. Even though the single joint stimuli produced
robust and often large-amplitude responses (177, 253, 254),
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
FIG. 6. A scheme proposed by Prochazka (274) to
account for an interaction between sensory input and
motor output during locomotion based on fuzzy logic
principles that relate specific sensory variables to specific motor actions according to a set of imprecisely
defined or “fuzzy” rules. This scheme proposes a modular processing of sensory inputs to produce a set of
muscle activity patterns or motor membership functions. Different weightings of these motor membership
functions (indicated by percent values next to the corresponding function) may then produce different behaviors (slow walk or fast run, for example) with the
identical combination of sensory afferent inputs. The
scheme was motivated by the need to account for
task-dependent changes in reflexes, and it resembles
an organizational scheme originally proposed by
Bässler (20) to explain insect locomotion. In that case,
interneurons were found to be active in all phases of
locomotion, and the behavior was related to a dynamically weighted sum of their activities. These proposals
are both formally similar to the network-like organization depicted in Figure 5. In all cases, there is a largescale connectivity leading to behaviors that depend on
connectivity weightings that may vary dynamically.
[From Prochazka (274).]
April 2001
SPINAL PROPRIOCEPTION
551
there was no consistent pattern across neurons that related
them to the stimulus parameters.
This experiment provided the first clear, direct evidence that the output of the spinal sensory circuitry might
be related to whole limb parameters rather than to local
parameters like muscle lengths or individual joint movements. Moreover, the same study showed that the activity of
DSCT neurons is broadly tuned for limb movement direction
and for the position of the hindfoot. These observations set
the stage for a completely different functional concept for
spinal proprioception by raising the question of the extent to
which global versus local limb parameters are represented
in the activity of DSCT neurons.
C. DSCT Neurons Encode a Linear Representation
of Foot Position and Movement
This question was examined systematically by recording DSCT unit activity during passive placement of
the cat hindfoot over most of its parasagittal workspace. The response data were related to limb kinematics to determine whether they best represented individual joint angles or more global limb parameters (see
Figs. 8A and 9A for definitions). Kinematic parameters
that included multiple joint angles always explained a
greater fraction of the variance in unit activity than did
any single joint representation (38). In fact, linear relationships between unit activity level and the polar coordinates of limb axis length and orientation consistently explained the greatest percentage of variance in
unit activity. Examples of this relationship are illustrated in Figure 8, C–F. These three-dimensional plots
show a planar relationship between unit firing activity
and the limb end-point position given by the coordinates of the limb axis.
Another feature of the DSCT responses to passive
limb placement is that the activity of many neurons may
also relate to the direction of limb movement as it goes
from one position to another. In other words, movement
and positional parameters can be represented simultaneously by the activity of individual DSCT neurons, but
they are not represented independently. The amplitude of
the movement response was found to be linearly dependent on the level of position-related activity (34). This
relationship between inputs is reminiscent of the interaction between retinal and eye (or head) position signals
first described by Andersen et al. (5) for neurons of the
parietal cortex. The amplitude of the retinal receptive
field response of those neurons was found to be modulated by the eye or head position. This type of modulation,
resulting from a multiplicative type of interaction between the two signals, is commonly referred to as “gain
field” modulation. Gain fields have now been demon-
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
FIG. 7. Dorsal spinocerebellar responses to single-joint versus whole limb stimulation. A: poststimulus time histograms
from 15 DSCT neurons in response to flexion and extension perturbations of the ankle joint with the other joints fixed. Each
trace shows an individual neuronal response, normalized to the maximum and referred to the mean firing rate (horizontal
line). The ensemble of neurons was selected based on the similarity of response time courses for ankle flexion. The same 15
neurons gave a variety of responses to ankle extension, indicating therefore no consistent relationship overall to the stimulus.
B: responses of 15 neurons to opposite directions of whole limb movement. In this case, perturbations were applied at the hind
paw, and all the joints were unrestrained. As in A, cells were selected based on the time course of their responses to flexion.
In this case, however, all 15 neurons gave similar responses to extension that were essentially phase-reversed, indicating a
consistent relationship across cells when the whole limb was engaged in the movement. [Modified from Bosco and Poppele
(33).]
552
G. BOSCO AND R. E. POPPELE
Volume 81
strated for several CNS structures involved in oculomotor
control (39, 323) and spatial orientation (207, 237, 238,
316). Although the functional interpretation of gain fields
is still debated, they appear to represent a neural mechanism by which information about multiple parameters
may be compressed and combined in a single unit’s activity, and they have also been interpreted as evidence for
coordinate transformations (4, 6, 288, 302, 310, 338, for
reviews; see Ref. 40).
X. REFERENCE FRAMES TO FORMALIZE
RELATIONSHIPS
A. Muscle- and Joint-Based Reference Frames
One way to formalize the relationship between unit
activity and limb kinematics is through the definition of a
reference frame, which is a spatial domain in which parameters of interest can vary (305). For example, a pri-
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
FIG. 8. Broad tuning of DSCT neurons with respect to limb position. The
length and orientation of the hindlimb
axis shown in A define the foot position
relative to the hip. The stick figures in B
illustrate hindlimb postures corresponding to 20 different foot locations in a
parasagittal workspace. The data points
plotted in C–E represent DSCT unit activity recorded in each position plotted
against limb axis length and orientation
for the 20 foot positions. In each case,
the data points fall within a plane, indicating a linear relationship between cell
activity and foot position. The three examples show neurons modulated primarily by changes in limb axis orientation
(C), length (D), or both (E). The circular
histogram in D represents the directions
of foot movement producing the maximal responses (preferred tuning directions) for 79 broadly tuned DSCT neurons. The distribution is significantly
quadramodal, with preferred directions
clustering along axes (dashed lines) oriented at 13 and 103° with respect to the
limb axis orientation. [Redrawn from
Bosco and Poppele (34).]
April 2001
553
SPINAL PROPRIOCEPTION
mary reference frame for muscle receptors is the spatial
domain of the muscle, and the coordinates for effective
stimuli in that frame of reference might be the one-dimensional coordinate of muscle length. From the relationship
between muscles and joints, we may also define another
reference frame for muscle receptors that is joint based.
The coordinates in this reference frame would be the
relevant joint angles.
B. Limb- Versus Joint-Based Reference Frames
and Biomechanical Factors
The reference frame for a DSCT neuron, which combines signals from receptors in different muscles and
joints, each having a separate reference frame, depends
entirely on exactly how the signals are combined. For
example, a combination of joint angles might be weighted
such that the resultant is equivalent to the length or
orientation of the limb axis, thereby transforming the
joint-based reference frame to a limb-based reference
frame. Or the combination might simply represent a sum
or difference of joint angles, in which case the reference
frame would still be joint based. Thus the fact that DSCT
neurons might receive convergent inputs from more than
one joint does not in itself imply anything about a reference frame for the resulting information.
The question then is whether DSCT activity is based
on the reference frames of the sensory receptors or on
some derived reference frame that results from central
processing. The difficulty in distinguishing between these
possibilities is that mechanical constraints in the limb
result in a correlation between joint angles and more
global limb parameters. Therefore, even a clear relationship between unit activity and limb axis parameters is not
necessarily indicative of a centrally organized coordinate
transformation.
XI. INTERACTION BETWEEN BIOMECHANICAL
AND NEURAL FACTORS
A. Joint-Angle Covariance Reduces Limb
Degrees of Freedom
To explore this issue further, it is important then to
examine how the limb segments and joints interact. The
cat hindlimb may be considered as a 3-degree-of-freedom
linkage of segments allowing for two-dimensional movement of the end point in a parasagittal plane. This relationship illustrates an important point about limb motor
control, namely, the need to deal with extra degrees of
freedom. In the cat hindlimbs for example, movements
about three joints must be controlled to achieve limb
end-point movements that are confined to a two-dimen-
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
FIG. 9. Covariation of cat hindlimb joint angles for
passive and active postures. For a given hindlimb posture, the angles at the hip (h), knee (k), and ankle (a)
illustrated in A are plotted versus one another in a
three-dimensional joint-angle space. Joint-angle measurements made while the hind foot of an anesthetized
cat was placed in the 20 positions indicated in Figure
8B all fall within a single plane indicated by the jointangle covariance plane drawn in B for data pooled
from six animals (38). When alert cats were required to
maintain stance on a tilted platform, the joint angles
also showed a planar covariance as illustrated in C.
[Replotted from Lacquaniti and Maioli (187).] Although
the joint angles were confined to a linear covariance
plane both during active stance and passive limb placement, the planes had different orientations. The covariance plane determined from joint angles measured
during active stance was rotated ⬃90° in the ankle-hip
plane with respect to the covariance plane determined
for the anesthetized cat. This observation is consistent
with a control strategy that may take advantage of
biomechanical joint-angle constraints by modulating
joint-angle relationships, in this case by reversing the
relationship between hip and ankle joints.
554
G. BOSCO AND R. E. POPPELE
B. Implications of Joint-Angle Covariance
for Motor Behavior
Recently intersegmental covariations have also been
observed and studied during locomotion (27, 120, 298; see
also Ref. 184 for a review). These studies extended the
observations made during standing behavior to the kinematics of locomotion and also pointed out that limb axis
length and orientation represent kinematic invariance
across various types of gait. Lacquaniti et al. (184) proposed from these observations that “the CPG [central
pattern generator] may control limb segment motion by
encoding the waveform of the elevation angles. In response to these kinematic reference signals, the appropriate muscle synergies would be determined in a subordinate and flexible manner to adapt to the current
mechanical constraints.”
The biomechanical properties of the limb may also
provide a substrate for interjoint coordination at the reflex level (114, 181, 335; see also Ref. 243 for a review).
Muscle length-dependent excitatory connections exist between muscles with synergistic biomechanical actions
across joints (242, 245), whereas force-dependent, inhibitory pathways link muscles that exert torque in different
directions (31, 32, 145, 300). Therefore, the action of these
connections could result in a stronger across-joint coordination that complements the biomechanical coupling.
These results suggest that biomechanical interjoint
coupling may be utilized and perhaps fine-tuned by the
CNS in controlling the limb. Such interplay between controller and the controlled plant could play an important
role in the process by which the nervous system copes
with the potentially complex multi degree-of-freedom
control problem. In essence, purely biomechanical factors establish a reduction in the limb degrees of freedom
that is reinforced and modified by spinal reflex pathways
that interconnect muscles acting at different joints. Consequently, the role and the computational load of the
higher level control may be effectively simplified.
C. Implications of Joint-Angle Covariance
for Sensory Representations
The relevant functional implication for sensory information processing is that receptor activity throughout the
limb is also likely to be correlated because of the strong
biomechanical coupling among segments. For example,
the activity of receptors located in muscles acting at the
hip and the ankle might reflect the covariation of the
angular motion of the two joint angles by showing a high
level of temporal correlation; that is, the activity of a hip
extensor muscle receptor can also transmit information
about ankle joint motion. Patterns of correlated activity
across receptors along with a large degree of sensory
convergence could potentially bias the activity within the
spinal circuitry. The result would be that the activity of
spinal neurons would appear to relate to global hindlimb
parameters even though it might simply reflect the jointangle covariation without additional sensory transformations by the spinal circuitry.
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
sional workspace. However, there is a tight coupling
among the three joint angles in the cat hindlimbs during
both passive and active limb movements that effectively
reduces the limb degrees of freedom from three to two
(38, 188), implying in this case there may be no extra
degrees of freedom to control. This reduction is illustrated by the finding that the relationship among the three
joint angles of the hindlimb shows a planar or two-dimensional covariation over a large range of limb positions.
Thus the data representing limb positions in a threedimensional joint-angle space fall on a plane rather than
being scattered throughout the space as expected if joint
angles varied independently (see Fig. 9).
The basis for the joint-angle coupling that leads to
this behavior in the passive limb is presumably biomechanical, as indicated by post mortem assessment (38).
For example, the biomechanical properties of biarticular
muscles that span two joints from origin to insertion as
well as more passive structures such as ligaments undoubtedly have a role in coupling forces across joints.
During movement there may also be inertial coupling
between limb segments as well.
Behavioral studies of posture in cats have suggested
that neural control of the limb may actually take advantage of these biomechanical constraints (186 –188). Cats
trained to maintain stance on a tilting support platform
also showed a linear covariation of joint angles during this
task (187; Fig. 9C). However, the joint-angle covariance
pattern in this case is somewhat different from that described in the passive limb (Fig. 9B). First, the coupling
among joint angles is tighter, suggesting that neural control may actually reduce further any independent motion
in the individual limb segments. Second, the orientation of
the covariance plane is different due mostly to a sign
inversion of the relationship between the hip and the
ankle angles (compare Fig. 9, B and C). These observations suggest that the limb biomechanics may provide a
basis for the planar joint-angle covariance that effectively
reduces the limb’s degrees of freedom. A reduction in
degrees of freedom may then simplify control strategies
for maintaining stance, since the problem of mapping sets
of joint angles into foot positions (188) can be solved by
appropriately adjusting the relationships among limb segments. In this case, it would involve somehow reversing
the passive relationship between hip and ankle angles.
Sensory input that is also constrained by the biomechanical coupling is more likely to support this type of control
strategy based on global limb parameters rather than one
that controls joints independently.
Volume 81
April 2001
SPINAL PROPRIOCEPTION
XII. REFERENCE FRAMES FOR DORSAL
SPINOCEREBELLAR TRACT ACTIVITY
A. Can We Distinguish Between End Pointand Joint-Based Reference Frames?
straints. The other cells continued to signal a linear representation of the limb length and orientation, or limb
end-point position, but it was a different representation in
the two cases. The activity of these cells was clearly
influenced by the joint-angle covariance.
B. Evidence for a Kinematic-Based
Reference Frame
These studies in the anesthetized cat ignored, however, an obvious source of sensory input to the DSCT,
namely, the inputs activated by muscle forces and joint
torques. Thus the finding that DSCT activity encodes endpoint position cannot be taken to rule out more elaborate
representations based on joint torques or limb compliance. The reason is that all these variables are highly
correlated in the passive limb. Moreover, DSCT cells in
particular have been found to be very sensitive to sensory
inputs activated by muscle contraction (200, 248, 252,
339), so it seems reasonable that muscle force or joint
torque could as well be represented explicitly by DSCT
activity. If so, it brings into question any conclusion about
DSCT encoding of hindlimb parameters in an end pointbased reference frame, since the position of the limb end
point is potentially independent from joint torques.
This issue was investigated by comparing responses
to passive limb positions with and without muscle stimulation (36). In this experiment, various muscle groups
were activated by electrical stimulation of dissected ventral root filaments. For a given foot position, this generally
resulted in a stiffening of the limb with only modest
changes in joint-angle kinematics. The underlying assumption of the experiment was that if the reference
frame for DSCT responses were based to any extent on
kinetics or muscle forces, then the muscle tension perturbations produced by this stimulus would be expected to
alter the presumed kinematic representation. In fact, we
might expect a substantial disruption of the DSCT representation since the stimulus was somewhat artificial and
unrelated to the structured pattern of muscle activation
occurring during normal behavior. Instead, the general
finding was that the cells’ preferred tuning directions
based on limb position were mostly unchanged by the
muscle activation.
The result was complicated though by the fact that
most of the cells did respond to the muscle stimulation,
and while most of them (59%) nevertheless showed no
significant change in their position-related activity, many
cells (41%) did show changes. Closer examination of
these changes showed that they resulted from uniform
increases or decreases in response levels across positions. The effect of the muscle stimulation therefore was
to alter the cells’ sensitivity to limb position, but this
sensitivity or gain change was uniform over the entire
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
The question for the DSCT then is the extent to which
the activity of these neurons is referenced to the jointangle covariance or to more abstract limb parameters
such as the limb end-point position. In the passive limb
there is a single relationship between limb end point and
a linear combination of joint angles and thus a unique
limb geometry for each end-point position (38). Therefore, the finding under passive conditions that DSCT cells
encode limb length and orientation, or effectively limb
end-point position is not surprising. In fact, because of the
biomechanical coupling, we could expect this result even
if the cells received only a minimal sensory convergence
across joints.
However, a number of observations suggested that
DSCT activity does reflect a contribution from the spinal
circuitry in elaborating a representation of limb axis
length and orientation. One is that among the cells that
responded independently to all three hindlimb joint angles, those that related similarly to one joint angle could
show different relationships with the other two (38). In
other words, the pattern of joint-angle convergence expressed by these cells was not fixed to the biomechanical
joint covariance. The relationship between DSCT activity
and joint angles could be weighted differently across cells
as in a network or fuzzy logic type of organization. The
other observation was that the cells are broadly tuned
with respect to limb axis parameters (33), and the preferred tuning directions are clustered along and perpendicular to an axis roughly coincident with the limb axis
(34; see Fig. 8E). This observation provided an indirect
indication that the weightings of inputs from various limb
segments might be biased toward an explicit representation of the whole limb.
The crucial test of this issue was to alter the joint
angles and end-point position separately (35, 37). By constraining the movement at specific joints, sets of limb
end-point positions could be imposed that were each
associated with a different joint-angle covariance pattern.
The responses of DSCT neurons for each position in
which the limb was free to move was compared with
those obtained with joint constraints. The result was that
about one-half the sample cell population studied exhibited the same response to the two different joint-angle
covariance patterns across limb end-point positions.
These cells were clearly able to signal the position of the
limb end point for at least two different sets of limb
geometry, that is, they somehow derived the end-point
position independently from the biomechanical con-
555
556
G. BOSCO AND R. E. POPPELE
XIII. HOW DOES DISTAL SPINOCEREBELLAR
TRACT RELATE TO HIGHER ORDER
SENSORY STRUCTURES: EVIDENCE FROM
SENSORY CORTEX AND CEREBELLUM
What seems to emerge from this analysis is a framework for proprioception that involves a highly flexible
network organization based in some way on whole limb
kinematics. The functional organization underlying this
framework originates with the biomechanical linkages in
the limb. Afferent information from limb receptors is then
processed further through a distributed neural network in
the spinal cord (see Fig. 11). Although we examined only
the evidence from the dorsal spinocerebellar system to
support this view, we also implied that spinal circuitry in
general may be organized in a similar manner, at least
with respect to the distributed nature of sensory processing. Here we will examine the extent to which other CNS
structures may exhibit an organization similar to that
described for the DSCT. An important consideration implied by such comparisons is the extent to which a common type of organization might be necessary for communication between spinal cord and central structures.
A. Broad Directional Tuning
One prominent feature of the proposed spinal organization is broad directional tuning. Although studies of
central sensory processing have mostly focused on receptive field organization and mapping the body surface (50,
FIG. 10. Effect of knee joint constraint or muscle stimulation on the
spatial tuning of 42 DSCT neurons. Preferred tuning directions were
determined for the broadly tuned DSCT cells by finding the direction of
the maximal activity gradient or direction in the workspace that produced the maximal cell response. The preferred directions were computed separately for the passive (control) condition and the knee constraint or muscle stimulation (test) conditions. The difference angles
between the control and test preferred directions are plotted here to
indicate the change in directional tuning produced in each cell by joint
constraint (ordinate) and muscle stimulation (abscissa). The line at 30°
(i.e., ⫾15° difference) indicates a cut-off for a significant difference.
Significant changes in preferred direction occurred for about one-half
the cells with the knee constraint and for relatively few cells with
muscle stimulation. [Data from Bosco and Poppele (36).]
80, 88, 94, 160, 164, 235, 240, 270), there is evidence that a
distributed processing of sensory information by broadly
tuned cortical neurons may serve as a potential neural
mechanism for tactile stimulus localization (246, 271,
337). This issue remains controversial though because the
relevance of topographical maps for sensory processing
of both kinesthetic sense and tactile discrimination in the
somatosensory cortex is far from settled (for different
perspectives on this issue, see Refs. 143, 163, 283, 328).
Broad directional tuning with respect to limb kinematic parameters was first characterized in the primate
motor cortex (16, 95, 97, 103, 233, 292), and the same type
of unit behavior has now been observed in many CNS
structures involved in sensorimotor control. In particular,
neurons in the sensory (53, 167, 281) and parietal cortex
(16, 56, 81, 167, 169, 170), the cerebellum (69, 91, 96), and
basal ganglia (3, 57, 58, 61, 102, 144, 322) have all been
observed to be broadly tuned to the direction of arm
movements. Although there is not yet a general agreement
about the functional implications of the broad tuning, it is
generally accepted it can provide for a parallel processing
of global limb variables (101, 104, 293).
B. Representation of Global Limb Parameters
A relationship to global limb parameters is another
feature of the spinocerebellar organization of propriocep-
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
workspace. Thus it did not significantly alter the cells’
preferred tuning directions, or region in the limb workspace where a cell was most active (also referred to as the
preferred maximal activity gradient direction in Ref. 36).
Preferred tuning directions determined from responses
recorded for passive limb positions were found to differ
from those determined for responses recorded during
muscle stimulation by less than ⫾15° for about threequarters of the cells studied. In fact, as illustrated by the
difference angles in Figure 10, many cells that exhibited
large differences in tuning direction when the knee joint
was constrained showed essentially no difference during
muscle stimulation.
Therefore, when limb forces were altered while
maintaining limb geometry and joint-angle covariance
nearly constant, the spatial tuning behavior of the DSCT
cells was largely invariant. This result was interpreted as
consistent with a kinematic reference frame for DSCT
representations. Thus, in noting that the spatial variables
appear to define reference frames for DSCT activity,
Bosco and Poppele (36) concluded that “ . . . whatever
force information is encoded by the DSCT is encoded in a
kinematic reference frame.”
Volume 81
April 2001
SPINAL PROPRIOCEPTION
557
tion. This topic has received considerable attention in studies of motor areas, but it is less often the focus of sensory
area studies. Although Gardner and Costanzo (55, 98, 99)
pointed out nearly two decades ago that the activity of
sensory cortical neurons might also relate to the global
kinematic parameters of limb movement and posture, the
issue was not revisited until relatively recently. Two studies
employing the center-out reaching task incorporated by
Georgopoulos et al. (100, 103) showed that most of the
neurons in the primary sensory cortex (SI) receiving tactile
or deep (mainly muscle) receptor information were broadly
tuned for movement direction and arm position (53, 281).
For reasons we discussed above, the latter investigators also
pointed out that the directional tuning could be explained to
a certain extent by peripheral receptor properties. Nevertheless, some of their findings were strongly suggestive of a
more distributed processing of sensory information based
on more global limb parameters.
A subsequent SI study extended the two-dimensional
reaching paradigm to three dimensions by training monkeys to grasp a manipulandum within a large workspace
(318). This study showed specifically that the activity of SI
neurons related best to linear combinations of joint or
segment angles rather than to single joint angles. About
half of the neurons were modulated by end-point displacements along a single axis in space, and many other
neurons were modulated for hand displacements in a
plane, that is, along two perpendicular axes. Only a few
cells were found to be modulated along all three axes.
C. A Common Coordinate System for Encoding
Spatial Information
The majority of the neurons in these cortical studies
were modulated for hand displacements in the anteropos-
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
FIG. 11. A functional organization scheme for the dorsal spinocerebellar tract circuitry that includes the biomechanical linkages in the hindlimb as an essential factor in generating the sensory input. Signals from various mechanoreceptors in the limb reflect the joint-angle covariance, limb forces and torques, as well as various other parameters like
skin contact forces and skin stretch. The sensory signals are processed through a distributed neural network in the spinal
cord illustrated here by large arrows. Different shadings are intended to indicate that different patterns of connectivity
and/or different kinds of weighting or modulation might lead to different kinds of limb representations within the spinal
circuitry. One of these is a whole limb representation of limb axis length and orientation. Another is a joint-level
representation of limb mechanics. These circuits may then provide the signals that are summated and transmitted by the
DSCT neurons. Although two distinct “pathways” are indicated here for simplicity (black and white arrows), it is more
likely that the properties of the DSCT cells are distributed along a continuum as suggested by the range of behaviors
exhibited by DSCT cells (note, for example, the results illustrated in Figs. 8 and 10).
558
G. BOSCO AND R. E. POPPELE
D. Kinematic and Kinetic Representations
It seems therefore that there is increasing experimental support for a global representation of limb parameters
by central neurons in various sensorimotor structures. In
addition, however, the activity of these neurons can and
often does relate to both limb kinematic (position, movement) and kinetic (muscle force, joint torque) parameters
(e.g., Ref. 281). However, a feature of the proposed spinocerebellar organization of proprioception is its emphasis
on limb kinematics. The extent to which other sensory
structures may share this emphasis has not yet been
directly investigated.
One recent study of SI neurons addressed the issue
indirectly by examining neuronal activity with external
loads applied to the manipulandum. The loads modified
the pattern of muscle activation required for the execu-
FIG. 12. Distribution of preferred tuning directions for DSCT, cerebellum, and primary somatosensory cortex (SI) neurons. Note that all
three distributions are similarly skewed along an axis corresponding to
movements toward or away from the animal’s body. A: distribution for
87 DSCT neurons determined when the cat hindlimb was moved passively through a conventional center-out protocol. [Redrawn from Bosco
and Poppele (34).] B: distribution for 116 cerebellar Purkinje cells in
lobules IV, V, and VI. Monkeys were trained to perform a variation of the
classical center-out reaching task. [Redrawn from Fu et al. (96).] C:
distribution of 254 SI neurons’ preferred directions. Monkeys performed
a center-out reaching task. [Redrawn from Prud’homme and Kalaska
(281).]
tion of the reaching movements and led to changes in the
activity of a large fraction of SI neurons. However, the
loading effect was examined only in relation to activity
levels during the center holding time (that is, when the
monkey’s hand is grasping the manipulandum at the center of the workspace). Furthermore, only two examples
were given (see Fig. 9 in Ref. 281) to show the effect of the
load on the directional activity, and they suggest that the
activity was mainly associated with the gain of the response rather than the cells’ preferred directions. Although it is difficult to generalize from this, the result
appears to be similar to the DSCT result. In parietal area
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
terior direction, i.e., for arm flexion and extension, and
this bias did not appear to depend on the locations of the
neurons’ receptive fields or their modalities (tactile or
deep) (281). This type of anisotropy in the distribution of
preferred directions that is strongly biased along the axis
of flexion-extension of the arm is quite different from that
seen in motor cortex, where preferred directions have
been found to be uniformly distributed (103). However,
the same bias has also been observed in the cerebellum
(96), and it bears a strong resemblance to the distributional bias observed for DSCT neurons that tended to
align with the limb axis (34). This type of preferreddirection clustering along coordinate axes might be expected if information were encoded in a coordinate reference frame (17, 18, 116, 173, 174, 208, 267, 301, 332; see
Ref. 305 for a review), implying then that spinal, cerebellar, and cortical sensory neurons may share a similar
limb-based coordinate system for encoding spatial information (see Fig. 12).
This idea was extended by an analysis of the behavior
of neurons in area 5 of the parietal cortex. Lacquaniti et al.
(185) fitted regression models to recordings made while
monkeys performed three-dimensional reaching movements in three separate workspaces (81) to test different
assumptions about possible coordinate systems. Their
analysis showed that the most parsimonious description
of area 5 neuronal activity was a coordinate system for
the representation of hand position based on distance and
direction from the shoulder. In support of this idea, there
was a strong tendency for parietal neurons to be tuned
along one of the three coordinate axes (elevation, azimuth, and distance or the equivalent elbow angle). This is
also consistent with the psychophysical observation that
these same spatial parameters may be processed by the
nervous system independently (24, 29, 79, 86, 87, 112, 228,
284, 303, 304).
Volume 81
April 2001
SPINAL PROPRIOCEPTION
5 however, neuronal activity was found to be invariant
with respect to limb kinematics under the same experimental conditions (170), implying a kinematic-based reference frame in that structure.
XIV. SIGNIFICANCE OF KINEMATIC-BASED
REFERENCE FRAMES FOR DORSAL
SPINOCEREBELLAR TRACT ACTIVITY
A. A Basic Kinematic Reference Frame
for Proprioception
B. Alternative Proposal for Proprioceptive
Feedback in Cerebellar Regulation of Posture
The existence of two roughly equal neuronal subpopulations projecting to the cerebellar cortex suggested to us that they might underlie some kind of
comparison between end point-related and joint-related
signals. For example, one way the cerebellum might
influence reflex behavior during posture and locomotion could be to ensure that muscle activation patterns
and, therefore, joint torques, would be such that the
desired limb end-point positions are mapped onto a set
of joint angles within a given covariance plane; that is,
it might play a role, through its modulation of reflexes,
in modifying the passive biomechanical coupling across
joints to produce the active patterns observed in stance
and locomotion. The sensory feedback via dorsal spinocerebellar pathways could provide a measure of how
well the reflex-induced coupling achieves the desired
covariance pattern.
One way this might occur is by sensing a mismatch
between end point-related and joint angle-related signals carried independently by two neuronal subpopulations. Because each DSCT cell’s activity encodes an
end-point position, a comparison between cells that
encode end point invariantly and those that encode end
point differently for different joint configurations could
provide an error signal for deviations from a desired
joint covariance. Figure 12 illustrates a hypothetical
example of two such cells based on the joint constraint
model (35, 36). When the limb is free to move according
to its passive compliance, both cells respond in a similar way to different limb postures. Thus the difference
between their firing rates for any posture is some constant value. However, when the relative joint compliance is changed, in this case by constraining the knee
joint, the cells no longer respond similarly. Thus a
comparison of the behavior of two such cells during
limb movement could reveal changes in the joint-angle
covariance (Fig. 13).
To implement this comparison in the cerebellum
would require an appropriate DSCT projection pattern.
The projection could also provide for a summation of
oppositely phased responses to achieve the desired
difference. [DSCT neurons are typically divided into 2
groups of similar size with opposite response phase
(33, 34, 251–254; see also Figs. 8 and 12).] Thus if the
DSCT projection to the cerebellum were to bring together matching end point-related signals of opposite
phase from the two DSCT subpopulations within a
given joint-angle covariance pattern, then a linear summation of their activity by cerebellar cortical neurons
could provide the desired comparison. The resulting
cerebellar output activity could conceivably direct a
modulation of reflex activity to reestablish a desired
joint-angle covariance.
This example is not intended as an explanation of
cerebellar function, since the DSCT is only one of many
inputs to the cerebellum. It is presented here to illustrate
how the framework proposed for the organization of proprioception with the DSCT might contribute to a possible
regulation of joint-angle covariance. Nevertheless, it is
not unreasonable to consider a mechanism of this kind. In
fact, the type of DSCT convergence envisioned to take
place in the spinocerebellar cortex is analogous to the
linear summation of mossy fiber directional signals representing head and eye velocity that occurs in the cerebellar flocculus of the goldfish (259, 260).
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
The evidence in support of a kinematic-based reference frame at various levels of sensorimotor processing is
still sketchy and somewhat indirect. Nevertheless, we
believe from the results of the DSCT studies that this
basic idea can provide a plausible framework for evaluating the functional significance of at least the early stages
of proprioceptive information processing.
The evidence for a gain field-like interaction between limb position responses and the responses to
both movement direction (34) and muscle force (36)
suggests one way in which various proprioceptive parameters might interact within a basic kinematic reference frame; that is, they may modulate the sensitivity of
a kinematic response. Furthermore, the finding that
about half of the cells in the DSCT appear to represent
limb parameters in terms of the limb end point while a
similar fraction are instead influenced by specific limb
geometries is an intriguing one that may suggest different functional interpretations. For example, these behaviors could represent the outputs from two stages in
a transformation from a joint-based to end point-based
reference frame (e.g., Refs. 165, 166). Another possibility comes from a consideration of the possible role of
DSCT in cerebellar function.
559
560
G. BOSCO AND R. E. POPPELE
Volume 81
XV. SUMMARY
A. Role of Limb Biomechanics in Global
Limb Representations
We have presented here a possible framework for
interpreting proprioceptive signals at the spinal level. It is
based on the premise that global limb information rather
than localized receptor-like proprioceptive information is
encoded by the nervous system. Within a basic global
framework, information is encoded by a distributed system in which each neural element may still bias the global
information according to some local detail. For example,
the DSCT data suggest that details about stiffness at a
single joint might be contained in a population signal that
encodes a representation of the limb end point that may
then depend on the joint covariance resulting from specific levels of joint stiffness.
This global sensory representation is not organized
entirely by the neural circuitry however. It begins in the
periphery with the biomechanical structure of the limb.
Biomechanical constraints ensure that the activity from
individual sensory receptors will be correlated in certain
ways that depend on whole limb parameters. Therefore,
even a minimum of central sensory convergence could
lead to global representations with this peripheral apparatus (37, 38, 281).
B. Significance of Kinematic-Based Representations
We also suggest that this framework could be based
on limb kinematics. If so, it is noteworthy that while many
participating sensory receptors are associated with muscles and some even specifically tuned to muscle force,
nevertheless their ensemble is capable of encoding limb
kinematics. In other words, inputs from receptors located
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
FIG. 13. Hypothetical DSCT responses under different
joint-angle covariance conditions. Responses of cell A and
cell B are illustrated by the plane that best fits the relationship between unit activity and the limb axis length and
orientation (see Fig. 8). Passive: joint-angle covariance is
determined by passive limb mechanics for different postures in the parasagittal workspace. Stick figures illustrate
three of those postures. Constrained: a constraint placed
across the knee joint modifies the joint covariance leading
to different postures for the same foot positions as illustrated by the stick figures. The response of cell A is not
altered by the knee constraint, but the response of cell B
is. The difference between the activity of cell A and cell B
for each foot position is the same for the passive limb, but
it varies with changes in foot position for the constrained
limb. Therefore, a computation of the difference between
the responses of these cells can indicate any deviation
from the passive joint-angle covariance for limb postures
throughout the workspace.
April 2001
SPINAL PROPRIOCEPTION
We acknowledge and thank J. Soechting, A. Prochazka, and
the reviewers for helpful comments.
National Institute of Neurological Disorders and Stroke
Grant R01-NS-41143 supported the authors’ work cited in this
review.
Address for reprint requests and other correspondence:
R. E. Poppele, Dept. of Neuroscience, 321 Church St. SE, Minneapolis, MN 55455 (E-mail: [email protected]).
REFERENCES
1. ABRAHAMS VC, DOWNEY ED, AND HAMMOND CG. Organization of segmental input from neck muscles to the external cuneate nucleus of
the cat. Exp Brain Res 71: 557–562, 1988.
2. ABRAHAMS VC AND SWETT JE. The pattern of spinal and medullary
projections from a cutaneous nerve and a muscle nerve of the
forelimb of the cat: a study using the transganglionic transport of
HRP. J Comp Neurol 246: 70 – 84, 1986.
3. ALEXANDER GE AND CRUTCHER MD. Preparation for movement: neural representations of intended direction in three motor areas of the
monkey. J Neurophysiol 64: 133–150, 1990.
4. ANDERSEN RA, BRACEWELL RM, BARASH S, GNADT JW, AND FOGASSI L.
Eye position effects on visual, memory, and saccade-related activity in areas LIP and 7a of macaque. J Neurosci 10: 1176 –1196, 1990.
5. ANDERSEN RA, ESSICK GK, AND SIEGEL RM. Encoding of spatial
location by posterior parietal neurons. Science 230: 456 – 458, 1985.
6. ANDERSEN RA, SNYDER LH, LI CS, AND STRICANNE B. Coordinate
transformations in the representation of spatial information. Curr
Opin Neurobiol 3: 171–176, 1993.
7. ANDERSSON O, FORSSBERG H, GRILLNER S, AND WALLEN P. Peripheral
feedback mechanisms acting on the central pattern generators for
locomotion in fish and cat. Can J Physiol Pharmacol 59: 713–726,
1981.
8. ANDERSSON O AND GRILLNER S. Peripheral control of the cat’s step
cycle. I. Phase dependent effects of ramp-movements of the hip
during “fictive locomotion.” Acta Physiol Scand 113: 89 –101, 1981.
9. ANDERSSON O AND GRILLNER S. Peripheral control of the cat’s step
cycle. II. Entrainment of the central pattern generators for locomotion by sinusoidal hip movements during “fictive locomotion.”
Acta Physiol Scand 118: 229 –239, 1983.
10. ANGEL MJ, GUERTIN P, AND JIMENEZ I. Group I extensor afferents
evoke disynaptic EPSPs in cat hindlimb extensor motorneurones
during fictive locomotion. J Physiol (Lond) 494: 851– 861, 1996.
11. AOYAMA M, HONGO T, AND KUDO N. Sensory input to cells of origin of
uncrossed spinocerebellar tract located below Clarke’s column in
the cat. J Physiol (Lond) 398: 233–257, 1988.
12. ARSHAVSKY YI, BERKINBLIT MB, FUKSON OI, GELFAND IM, AND ORLOVSKY
GN. Recordings of neurones of the dorsal spinocerebellar tract
during evoked locomotion. Brain Res 43: 272–275, 1972.
13. ARSHAVSKY YI, BERKINBLIT MB, FUKSON OI, GELFAND IM, AND ORLOVSKY
GN. Origin of modulation in neurones of the ventral spinocerebellar tract during locomotion. Brain Res 43: 276 –279, 1972.
14. ARSHAVSKY YI, GELFAND IM, ORLOVSKY GN, AND PAVLOVA GA. Messages conveyed by spinocerebellar pathways during scratching in
the cat. II. Activity of neurons of the ventral spinocerebellar tract.
Brain Res 151: 493–506, 1978.
15. ARSHAVSKY YI, GELFAND IM, ORLOVSKY GN, PAVLOVA GA, AND POPOVA
LB. Origin of signals conveyed by the ventral spino-cerebellar tract
and spino-reticulo-cerebellar pathway. Exp Brain Res 54: 426 – 431,
1984.
16. ASHE J AND GEORGOPOULOS AP. Movement parameters and neural
activity in motor cortex and area 5. Cereb Cortex 4: 590 – 600, 1994.
17. BAKER J, GOLDBERG J, HERMANN G, AND PETERSON B. Optimal response planes and canal convergence in secondary neurons in
vestibular nuclei of alert cats. Brain Res 294: 133–137, 1984.
18. BAKER J, GOLDBERG J, HERMANN G, AND PETERSON B. Spatial and
temporal response properties of secondary neurons that receive
convergent input in vestibular nuclei of alert cats. Brain Res 294:
138 –143, 1984.
19. BALDISSERA F, HULTBORN H, AND ILLERT M. Integration in spinal
neuronal systems. In: Handbook of Physiology. The Nervous System. Motor Control. Bethesda, MD: Am. Physiol. Soc., 1981, sect. 1,
vol. II, pt. 1, chapt. 12, p. 509 –525.
20. BÄSSLER U. The femur-tibia control system of stick insects: a model
system for the study of the neural basis of joint control. Brain Res
Rev 18: 207–226, 1993.
21. BÄSSLER U AND BUSCHGES A. Pattern generation for stick insect
walking movements—multisensory control of a locomotor program. Brain Res Rev 27: 65– 88, 1998.
22. BASTIAN AJ, MARTIN TA, KEATING JG, AND THACH WT. Cerebellar
ataxia: abnormal control of interaction torques across multiple
joints. J Neurophysiol 76: 492–509, 1996.
23. BEHRENDS T, SCHOMBURG ED, AND STEFFENS H. Facilitatory interaction between cutaneous afferents from low threshold mechanoreceptors and nociceptors in segmental reflex pathways to alphamotoneurons. Brain Res 260: 131–134, 1983.
24. BERKINBLIT MB, FOOKSON OI, SMETANIN B, ADAMOVICH SV, AND POIZNER
H. The interaction of visual and proprioceptive inputs in pointing to
actual and remembered targets. Exp Brain Res 107: 326 –330, 1995.
25. BERKOWITZ A AND STEIN PS. Activity of descending propriospinal
axons in the turtle hindlimb enlargement during two forms of
fictive scratching: broad tuning to regions of the body surface.
J Neurosci 14: 5089 –5104, 1994.
26. BERKOWITZ A AND STEIN PS. Activity of descending propriospinal
axons in the turtle hindlimb enlargement during two forms of
fictive scratching: phase analyses. J Neurosci 14: 5105–5119, 1994.
27. BIANCHI L, ANGELINI D, ORANI GP, AND LACQUANITI F. Kinematic
coordination in human gait: relation to mechanical energy cost.
J Neurophysiol 79: 2155–2170, 1998.
28. BLOEDEL JR AND COURVILLE J. Cerebellar afferent systems In: Handbook of Physiology. The Nervous System. Motor Control. Bethesda,
MD: Am. Physiol. Soc., 1981, sect. 1, vol. II, pt. 2, chapt. 16, p.
735– 829.
29. BOCK O AND ARNOLD K. Error accumulation and error correction in
sequential pointing movements. Exp Brain Res 95: 111–117, 1993.
30. BOEHME CC. The neural structure of Clarke’s nucleus of the spinal
cord. J Comp Neurol 132: 445– 462, 1968.
31. BONASERA SJ AND NICHOLS TR. Mechanical actions of heterogenic
reflexes linking long toe flexors with ankle and knee extensors of
the cat hindlimb. J Neurophysiol 71: 1096 –1110, 1994.
32. BONASERA SJ AND NICHOLS TR. Mechanical actions of heterogenic
reflexes among ankle stabilizers and their interactions with plantarflexors of the cat hindlimb. J Neurophysiol 75: 2050 –2070, 1996.
33. BOSCO G AND POPPELE RE. Broad directional tuning in spinal projections to the cerebellum. J Neurophysiol 70: 863– 866, 1993.
34. BOSCO G AND POPPELE RE. Representation of multiple kinematic
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
in individual muscles or associated deep structures as
well as in the skin are assembled at very early stages of
central processing to provide a representation of limb
kinematics. Because this occurs at these earliest stages, it
suggests that the peripheral apparatus may also in some
way play a role in its determination. The result, however,
is that centrally directed sensory information may be
encoded in a framework common to that of central motor
activity that relates to limb kinematics. It may therefore
be analogous to the situation in the superior colliculus
where sensory information from various modalities is
mapped congruently within a retinotopic map (227, 306)
that may be modified or transformed by gaze (123, 124,
157, 158); that is, the sensory information is combined and
integrated through a common coding framework. Although the retinal projection may provide the basis for a
common framework for eye, head, and body movement
control, limb biomechanics and associated proprioceptors appear to provide the basis for a common framework
for limb movement control.
561
562
35.
36.
37.
38.
39.
40.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
parameters of the cat hindlimb in spinocerebellar activity. J Neurophysiol 78: 1421–1432, 1997.
BOSCO G AND POPPELE RE. Do DSCT neurons encode limb kinematics? Neural Control Movement Abstr 1: B1, 1998.
BOSCO G AND POPPELE RE. Reference frames for spinal proprioception: kinematics based or kinetics based? J Neurophysiol 83: 2946 –
2955, 2000.
BOSCO G, POPPELE RE, AND EIAN J. Reference frames for spinal
proprioception: limb endpoint based or joint-level based? J Neurophysiol 83: 2931–2945, 2000.
BOSCO G, RANKIN A, AND POPPELE R. Representation of passive
hindlimb postures in cat spinocerebellar activity. J Neurophysiol
76: 715–726, 1996.
BOUSSAOUD D, BARTH TM, AND WISE SP. Effects of gaze on apparent
visual responses of frontal cortex neurons. Exp Brain Res 93:
423– 434, 1993.
BOUSSAOUD D AND BREMMER F. Gaze effects in the cerebral cortex:
reference frames for space coding and action. Exp Brain Res 128:
170 –180, 1999.
BRINK EE, SUZUKI I, TIMERICK SJ, AND WILSON VJ. Directional sensitivity of neurons in the lumbar spinal cord to neck rotation. Brain
Res 323: 172–175, 1984.
BRINK EE, SUZUKI I, TIMERICK SJ, AND WILSON VJ. Tonic neck reflex of
the decerebrate cat: a role for propriospinal neurons. J Neurophysiol 54: 978 –987, 1985.
BROWN PB AND FUCHS JL. Somatotopic representation of hindlimb
skin in cat dorsal horn. J Neurophysiol 38: 1–9, 1975.
BROWN PB, KOERBER HR, AND MILLECCHIA R. Assembly of the dorsal
horn somatotopic map. Somatosens Motor Res 14: 93–106, 1997.
BURGESS PR, WEI JY, CLARK FJ, AND SIMON J. Signaling of kinesthetic
information by peripheral sensory receptors. Annu Rev Neurosci 5:
171–187, 1982.
BURKE D, GANDEVIA SC, AND MACEFIELD G. Responses to passive
movement of receptors in joint, skin and muscle of the human
hand. J Physiol (Lond) 402: 347–361, 1988.
BURKE RE. Motor units: anatomy, physiology and functional organization. In: Handbook of Physiology. The Nervous System. Motor
Control. Bethesda, MD: Am. Physiol. Soc., 1981, sect. 1, vol. II, pt.
1, chapt. 10, p. 345– 422.
CARLI G, FARABOLLINI F, FONTANI G, AND MEUCCI M. Slowly adapting
receptors in cat hip joint. J Neurophysiol 42: 767–778, 1979.
CAVALLARI P, KATZ R, AND PENICAUD A. Pattern of projections of
group I afferents from elbow muscles to motoneurones supplying
wrist muscles in man. Exp Brain Res 91: 311–319, 1992.
CHAPIN JK AND LIN CS. Mapping the body representation in the SI
cortex of anesthetized and awake rats. J Comp Neurol 229: 199 –
213, 1984.
CLARK FJ, BURGESS RC, CHAPIN JW, AND LIPSCOMB WT. Role of
intramuscular receptors in the awareness of limb position. J Neurophysiol 54: 1529 –1540, 1985.
CLIFFER KD, HASEGAWA T, AND WILLIS WD. Responses of neurons in
the gracile nucleus of cats to innocuous and noxious stimuli: basic
characterization and antidromic activation from the thalamus.
J Neurophysiol 68: 818 – 832, 1992.
COHEN DA, PRUD’HOMME MJ, AND KALASKA JF. Tactile activity in
primate primary somatosensory cortex during active arm movements: correlation with receptive field properties. J Neurophysiol
71: 161–172, 1994.
CONWAY BA, HULTBORN H, AND KIEHN O. Proprioceptive input resets
central locomotor rhythm in the spinal cat. Exp Brain Res 68:
643– 656, 1987.
COSTANZO RM AND GARDNER EP. Multiple-joint neurons in somatosensory cortex of awake monkeys. Brain Res 214: 321–333, 1981.
CRAMMOND DJ AND KALASKA JF. Neuronal activity in primate parietal
cortex area 5 varies with intended movement direction during an
instructed-delay period. Exp Brain Res 76: 458 – 462, 1989.
CRUTCHER MD AND ALEXANDER GE. Movement-related neuronal activity selectively coding either direction or muscle pattern in three
motor areas of the monkey. J Neurophysiol 64: 151–163, 1990.
CRUTCHER MD AND DELONG MR. Single cell studies of the primate
putamen. II. Relations to direction of movement and pattern of
muscular activity. Exp Brain Res 53: 244 –258, 1984.
Volume 81
59. CURTIS DR, ECCLES JC, AND LUNDBERG A. Intracellular recording from
cells in Clarke’s column. Acta Physiol Scand 43: 303–314, 1958.
60. CZARKOWSKA J, JANKOWSKA E, AND SYBIRSKA E. Common interneurones in reflex pathways from group 1a and 1b afferents of knee
flexors and extensors in the cat. J Physiol (Lond) 310: 367–380,
1981.
61. DELONG MR, CRUTCHER MD, AND GEORGOPOULOS AP. Primate globus
pallidus and subthalamic nucleus: functional organization. J Neurophysiol 53: 530 –543, 1985.
62. DENNY-BROWN D. On the nature of postural reflexes. Proc R Soc
Lond B Biol Sci 104: 252–301, 1929.
63. DIETZ V. Human neuronal control of automatic functional movements: interaction between central programs and afferent input.
Physiol Rev 72: 33– 69, 1992.
64. DIZIO P AND LACKNER JR. Motor adaptation to Coriolis force perturbations of reaching movements: endpoint but not trajectory adaptation transfers to the nonexposed arm. J Neurophysiol 74: 1787–
1792, 1995.
65. DUM RP AND KENNEDY TT. Synaptic organization of defined motorunit types in cat tibialis anterior. J Neurophysiol 43: 1631–1644,
1980.
66. DUYSENS J AND PEARSON KG. Inhibition of flexor burst generation by
loading ankle extensor muscles in walking cats. Brain Res 187:
321–332, 1980.
67. DUYSENS J, TAX AA, TRIPPEL M, AND DIETZ V. Phase-dependent reversal of reflexly induced movements during human gait. Exp Brain
Res 90: 404 – 414, 1992.
68. DUYSENS J, TRIPPEL M, HORSTMANN GA, AND DIETZ V. Gating and
reversal of reflexes in ankle muscles during human walking. Exp
Brain Res 82: 351–358, 1990.
69. EBNER TJ AND FU Q. What features of visually guided arm movements are encoded in the simple spike discharge of cerebellar
Purkinje cells? Prog Brain Res 114: 431– 447, 1997.
70. ECCLES JC, OSCARSSON O, AND WILLIS WD. Synaptic action of group I
and II afferent fibres of muscles on cells of the dorsal spinocerebellar tract. J Physiol (Lond) 158: 517–543, 1961.
71. ECCLES RM AND LUNDBERG A. Synaptic action in motoneurones by
afferents which may evoke the flexion reflex. Arch Ital Biol 97:
199 –221, 1959.
72. EDGLEY SA AND GALLIMORE CM. The morphology and projections of
dorsal horn spinocerebellar tract neurones in the cat. J Physiol
(Lond) 397: 99 –111, 1988.
73. EDGLEY SA AND JANKOWSKA E. Information processed by dorsal horn
spinocerebellar tract neurones in the cat. J Physiol (Lond) 397:
81–97, 1988.
74. EIDE E, FEDINA L, JANSEN J, LUNDBERG A, AND VYKLICKY L. Unitary
excitatory postsynaptic potentials in Clarke’s column neurones.
Nature 215: 1176 –1177, 1967.
75. EIDE E, FEDINA L, JANSEN J, LUNDBERG A, AND VYKLICKY L. Properties
of Clarke’s column neurones. Acta Physiol Scand 77: 125–144,
1969.
76. EIDE E, FEDINA L, JANSEN J, LUNDBERG A, AND VYKLICKY L. Unitary
components in the activation of Clarke’s column neurones. Acta
Physiol Scand 77: 145–158, 1969.
77. ELEK J, PROCHAZKA A, HULLIGER M, AND VINCENT S. In-series compliance of gastrocnemius muscle in cat step cycle: do spindles signal
origin-to-insertion length? J Physiol (Lond) 429: 237–258, 1990.
78. EVARTS EV. Sherrington’s concept of proprioception. Trends Neurosci 4: 44 – 46, 1981.
79. FAVILLA M, HENING W, AND GHEZ C. Trajectory control in targeted
force impulses. VI. Independent specification of response amplitude and direction. Exp Brain Res 75: 280 –294, 1989.
80. FELLEMAN DJ, WALL JT, CUSICK CG, AND KAAS JH. The representation
of the body surface in S-I of cats. J Neurosci 3: 1648 –1669, 1983.
81. FERRAINA S AND BIANCHI L. Posterior parietal cortex: functional
properties of neurons in area 5 during an instructed-delay reaching
task within different parts of space. Exp Brain Res 99: 175–178,
1994.
82. FERRELL WR. The adequacy of stretch receptors in the cat knee joint
for signalling joint angle throughout a full range of movement.
J Physiol (Lond) 299: 85–99, 1980.
83. FIELD EC AND STEIN PS. Spinal cord coordination of hindlimb move-
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
41.
G. BOSCO AND R. E. POPPELE
April 2001
84.
85.
86.
87.
88.
89.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
108.
ments in the turtle: intralimb temporal relationships during scratching and swimming. J Neurophysiol 78: 1394 –1403, 1997.
FIELD EC AND STEIN PS. Spinal cord coordination of hindlimb movements in the turtle: interlimb temporal relationships during bilateral scratching and swimming. J Neurophysiol 78: 1404 –1413, 1997.
FLANAGAN JR AND WING AM. The role of internal models in motion
planning and control: evidence from grip force adjustments during
movements of hand-held loads. J Neurosci 17: 1519 –1528, 1997.
FLANDERS M AND SOECHTING JF. Parcellation of sensorimotor transformations for arm movements. J Neurosci 10: 2420 –2427, 1990.
FLANDERS M, TILLERY SIH, AND SOECHTING JF. Early stages in a
sensorimotor transformation. Behav Brain Sci 15: 309 –362, 1992.
FLORENCE SL, WALL JT, AND KAAS JH. Somatotopic organization of
inputs from the hand to the spinal gray and cuneate nucleus of
monkeys with observations on the cuneate nucleus of humans.
J Comp Neurol 286: 48 –70, 1989.
FOREMAN RD, SCHMIDT RF, AND WILLIS WD. Convergence of muscle
and cutaneous input onto primate spinothalamic tract neurons.
Brain Res 124: 555–560, 1977.
FORSSBERG H, GRILLNER S, AND ROSSIGNOL S. Phase dependent reflex
reversal during walking in chronic spinal cats. Brain Res 85: 103–
107, 1975.
FORTIER PA, KALASKA JF, AND SMITH AM. Cerebellar neuronal activity
related to whole-arm reaching movements in the monkey. J Neurophysiol 62: 198 –211, 1989.
FOUAD K AND PEARSON KG. Effects of extensor muscle afferents on
the timing of locomotor activity during walking in adult rats. Brain
Res 749: 320 –328, 1997.
FREUND HJ. Motor unit and muscle activity in voluntary motor
control. Physiol Rev 63: 387– 436, 1983.
FRIEDMAN DP, JONES EG, AND BURTON H. Representation pattern in
the second somatic sensory area of the monkey cerebral cortex.
J Comp Neurol 192: 21– 41, 1980.
FU QG, FLAMENT D, COLTZ JD, AND EBNER TJ. Temporal encoding of
movement kinematics in the discharge of primate primary motor
and premotor neurons. J Neurophysiol 73: 836 – 854, 1995.
FU QG, FLAMENT D, COLTZ JD, AND EBNER TJ. Relationship of cerebellar Purkinje cell simple spike discharge to movement kinematics in the monkey. J Neurophysiol 78: 478 – 491, 1997.
FU QG, SUAREZ JI, AND EBNER TJ. Neuronal specification of direction
and distance during reaching movements in the superior precentral
premotor area and primary motor cortex of monkeys. J Neurophysiol 70: 2097–2116, 1993.
GARDNER EP. Somatosensory cortical mechanisms of feature detection in tactile and kinesthetic discrimination. Can J Physiol Pharmacol 66: 439 – 454, 1988.
GARDNER EP AND COSTANZO RM. Properties of kinesthetic neurons in
somatosensory cortex of awake monkeys. Brain Res 214: 301–319,
1981.
GEORGOPOULOS AP. On reaching. Annu Rev Neurosci 9: 147–170,
1986.
GEORGOPOULOS AP. Higher order motor control. Annu Rev Neurosci
14: 361–377, 1991.
GEORGOPOULOS AP, DELONG MR, AND CRUTCHER MD. Relations between parameters of step-tracking movements and single cell discharge in the globus pallidus and subthalamic nucleus of the behaving monkey. J Neurosci 3: 1586 –1598, 1983.
GEORGOPOULOS AP, KALASKA JF, CAMINITI R, AND MASSEY JT. On the
relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J Neurosci 2:
1527–1537, 1982.
GEORGOPOULOS AP, KETTNER RE, AND SCHWARTZ AB. Primate motor
cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal
population. J Neurosci 8: 2928 –2937, 1988.
GHEZ C, GORDON J, AND GHILARDI MF. Impairments of reaching
movements in patients without proprioception. II. Effects of visual
information on accuracy. J Neurophysiol 73: 361–372, 1995.
GHEZ C, HENING W, AND GORDON J. Organization of voluntary movement. Curr Opin Neurobiol 1: 664 – 671, 1991.
GHEZ C AND SAINBURG R. Proprioceptive control of interjoint coordination. Can J Physiol Pharmacol 73: 273–284, 1995.
GISZTER SF, MCINTYRE J, AND BIZZI E. Kinematic strategies and
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
563
sensorimotor transformations in the wiping movements of frogs.
J Neurophysiol 62: 750 –767, 1989.
GOMI H, SHIDARA M, TAKEMURA A, INOUE Y, KAWANO K, AND KAWATO M.
Temporal firing patterns of Purkinje cells in the cerebellar ventral
paraflocculus during ocular following responses in monkeys. I.
Simple spikes. J Neurophysiol 80: 818 – 831, 1998.
GOODBODY SJ AND WOLPERT DM. Temporal and amplitude generalization in motor learning. J Neurophysiol 79: 1825–1838, 1998.
GORASSINI MA, PROCHAZKA A, HIEBERT GW, AND GAUTHIER MJ. Corrective responses to loss of ground support during walking. I. Intact
cats. J Neurophysiol 71: 603– 610, 1994.
GORDON J, GHILARDI MF, AND GHEZ C. Accuracy of planar reaching
movements. I. Independence of direction and extent variability.
Exp Brain Res 99: 97–111, 1994.
GORDON J, GHILARDI MF, AND GHEZ C. Impairments of reaching
movements in patients without proprioception. I. Spatial errors.
J Neurophysiol 73: 347–360, 1995.
GOSLOW GE JR, REINKING RM, AND STUART DG. The cat step cycle:
hind limb joint angles and muscle lengths during unrestrained
locomotion. J Morphol 141: 1– 41, 1973.
GOSSARD JP, BROWNSTONE RM, BARAJON I, AND HULTBORN H. Transmission in a locomotor-related group Ib pathway from hindlimb
extensor muscles in the cat. Exp Brain Res 98: 213–228, 1994.
GRAF W, SIMPSON JI, AND LEONARD CS. Spatial organization of visual
messages of the rabbit’s cerebellar flocculus. II. Complex and
simple spike responses of Purkinje cells. J Neurophysiol 60: 2091–
2121, 1988.
GRANT G. Spinal course and somatotopically localized termination
of the spinocerebellar tracts. An experimental study in the cat. Acta
Physiol Scand Suppl 193: 5– 45, 1962.
GRANT G. Projection of the external cuneate nucleus onto the
cerebellum in the cat: an experimental study using silver methods.
Exp Neurol 5: 179 –195, 1962.
GRANT G, WIKSTEN B, BERKLEY KJ, AND ALDSKOGIUS H. The location of
cerebellar-projecting neurons within the lumbosacral spinal cord in
the cat. An anatomical study with HRP and retrograde chromatolysis. J Comp Neurol 204: 336 –348, 1982.
GRASSO R, BIANCHI L, AND LACQUANITI F. Motor patterns for human
gait: backward versus forward locomotion. J Neurophysiol 80:
1868 –1885, 1998.
GRILLNER S AND HONGO T. Vestibulospinal effects on motoneurons
and interneurones in the lumbosacral cord. Prog Brain Res 37:
243–262, 1972.
GRILLNER S AND ROSSIGNOL S. On the initiation of the swing phase of
locomotion in chronic spinal cats. Brain Res 146: 269 –277, 1978.
GROH JM AND SPARKS DL. Saccades to somatosensory targets. II.
Motor convergence in primate superior colliculus. J Neurophysiol
75: 428 – 438, 1996.
GROH JM AND SPARKS DL. Saccades to somatosensory targets. III.
Eye-position-dependent somatosensory activity in primate superior
colliculus. J Neurophysiol 75: 439 – 453, 1996.
GUERTIN P, ANGEL MJ, PERREAULT MC, AND MCCREA DA. Ankle extensor group I afferents excite extensors throughout the hindlimb
during fictive locomotion in the cat. J Physiol (Lond) 487: 197–209,
1995.
HASAN Z. Role of proprioceptors in neural control. Curr Opin
Neurobiol 2: 824 – 829, 1992.
HASAN Z AND STUART DG. Animal solutions to problems of movement control: the role of proprioceptors. Annu Rev Neurosci 11:
199 –223, 1988.
HENNEMAN E AND MENDELL LM. Functional organization of motoneuron pool and its inputs. In: Handbook of Physiology. The Nervous
System. Motor Control. Bethesda, MD: Am. Physiol. Soc., 1981,
sect. 1, vol. II, pt. 1, chapt. 11, p. 423–507.
HIEBERT GW, GORASSINI MA, JIANG W, PROCHAZKA A, AND PEARSON KG.
Corrective responses to loss of ground support during walking. II.
Comparison of intact and chronic spinal cats. J Neurophysiol 71:
611– 622, 1994.
HIEBERT GW AND PEARSON KG. Contribution of sensory feedback to
the generation of extensor activity during walking in the decerebrate cat. J Neurophysiol 81: 758 –770, 1999.
HIEBERT GW, WHELAN PJ, PROCHAZKA A, AND PEARSON KG. Contribution of hind limb flexor muscle afferents to the timing of phase
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
90.
SPINAL PROPRIOCEPTION
564
132.
133.
134.
135.
136.
138.
139.
140.
141.
142.
143.
144.
145.
146.
147.
148.
149.
150.
151.
152.
153.
154.
155.
transitions in the cat step cycle. J Neurophysiol 75: 1126 –1137,
1996.
HOLMQUIST B, LUNDBERG A, AND OSCARSSON O. Functional organization of the dorsal spinocerebellar tract in the cat. V. Further experiments on convergence of excitatory and inhibitory actions.
Acta Physiol Scand 38: 76 –90, 1956.
HOLMQUIST B, LUNDBERG A, AND OSCARSSON O. Supraspinal inhibitory
control of transmission to three ascending spinal pathways influenced by the flexion reflex afferents. Arch Ital Biol 98: 60 – 80, 1960.
HONGO T, JANKOWSKA E, OHNO T, SASAKI S, YAMASHITA M, AND YOSHIDA
K. The same interneurones mediate inhibition of dorsal spinocerebellar tract cells and lumbar motoneurones in the cat. J Physiol
(Lond) 342: 161–180, 1983.
HONGO T AND OKADA Y. Cortically evoked pre- and postsynaptic
inhibition of impulse transmission to the dorsal spinocerebellar
tract. Exp Brain Res 3: 163–177, 1967.
HONGO T, OKADA Y, AND SATO M. Corticofugal influences on transmission to the dorsal spinocerebellar tract from hindlimb primary
afferents. Exp Brain Res 3: 135–149, 1967.
HULLIGER M. The mammalian muscle spindle and its central control.
Rev Physiol Biochem Pharmacol 101: 1–110, 1984.
HULLIGER M. The role of muscle spindle receptors and fusimotor
neurones in the control of movement. EEG Clin Neurophysiol 39:
58 – 66, 1987.
HULTBORN H. Transmission in the pathway of reciprocal Ia inhibition to motoneurones and its control during the tonic stretch reflex.
Prog Brain Res 44: 235–255, 1976.
HULTBORN H, ILLERT M, AND SANTINI M. Convergence on interneurones mediating the reciprocal Ia inhibition of motoneurones. III.
Effects from supraspinal pathways. Acta Physiol Scand 96: 368 –
391, 1976.
HULTBORN H AND LUNDBERG A. Reciprocal inhibition during the
stretch reflex. Acta Physiol Scand 85: 136 –138, 1972.
HULTBORN H AND UDO M. Convergence in the reciprocal Ia inhibitory
pathway of excitation from descending pathways and inhibition
from motor axon collaterals. Acta Physiol Scand 84: 95–108, 1972.
IWAMURA Y. Hierarchical somatosensory processing. Curr Opin
Neurobiol 8: 522–528, 1998.
JAEGER D, GILMAN S, AND ALDRIDGE JW. Primate basal ganglia activity
in a precued reaching task: preparation for movement. Exp Brain
Res 95: 51– 64, 1993.
JAMI L. Golgi tendon organs in mammalian skeletal muscle: functional properties and central actions. Physiol Rev 72: 623– 666,
1992.
JANKOWSKA E. Interneuronal relay in spinal pathways from proprioceptors. Prog Neurobiol 38: 335–378, 1992.
JANKOWSKA E AND EDGLEY S. Interactions between pathways controlling posture and gait at the level of spinal interneurones in the
cat. Prog Brain Res 97: 161–171, 1993.
JANKOWSKA E, JOHANNISSON T, AND LIPSKI J. Common interneurones
in reflex pathways from group 1a and 1b afferents of ankle extensors in the cat. J Physiol (Lond) 310: 381– 402, 1981.
JANKOWSKA E, JUKES MGM, AND LUND S. On the presynaptic inhibition of transmission to the dorsal spino-cerebellar tract. J Physiol
(Lond) 177: 19 –21, 1964.
JANKOWSKA E, JUKES MGM, AND LUND S. The pattern of presynaptic
inhibition of transmission to the dorsal spino-cerebellar tract of the
cat. J Physiol (Lond) 178: 17–18, 1965.
JANSEN JK, POPPELE RE, AND TERZUOLO CA. Transmission of proprioceptive information via the dorsal spinocerebellar. Brain Res 6:
382–384, 1967.
JANSEN JK AND WALLOE L. Signal transmission between successive
neurons in the dorsal spinocerebellar pathway. In: The Neurosciences: Second Study Program, edited by Schmitt FO. New York:
Rockefeller Univ. Press, 1970, p. 617– 629.
JANSEN JKS, NICOLAYSEN K, AND RUDJORD T. On the firing pattern of
spinal neurones activated from the secondary endings of muscle
spindles. Acta Physiol Scand 70: 188 –193, 1967.
JANSEN JKS, NICOLAYSEN K, AND WALLOE L. On the inhibition of
transmission to the dorsal spinocerebellar tract by stretch of various ankle muscles of the cat. Acta Physiol Scand 70: 362–368, 1967.
JANSEN JKS, NICOLAYSEN K, AND WALLOE L. The firing pattern of
156.
157.
158.
159.
160.
161.
162.
163.
164.
165.
166.
167.
168.
169.
170.
171.
172.
173.
174.
175.
176.
177.
178.
179.
180.
Volume 81
dorsal spinocerebellar tract neurons during inhibition. Acta
Physiol Scand 77: 68 – 84, 1969.
JANSEN JKS AND RUDJORD T. Dorsal spinocerebellar tract. Response
patterns of nerve fibers to muscle stretch. Science 149: 1109 –1111,
1965.
JAY MF AND SPARKS DL. Sensorimotor integration in the primate
superior colliculus. I. Motor convergence. J Neurophysiol 57: 22–
34, 1987.
JAY MF AND SPARKS DL. Sensorimotor integration in the primate
superior colliculus. II. Coordinates of auditory signals. J Neurophysiol 57: 35–55, 1987.
JOHANSSON RS AND COLE KJ. Sensory-motor coordination during
grasping and manipulative actions. Curr Opin Neurobiol 2: 815–
823, 1992.
JONES EG, FRIEDMAN DP, AND HENDRY SH. Thalamic basis of placeand modality-specific columns in monkey somatosensory cortex: a
correlative anatomical and physiological study. J Neurophysiol 48:
545–568, 1982.
JONES LA. Peripheral mechanisms of touch and proprioception.
Can J Physiol Pharmacol 72: 484 – 487, 1994.
JORDAN MI. Computational aspects of motor control and motor
learning. In: Handbook of Perception and Action: Motor Skills,
edited by Heuer H and Keele S. New York: Academic, 1995, vol. 2,
p. 71–118.
KAAS JH. Topographic maps are fundamental to sensory processing. Brain Res Bull 44: 107–112, 1997.
KAAS JH, NELSON RJ, SUR M, LIN CS, AND MERZENICH MM. Multiple
representations of the body within the primary somatosensory
cortex of primates. Science 204: 521–523, 1979.
KAKEI S, HOFFMAN DS, AND STRICK PL. Coordinate frames for wrist
movements. Soc Neurosci Abstr 24: 2108, 1998.
KAKEI S, HOFFMAN DS, AND STRICK PL. Muscle and movement representations in the primary motor cortex. Science 285: 2136 –2139,
1999.
KALASKA JF. The representation of arm movements in postcentral
and parietal cortex. Can J Physiol Pharmacol 66: 455– 463, 1988.
KALASKA JF. Central neural mechanisms of touch and proprioception. Can J Physiol Pharmacol 72: 542–545, 1994.
KALASKA JF, CAMINITI R, AND GEORGOPOULOS AP. Cortical mechanisms related to the direction of two-dimensional arm movements:
relations in parietal area 5 and comparison with motor cortex. Exp
Brain Res 51: 247–260, 1983.
KALASKA JF, COHEN DA, PRUD’HOMME M, AND HYDE ML. Parietal area
5 neuronal activity encodes movement kinematics, not movement
dynamics. Exp Brain Res 80: 351–364, 1990.
KARGO WJ, DAVIES MR, AND GISZTER SF. Segmental afferent regulation of hindlimb wiping in the spinal frog. Ann NY Acad Sci 860:
456 – 457, 1998.
KARGO WJ AND GISZTER SF. Rapid correction of aimed movements
by summation of force-field primitives. J Neurosci 20: 409 – 426,
2000.
KASPER J, SCHOR RH, AND WILSON VJ. Response of vestibular neurons
to head rotations in vertical planes. I. Response to vestibular
stimulation. J Neurophysiol 60: 1753–1764, 1988.
KASPER J, SCHOR RH, AND WILSON VJ. Response of vestibular neurons
to head rotations in vertical planes. II. Response to neck stimulation and vestibular-neck interaction. J Neurophysiol 60: 1765–1778,
1988.
KAWATO M. Internal models for motor control and trajectory planning. Curr Opin Neurobiol 9: 718 –727, 1999.
KAWATO M AND WOLPERT D. Internal models for motor control.
Novartis Found Symp 218: 291–304, 1998.
KIM JH, EBNER TJ, AND BLOEDEL JR. Comparison of response properties of dorsal and ventral spinocerebellar tract neurons to a
physiological stimulus. Brain Res 369: 125–135, 1986.
KITAI ST AND MORIN F. Microelectrode study of dorsal spinocerebellar tract. Am J Physiol 203: 799 – 802, 1963.
KNOX CK, KUBOTA S, AND POPPELE RE. A determination of excitability changes in dorsal spinocerebellar tract neurons from spike-train
analysis. J Neurophysiol 40: 626 – 646, 1977.
KNOX CK AND POPPELE RE. Correlation analysis of stimulus-evoked
changes in excitability of spontaneously firing neurons. J Neurophysiol 40: 616 – 625, 1977.
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
137.
G. BOSCO AND R. E. POPPELE
April 2001
SPINAL PROPRIOCEPTION
206. MANN MD. Clarke’s column and the dorsal spinocerebellar tract: a
review. Brain Behav Evol 7: 34 – 83, 1973.
207. MARKUS EJ, QIN YL, LEONARD B, SKAGGS WE, MCNAUGHTON BL, AND
BARNES CA. Interactions between location and task affect the spatial and directional firing of hippocampal neurons. J Neurosci 15:
7079 –7094, 1995.
208. MASINO T. Brainstem control of orienting movements: intrinsic
coordinate systems and underlying circuitry. Brain Behav Evol 40:
98 –111, 1992.
209. MASSION J. Movement, posture and equilibrium: interaction and
coordination. Prog Neurobiol 38: 35–56, 1992.
210. MATSUSHITA M. Spinocerebellar projections from the lowest lumbar
and sacral-caudal segments in the cat, as studied by anterograde
transport of wheat germ agglutinin-horseradish peroxidase.
J Comp Neurol 274: 239 –254, 1988.
211. MATSUSHITA M. Projections from the lowest lumbar and sacralcaudal segments to the cerebellar nuclei in the rat, studied by
anterograde axonal tracing. J Comp Neurol 404: 21–32, 1999.
212. MATSUSHITA M AND GAO X. Projections from the thoracic cord to the
cerebellar nuclei in the rat, studied by anterograde axonal tracing.
J Comp Neurol 386: 409 – 421, 1997.
213. MATSUSHITA M AND HOSOYA Y. Spinocerebellar projections to lobules
III to V of the anterior lobe in the cat, as studied by retrograde
transport of horseradish peroxidase. J Comp Neurol 208: 127–143,
1982.
214. MATSUSHITA M, HOSOYA Y, AND IKEDA M. Anatomical organization of
the spinocerebellar system in the cat, as studied by retrograde
transport of horseradish peroxidase. J Comp Neurol 184: 81–106,
1979.
215. MATSUSHITA M AND IKEDA M. Spinocerebellar projections to the
vermis of the posterior lobe and the paramedian lobule in the cat,
as studied by retrograde transport of horseradish peroxidase.
J Comp Neurol 192: 143–162, 1980.
216. MATSUSHITA M AND OKADO N. Spinocerebellar projections to lobules
I and II of the anterior lobe in the cat, as studied by retrograde
transport of horseradish peroxidase. J Comp Neurol 197: 411– 424,
1981.
217. MATSUSHITA M AND YAGINUMA H. Spinocerebellar projections from
spinal border cells in the cat as studied by anterograde transport of
wheat germ agglutinin-horseradish peroxidase. J Comp Neurol 288:
19 –38, 1989.
218. MATTHEWS PBC. Mammalian Muscle Receptors and Their Central
Actions. London: Arnold, 1972.
219. MATTHEWS PBC. Evolving views on the internal operation and
functional role of the muscle spindle. J Physiol (Lond) 320:
1–30, 1981.
220. MATTHEWS PBC. Where does Sherringon’s “muscular sense” originate? Muscles, joints, corollary discharges? Annu Rev Neurosci 5:
189 –218, 1982.
221. MCCLOSKEY DI. Corollary discharges: motor commands and perception. In: Handbook of Physiology. The Nervous System. Motor
Control. Bethesda, MD: Am. Physiol. Soc., 1981, sect. 1, vol. II, pt.
2, chapt. 32, p. 1415–1447.
222. MCCLOSKEY DI AND PROCHAZKA A. The role of sensory information in
the guidance of voluntary movement: reflections on a symposium
held at the 22nd annual meeting of the Society for Neuroscience.
Somatosens Motor Res 11: 69 –76, 1994.
223. MCCREA DA. Neuronal basis of afferent-evoked enhancement of
locomotor activity. Ann NY Acad Sci 860: 216 –225, 1998.
224. MCCREA DA, SHEFCHYK SJ, STEPHENS MJ, AND PEARSON KG. Disynaptic group I excitation of synergist ankle extensor motoneurones
during fictive locomotion in the cat. J Physiol (Lond) 487: 527–539,
1995.
225. MCINTYRE AK AND MARK RF. Synaptic linkage between afferent
fibres of the cat’s hind limb and ascending fibres in the dorsolateral
funiculus. J Physiol (Lond) 153: 306 –330, 1960.
226. MENDELL LM. Physiological properties of unmyelinated fiber projections to the spinal cord. Exp Neurol 16: 316 –332, 1966.
227. MEREDITH MA AND STEIN BE. Visual, auditory, and somatosensory
convergence on cells in superior colliculus results in multisensory
integration. J Neurophysiol 56: 640 – 662, 1986.
228. MESSIER J AND KALASKA JF. Differential effect of task conditions on
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
181. KUO AD AND ZAJAC FE. Human standing posture: multi-joint movement strategies based on biomechanical constraints. Prog Brain
Res 97: 349 –358, 1993.
182. LABELLA LA AND MCCREA DA. Evidence for restricted central convergence of cutaneous afferents on an excitatory reflex pathway to
medial gastrocnemius motoneurons. J Neurophysiol 64: 403– 412,
1990.
183. LACKNER JR AND DIZIO P. Rapid adaptation to Coriolis force perturbations of arm trajectory. J Neurophysiol 72: 299 –313, 1994.
184. LACQUANITI F, GRASSO R, AND ZAGO M. Motor patterns in walking.
News Physiol Sci 14: 168 –174, 1999.
185. LACQUANITI F, GUIGON E, BIANCHI L, FERRAINA S, AND CAMINITI R.
Representing spatial information for limb movement: role of area 5
in the monkey. Cereb Cortex 5: 391– 409, 1995.
186. LACQUANITI F, LE TAILLANTER M, LOPIANO L, AND MAIOLI C. The control
of limb geometry in cat posture. J Physiol (Lond) 426: 177–192,
1990.
187. LACQUANITI F AND MAIOLI C. Independent control of limb position
and contact forces in cat posture. J Neurophysiol 72: 1476 –1495,
1994.
188. LACQUANITI F AND MAIOLI C. Coordinate transformations in the control of cat posture. J Neurophysiol 72: 1496 –1515, 1994.
189. LACQUANITI F AND SOECHTING JF. EMG responses to load perturbations of the upper limb: effect of dynamic coupling between shoulder and elbow motion. Exp Brain Res 61: 482– 496, 1986.
190. LAPORTE Y AND LUNDBERG A. Functional organization of the dorsal
spino-cerebellar tract in the cat. III. Single fibre recording in Flechsig’s fasciculus on adequate stimulation of primary afferent neurones. Acta Physiol Scand 36: 204 –218, 1956.
191. LAPORTE Y, LUNDBERG A, AND OSCARSSON O. Functional organization
of the dorsal spino-cerebellar tract in the cat. II. Single fibre recording in Flechsig’s fasciculus on electrical stimulation of various
peripheral nerves. Acta Physiol Scand 36: 188 –203, 1956.
192. LIDDELL EGT AND SHERRINGTON CS. Reflexes in response to
stretch (myotatic reflexes). Proc R Soc Lond B Biol Sci 96:
212–242, 1924.
193. LIDDELL EGT AND SHERRINGTON CS. Further observations on myotatic reflexes. Proc R Soc Lond B Biol Sci 97: 267–283, 1925.
194. LIU CN. Afferent nerves to Clarke’s and the lateral cuneate nuclei in
the cat. Arch Neurol Psychiatry 75: 67–77, 1956.
195. LLOYD DPC AND MCINTYRE AK. Dorsal column conduction of group
I muscle afferent impulses and their relay through Clarke’s column.
J Neurophysiol 13: 39 –54, 1950.
196. LOEB GE, HOFFER JA, AND PRATT CA. Activity of spindle afferents
from cat anterior thigh muscles. I. Identification and patterns during normal locomotion. J Neurophysiol 54: 549 –564, 1985.
197. LOMELI J, QUEVEDO J, LINARES P, AND RUDOMIN P. Local control of
information flow in segmental and ascending collaterals of single
afferents. Nature 39: 600 – 604, 1998.
198. LUNDBERG A. Function of the ventral spinocerebellar tract. A new
hypothesis. Exp Brain Res 12: 317–330, 1971.
199. LUNDBERG A. Multisensory control of spinal reflex pathways. Prog
Brain Res 50: 11–28, 1979.
200. LUNDBERG A AND OSCARSSON O. Functional organization of the dorsal
spino-cerebellar tract in the cat. IV. Synaptic connections of afferents from Golgi tendon organs and muscle spindles. Acta Physiol
Scand 38: 53–75, 1956.
201. LUNDBERG A AND OSCARSSON O. Functional organization of the dorsal
spino-cerebellar tract in the cat. VII. Identification of units by
activation from cerebellar cortex with recognition of five functional subdivisions. Acta Physiol Scand 50: 356 –374, 1960.
202. LUNDBERG A AND WEIGHT F. Functional organization of connexions
to the ventral spinocerebellar tract. Exp Brain Res 12: 295–316,
1971.
203. LUNDBERG A AND WINSBURY G. Functional organization of the dorsal
spinocerebellar tract. VI. Further experiments on excitation from
tendon organ and muscle spindle afferents. Acta Physiol Scand 49:
165–170, 1960.
204. MAGNUS R. Studies in physiology of posture. Lancet 211: 531–536,
1926.
205. MANN MD. Axons of dorsal spinocerebellar tract which respond to
activity in cutaneous receptors. J Neurophysiol 34: 1035–1050,
1971.
565
566
229.
230.
231.
232.
233.
235.
236.
237.
238.
239.
240.
241.
242.
243.
244.
245.
246.
247.
248.
249.
250.
251.
252.
errors of direction and extent of reaching movements. Exp Brain
Res 115: 469 – 478, 1997.
MIALL MC AND WOLPERT DM. Forward models for physiological
motor control. Neural Networks 9: 1265–1279, 1996.
MISIASZEK JE, BARCLAY JK, AND BROOKE JD. Inhibition of canine H
reflexes during locomotor-like rotation about the knee arises from
muscle mechanoreceptors in quadriceps. J Neurophysiol 73: 2499 –
2506, 1995.
MISIASZEK JE, BROOKE JD, LAFFERTY KB, CHENG J, AND STAINES WR.
Long-lasting inhibition of the human soleus H reflex pathway after
passive movement. Brain Res 677: 69 – 81, 1995.
MISIASZEK JE AND PEARSON KG. Stretch of quadriceps inhibits the
soleus H reflex during locomotion in decerebrate cats. J Neurophysiol 78: 2975–2984, 1997.
MORAN DW AND SCHWARTZ AB. Motor cortical representation of
speed and direction during reaching. J Neurophysiol 82: 2676 –
2692, 1999.
MOSCHOVAKIS AK, SOLODKIN M, AND BURKE RE. Anatomical and physiological study of interneurons in an oligosynaptic cutaneous reflex
pathway in the cat hindlimb. Brain Res 586: 311–318, 1992.
MOUNTCASTLE VB. Modality and topographic properties of single
neurons of cat’s somatic sensory cortex. J Neurophysiol 20: 408 –
434, 1957.
MOUNTCASTLE VB AND DARIAN-SMITH I. Neuronal mechanisms in somesthesia. In: Medical Physiology (12th ed.), edited by Mountcastle
VB. St. Louis, MO: Mosby, 1968, p. 1372–1423.
MULLER RU, BOSTOCK E, TAUBE JS, AND KUBIE JL. On the directional
firing properties of hippocampal place cells. J Neurosci 14: 7235–
7251, 1994.
MULLER RU, RANCK JB JR, AND TAUBE JS. Head direction cells:
properties and functional significance. Curr Opin Neurobiol 6:
196 –206, 1996.
NELSON PG AND MENDELL LM. Projection of single knee flexor Ia
fibers to homonymous and heteronymous motoneurons. J Neurophysiol 41: 778 –787, 1978.
NELSON RJ, SUR M, FELLEMAN DJ, AND KAAS JH. Representations of
the body surface in postcentral parietal cortex of Macaca fascicularis. J Comp Neurol 192: 611– 643, 1980.
NEWTON RA. Joint receptor contributions to reflexive and kinesthetic responses. Phys Ther 62: 22–29, 1982.
NICHOLS TR. The organization of heterogenic reflexes among muscles crossing the ankle joint in the decerebrate cat. J Physiol
(Lond) 410: 463– 477, 1989.
NICHOLS TR. A biomechanical perspective on spinal mechanisms of
coordinated muscular action: an architecture principle. Acta Anat
(Basel) 151: 1–13, 1994.
NICHOLS TR. Receptor mechanisms underlying heterogenic reflexes
among the triceps surae muscles of the cat. J Neurophysiol 81:
467– 478, 1999.
NICHOLS TR, LAWRENCE JH III, AND BONASERA SJ. Control of torque
direction by spinal pathways at the cat ankle joint. Exp Brain Res
97: 366 –371, 1993.
NICOLELIS MA, GHAZANFAR AA, STAMBAUGH CR, OLIVEIRA LM, LAUBACH
M, CHAPIN JK, NELSON RJ, AND KAAS JH. Simultaneous encoding of
tactile information by three primate cortical areas. Nature Neurosci 1: 621– 630, 1998.
NYBERG G AND BLOMQVIST A. The termination of forelimb nerves in
the feline cuneate nucleus demonstrated by the transganglionic
transport method. Brain Res 248: 209 –322, 1982.
OSBORN CE AND POPPELE RE. Cross-correlation analysis of the response of units in the dorsal spinocerebellar tract (DSCT) to muscle stretch and contraction. Brain Res 280: 339 –342, 1983.
OSBORN CE AND POPPELE RE. A method for determining the primary
effect of a stimulus in tonically firing neurons using spike train
analysis. J Neurosci Methods 24: 125–130, 1988.
OSBORN CE AND POPPELE RE. The extent of polysynaptic responses
in the dorsal spinocerebellar tract to stimulation of group I afferent
fibers in gastrocnemius-soleus. J Neurosci 8: 316 –319, 1988.
OSBORN CE AND POPPELE RE. Components of the responses of a
population of DSCT neurons determined from single-unit recordings. J Neurophysiol 61: 447– 455, 1989.
OSBORN CE AND POPPELE RE. Components of responses of a popu-
253.
254.
255.
256.
257.
258.
259.
260.
261.
262.
263.
264.
265.
266.
267.
268.
269.
270.
271.
272.
273.
274.
275.
276.
277.
278.
Volume 81
lation of DSCT neurons to muscle stretch and contraction. J Neurophysiol 61: 456 – 465, 1989.
OSBORN CE AND POPPELE RE. Parallel distributed network characteristics of the DSCT. J Neurophysiol 68: 1100 –1112, 1992.
OSBORN CE AND POPPELE RE. Sensory integration by the dorsal
spinocerebellar tract circuitry. Neuroscience 54: 945–956, 1993.
OSCARSSON O. Primary afferent collaterals and spinal relays of the
dorsal and ventral spino-cerebellar tracts. Acta Physiol Scand 40:
222–231, 1957.
OSCARSSON O. Functional organization of the ventral spinocerebellar tract in the cat. II. Connections with muscle, joint, and skin
nerve afferents and effects on adequate stimulation of various
receptors. Acta Physiol Scand Suppl 146: 5–107, 1957.
OSCARSSON O. Functional organization of the spino- and cuneocerebellar tracts. Physiol Rev 45: 495–522, 1965.
OSCARSSON O AND UDDENBERG N. Identification of a spinocerebellar
tract activated from forelimb afferents in the cat. Acta Physiol
Scand 62: 125–136, 1964.
PASTOR AM, DE LA CRUZ RR, AND BAKER R. Cerebellar role in adaptation of the goldfish vestibuloocular reflex J. Neurophysiol 72:
1383–1394, 1994.
PASTOR AM, DE LA CRUZ RR, AND BAKER R. Characterization of
Purkinje cells in the goldfish cerebellum during eye movement and
adaptive modification of the vestibulo-ocular reflex. Prog Brain
Res 114: 359 –381, 1997.
PEARSON KG. Reflex reversal in the walking systems of mammals
and arthropods. In: Neural Control of Movement, edited by Ferrel
WR and Proske U. London: Plenum, 1995, p. 135–171.
PEARSON KG. Proprioceptive regulation of locomotion. Curr Opin
Neurobiol 5: 786 –791, 1995.
PEARSON KG AND COLLINS DF. Reversal of the influence of group Ib
afferents from plantaris on activity in medial gastrocnemius muscle
during locomotor activity. J Neurophysiol 70: 1009 –1017, 1993.
PEARSON KG, RAMIREZ JM, AND JIANG W. Entrainment of the locomotor rhythm by group Ib afferents from ankle extensor muscles in
spinal cats. Exp Brain Res 90: 557–566, 1992.
PENFIELD W AND BOLDREY E. Somatic motor and sensory representation in the cerebral cortex. Brain 60: 389 – 443, 1937.
PERREAULT MC, ANGEL MJ, GUERTIN P, AND MCCREA DA. Effects of
stimulation of hindlimb flexor group II afferents during fictive
locomotion in the cat. J Physiol (Lond) 487: 211–220, 1995.
PETERSON BW AND BAKER JF. Spatial transformations in vestibular
reflex systems. In: Motor Control: Concepts and Issues. Dahlem
Konferenzen, edited by Humphrey DR and Freund HJ. New York:
Wiley, 1991, p. 121–136.
POLIT A AND BIZZI E. Processes controlling arm movements in
monkeys. Science 201: 1235–1237, 1978.
POLIT A AND BIZZI E. Characteristics of motor programs underlying
arm movements in monkeys. J Neurophysiol 42: 183–194, 1979.
PONS TP, GARRAGHTY PE, CUSICK CG, AND KAAS JH. The somatotopic
organization of area 2 in macaque monkeys. J Comp Neurol 241:
445– 466, 1985.
PONS TP, GARRAGHTY PE, AND MISHKIN M. Serial and parallel processing of tactual information in somatosensory cortex of rhesus
monkeys. J Neurophysiol 68: 518 –527, 1992.
POPPELE RE AND QUICK DC. Effect of intrafusal muscle mechanics
on mammalian muscle spindle sensitivity. J Neurosci 5: 1881–1885,
1985.
PROCHAZKA A. Proprioceptive feedback and movement regulation.
In: Handbook of Physiology. Exercise: Regulation and Integration
of Multiple Systems. Bethesda, MD: Am. Physiol. Soc., 1996, sect.
12, p. 89 –127.
PROCHAZKA A. The fuzzy logic of visuomotor control. Can J Physiol
Pharmacol 74: 456 – 462, 1996.
PROCHAZKA A, GILLARD D, AND BENNETT DJ. Positive force feedback
control of muscles. J Neurophysiol 77: 3226 –3236, 1997.
PROCHAZKA A, GILLARD D, AND BENNETT DJ. Implications of positive
feedback in the control of movement. J Neurophysiol 77: 3237–
3251, 1997.
PROCHAZKA A AND GORASSINI M. Models of ensemble firing of muscle
spindle afferents recorded during normal locomotion in cats.
J Physiol (Lond) 507: 277–291, 1998.
PROCHAZKA A AND GORASSINI M. Ensemble firing of muscle afferents
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
234.
G. BOSCO AND R. E. POPPELE
April 2001
279.
280.
281.
282.
283.
284.
285.
287.
288.
289.
290.
291.
292.
293.
294.
295.
296.
297.
298.
299.
300.
301.
302.
303.
304.
recorded during normal locomotion in cats. J Physiol (Lond) 507:
293–304, 1998.
PROCHAZKA A AND HULLIGER M. Muscle afferent function and its
significance for motor control mechanisms during voluntary movements in cat, monkey, and man. Adv Neurol 39: 93–132, 1983.
PROSKE U, MORGAN DL, AND GREGORY JE. Muscle history dependence
of responses to stretch of primary and secondary endings of cat
soleus muscle spindles. J Physiol (Lond) 445: 81–95, 1992.
PRUD’HOMME MJ AND KALASKA JF. Proprioceptive activity in primate
primary somatosensory cortex during active arm reaching movements. J Neurophysiol 72: 2280 –2301, 1994.
RETHELY M. The Golgi architecture of Clarke’s column. Acta Morphol Hung 16: 311–330, 1955.
ROMO R AND SALINAS E. Sensing and deciding in the somatosensory
system. Curr Opin Neurobiol 9: 487– 493, 1999.
ROSENBAUM DA. Human movement initiation: specification of arm,
direction, and extent. J Exp Psychol 109: 444 – 474, 1980.
ROSSIGNOL S, LUND JP, AND DREW T. The role of sensory inputs in
regulating pattern of rhythmical movements in higher vertebrates.
In: Neural Control of Rhythmic Movements in Vertebrates, edited
by Cohen A, Rossignol S, and Grillner S. New York: Wiley, 1988, p.
201–283.
SAINBURG RL, GHEZ C, AND KALAKANIS D. Intersegmental dynamics
are controlled by sequential anticipatory, error correction, and
postural mechanisms. J Neurophysiol 81: 1045–1056, 1999.
SAINBURG RL, GHILARDI MF, POIZNER H, AND GHEZ C. Control of limb
dynamics in normal subjects and patients without proprioception.
J Neurophysiol 73: 820 – 835, 1995.
SALINAS E AND ABBOTT LF. Invariant visual responses from attentional gain fields. J Neurophysiol 77: 3267–3272, 1997.
SCHAIBLE HG, SCHMIDT RF, AND WILLIS WD. Convergent inputs from
articular, cutaneous and muscle receptors onto ascending tract
cells in the cat spinal cord. Exp Brain Res 66: 479 – 488, 1987.
SCHOMBURG ED. Spinal sensorimotor systems and their supraspinal
control. Neurosci Res 7: 265–340, 1990.
SCHOMBURG ED, PETERSEN N, BARAJON I, AND HULTBORN H. Flexor
reflex afferents reset the step cycle during fictive locomotion in the
cat. Exp Brain Res 122: 339 –350, 1998.
SCHWARTZ AB. Motor cortical activity during drawing movements:
single-unit activity during sinusoid tracing. J Neurophysiol 68:
528 –541, 1992.
SCHWARTZ AB. Distributed motor processing in cerebral cortex.
Curr Opin Neurobiol 4: 840 – 846, 1994.
SCHWEIGHOFER N, ARBIB MA, AND KAWATO M. Role of the cerebellum
in reaching movements in humans. I. Distributed inverse dynamics
control. Eur J Neurosci 10: 86 –94, 1998.
SCOTT JG AND MENDELL LM. Individual EPSPs produced by single
triceps surae Ia afferent fibers in homonymous and heteronymous
motoneurons. J Neurophysiol 39: 679 – 692, 1976.
SCOTT SH AND LOEB GE. The computation of position sense from
spindles in mono- and multiarticular muscles. J Neurosci 14: 7529 –
7540, 1994.
SHADMEHR R AND MUSSA-IVALDI F. Adaptive representation of dynamics during learning of a motor task. J Neurosci 14: 3208 –3224, 1994.
SHEN L AND POPPELE RE. Kinematic analysis of cat hindlimb stepping. J Neurophysiol 74: 2266 –2280, 1995.
SHERRINGTON CS. The Integrative Action of the Nervous System.
New Haven, CT: Yale Univ. Press, 1906.
SIEGEL SG, NICHOLS TR, AND COPE TC. Reflex activation patterns in
relation to multidirectional ankle torque in decerebrate cats. Motor
Control 3: 135–150, 1999.
SIMPSON JI, LEONARD CS, AND SOODAK RE. The accessory optic system of rabbit. II. Spatial organization of direction selectivity. J Neurophysiol 60: 2055–2072, 1988.
SNYDER LH, GRIEVE KL, BROTCHIE P, AND ANDERSEN RA. Separate
body- and world-referenced representations of visual space in parietal cortex. Nature 394: 887– 891, 1998.
SOECHTING JF AND FLANDERS M. Sensorimotor representations for
pointing to targets in three-dimensional space. J Neurophysiol 62:
582–594, 1989.
SOECHTING JF AND FLANDERS M. Errors in pointing are due to approximations in sensorimotor transformations. J Neurophysiol 62:
595– 608, 1989.
567
305. SOECHTING JF AND FLANDERS M. Moving in three-dimensional space:
frames of reference, vectors, and coordinate systems. Annu Rev
Neurosci 15: 167–191, 1992.
306. STEIN BE AND MEREDITH MA. Multisensory integration. Neural and
behavioral solutions for dealing with stimuli from different sensory
modalities. Ann NY Acad Sci 608: 51–70, 1990.
307. STEIN PS, MCCULLOUGH ML, AND CURRIE SN. Spinal motor patterns in
the turtle. Ann NY Acad Sci 860: 142–154, 1998.
308. STEIN PS, VICTOR JC, FIELD EC, AND CURRIE SN. Bilateral control of
hindlimb scratching in the spinal turtle: contralateral spinal circuitry contributes to the normal ipsilateral motor pattern of fictive
rostral scratching. J Neurosci 15: 4343– 4355, 1995.
309. STEIN RB AND CAPADAY C. The modulation of human reflexes during
functional motor tasks. Trends Neurosci 11: 328 –332, 1988.
310. STRICANNE B, ANDERSEN RA, AND MAZZONI P. Eye-centered, headcentered, and intermediate coding of remembered sound locations
in area LIP. J Neurophysiol 76: 2071–2076, 1996.
311. SURMEIER DJ AND TOWE AL. Properties of proprioceptive neurons
in the cuneate nucleus of the cat. J Neurophysiol 57: 938 –961,
1987.
312. SUZUKI I, PARK BR, AND WILSON VJ. Directional sensitivity of, and
neck afferent input to, cervical and lumbar interneurons modulated
by neck rotation. Brain Res 367: 356 –359, 1986.
313. SUZUKI I, TIMERICK SJ, AND WILSON VJ. Body position with respect to
the head or body position in space is coded by lumbar interneurons. J Neurophysiol 54: 123–133, 1985.
314. SZENTAGOTHAI J. Somatotopic arrangement of synapses of primary
sensory neurons in Clarke’s column. Acta Morphol Hung 10: 307–
310, 1961.
315. SZENTAGOTHAI J AND ALBERT A. The synaptology of Clarke’s column.
Acta Morphol Hung 5: 43–51, 1955.
316. TAUBE JS. Head direction cells and the neurophysiological basis for
a sense of direction. Prog Neurobiol 55: 225–256, 1998.
317. THOROUGHMAN KA AND SHADMEHR R. Electromyographic correlates
of learning an internal model of reaching movements. J Neurosci
19: 8573– 8588, 1999.
318. TILLERY SI, SOECHTING JF, AND EBNER TJ. Somatosensory cortical
activity in relation to arm posture: nonuniform spatial tuning.
J Neurophysiol 76: 2423–2438, 1996.
319. TOPKA H, KONCZAK J, AND DICHGANS J. Coordination of multi-joint
arm movements in cerebellar ataxia: analysis of hand and angular
kinematics. Exp Brain Res 119: 483– 492, 1998.
320. TOPKA H, KONCZAK J, SCHNEIDER K, BOOSE A, AND DICHGANS J. Multijoint arm movements in cerebellar ataxia: abnormal control of
movement dynamics. Exp Brain Res 119: 493–503, 1998.
321. TRACEY DJ. The projection of joint receptors to the cuneate nucleus
in the cat. J Physiol (Lond) 305: 433– 449, 1980.
322. TURNER RS AND ANDERSON ME. Pallidal discharge related to the
kinematics of reaching movements in two dimensions. J Neurophysiol 77: 1051–1074, 1997.
323. VAN OPSTAL AJ, HEPP K, SUZUKI Y, AND HENN V. Influence of eye
position on activity in monkey superior colliculus. J Neurophysiol
74: 1593–1610, 1995.
324. WALMSLEY B AND NICOL MJ. Location and morphology of dorsal
spinocerebellar tract neurons that receive monosynaptic afferent
input from ankle extensor muscles in cat hindlimb. J Neurophysiol
63: 286 –293, 1990.
325. WEI JY, KRIPKE BR, AND BURGESS PR. Classification of muscle spindle receptors. Brain Res 370: 119 –126, 1986.
326. WEI JY, SIMON J, RANDIC M, AND BURGESS PR. Ascending spinal axons
that signal the position of the hindlimbs under static conditions:
location and receptor input. Exp Brain Res 54: 7–22, 1984.
327. WEI JY, SIMON J, RANDIC M, AND BURGESS PR. Joint angle signaling by
muscle spindle receptors. Brain Res 370: 108 –118, 1986.
328. WEINBERG RJ. Are topographic maps fundamental to sensory processing? Brain Res Bull 44: 113–116, 1997.
329. WHELAN PJ, HIEBERT GW, AND PEARSON KG. Stimulation of the group
I extensor afferents prolongs the stance phase in walking cats. Exp
Brain Res 103: 20 –30, 1995.
330. WHELAN PJ AND PEARSON KG. Comparison of the effects of stimulating extensor group I afferents on cycle period during walk-
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017
286.
SPINAL PROPRIOCEPTION
568
331.
332.
333.
334.
G. BOSCO AND R. E. POPPELE
ing in conscious and decerebrate cats. Exp Brain Res 117:
444 – 452, 1997.
WILLIAMS WJ. A systems-oriented evaluation of the role of joint
receptors and other afferents in position and motion sense. Crit
Rev Biomed Eng 7: 23–77, 1981.
WILSON VJ, YAMAGATA Y, YATES BJ, SCHOR RH, AND NONAKA S. Response of vestibular neurons to head rotations in vertical planes.
III. Response of vestibulocollic neurons to vestibular and neck
stimulation. J Neurophysiol 64: 1695–1703, 1990.
WOLPERT DM, GHAHRAMANI Z, AND JORDAN MI. An internal model for
sensorimotor integration. Science 269: 1880 –1882, 1995.
YAMAMOTO S AND MIYAJIMA M. Activation of spinocerebellar neurons
by adequate exteroceptive and proprioceptive stimulation. Exp
Neurol 1: 427– 440, 1959.
Volume 81
335. ZAJAC FE. Muscle coordination of movement: a perspective. J Biomech 26: 109 –124, 1993.
336. ZEHR EP AND STEIN RB. What functions do reflexes serve during
human locomotion? Prog Neurobiol 58: 185–205, 1999.
337. ZHANG HQ, MURRAY GM, TURMAN AB, MACKIE PD, COLEMAN GT, AND
ROWE MJ. Parallel processing in cerebral cortex of the marmoset
monkey: effect of reversible SI inactivation on tactile responses in
SII. J. Neurophysiol 76: 3633–3655, 1996.
338. ZIPSER D AND ANDERSEN RA. A back-propagation programmed network that simulates response properties of a subset of posterior
parietal neurons. Nature 331: 679 – 684, 1988.
339. ZYTNICKI D, LAFLEUR J, KOUCHTIR N, AND PERRIER JF. Heterogeneity of
contraction-induced effects in neurons of the cat dorsal spinocerebellar tract. J Physiol (Lond) 487: 761–772, 1995.
Downloaded from http://physrev.physiology.org/ by 10.220.33.6 on July 4, 2017