J Neurophysiol 100: 76 –91, 2008.
First published March 26, 2008; doi:10.1152/jn.01109.2007.
Auditory Cortex Phase Locking to Amplitude-Modulated Cochlear Implant
Pulse Trains
John C. Middlebrooks
Kresge Hearing Research Institute, Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, Michigan
Submitted 5 October 2007; accepted in final form 21 March 2008
The cochlear implant is a highly successful neural prosthesis
that can restore hearing to severely and profoundly deaf people. Most present-day implant users receive stimulation in the
form of amplitude-modulated electrical pulse trains (Wilson
et al. 1991; Zeng et al. 2004). In such a stimulation strategy, the
sound waveform is processed by a bank of band-pass filters.
The envelope of each filter output is used to amplitude modulate a constant-rate pulse train, which is delivered to a particular electrode in an implanted electrode array. Spectral
information about sound is transmitted by the band-pass filter
operation leading to activation near the tonotopically appropriate cochlear place. Information about the temporal envelope of
sounds is transmitted by the modulation of the pulse trains. If
envelope information is to be used for speech recognition, that
information must in some way be transmitted to the auditory
cortex. This study examined the transmission of envelope
information from a cochlear implant to the auditory cortex in
an animal model.
Envelope frequencies useful for speech recognition in both
normal-hearing listeners and cochlear implant users range from
⬃2 to 16 Hz or higher (Drullman et al. 1994; Fu and Shannon
2000; Rosen 1992; Shannon et al. 1995; Van Tasell et al. 1987;
Xu and Zheng 2007; Xu et al. 2005); estimates of the upper
value of that range varies among studies, extending as high as
50 Hz in the scheme proposed by Rosen (1992). Envelopes
carry cues pertinent to manner of articulation, voicing, vowel
identity, and prosody (reviewed by Rosen 1992). Envelope
sensitivity in cochlear implant users is qualitatively similar to
that of normal-hearing listeners (Busby et al. 1993; Cazals
et al. 1994; Richardson et al. 1998; Shannon 1992). Psychophysical thresholds for detection of electrical pulse-train modulation are as low as ⫺40 dB (1% modulation) for modulation
frequencies ⱕ100 Hz. Critical features of speech waveforms,
after envelope extraction and logarithmic compression, appear
at the output of a cochlear implant speech processor with
modulation depths as low as ⫺30 to ⫺14 dB (3–10% modulation; Geurts and Wouters 2001; Snyder et al. 2000; Wilson
et al. 1991).
Neurons throughout the normal auditory system can phase lock
(i.e., fire in synchrony) with amplitude-modulated sounds (reviewed by Joris et al. 2004; Langner 1992). The upper frequency
cut-off for neural phase locking decreases at successively higher
levels of the auditory pathway. In the guinea pig, neurons phase
lock to modulator frequencies of nearly 1,000 Hz in the dorsal
cochlear nucleus (Zhao and Liang 1997), ⬎200 Hz in the inferior
colliculus (Rees and Palmer 1989), and up to ⬃200 Hz in the
medial geniculate body (MGB) (Creutzfeldt et al. 1980). In the
auditory cortex, most reports of phase locking to click trains or to
amplitude modulated sounds have indicated that driven spike rates
peak at “best modulation frequencies” around 8 –10 Hz and that
little phase locking to modulated sounds is observed for modulation frequencies above ⬃20 Hz (Creutzfeldt et al. 1980; Eggermont 1991, 1994, 1998, 2002; Gaese and Ostwald 1995; Schreiner and Urbas 1988).
The observations of limited cortical phase locking in most
studies prompt the question of how temporal envelope frequencies relevant to speech, roughly 2 to as high as 50 Hz, are
represented in the auditory cortex. One possibility is that
temporal information is recoded at some level of the auditory
pathway in a form not requiring phase locking. Another possibility is that temporal information is represented explicitly by
Address for reprint requests and other correspondence: J. C. Middlebrooks,
Department of Otolaryngology, University of California, Irvine, CA 926975310 (E-mail: [email protected]).
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked “advertisement”
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
INTRODUCTION
76
0022-3077/08 $8.00 Copyright © 2008 The American Physiological Society
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Middlebrooks JC. Auditory cortex phase locking to amplitude-modulated cochlear implant pulse trains. J Neurophysiol 100: 76 –91, 2008.
First published March 26, 2008; doi:10.1152/jn.01109.2007. Cochlear
implant speech processors transmit temporal features of sound as
amplitude modulation of constant-rate electrical pulse trains. This
study evaluated the central representation of amplitude modulation in
the form of phase-locked firing of neurons in the auditory cortex.
Anesthetized pigmented guinea pigs were implanted with cochlear
electrode arrays. Stimuli were 254 pulse/s (pps) trains of biphasic
electrical pulses, sinusoidally modulated with frequencies of 10 – 64
Hz and modulation depths of ⫺40 to ⫺5 dB re 100% (i.e., 1–56.2%
modulation). Single- and multiunit activity was recorded from multisite silicon-substrate probes. The maximum frequency for significant
phase locking (limiting modulation frequency) was ⱖ60 Hz for 42%
of recording sites, whereas phase locking to pulses of unmodulated
pulse trains rarely exceeded 30 pps. The strength of phase locking to
frequencies ⱖ40 Hz often varied nonmonotonically with modulation
depth, commonly peaking at modulation depths around ⫺15 to ⫺10
dB. Cortical phase locking coded modulation frequency reliably,
whereas a putative rate code for frequency was confounded by rate
changes with modulation depth. Group delay computed from the slope
of mean phase versus modulation frequency tended to increase with
decreasing limiting modulation frequency. Neurons in cortical extragranular layers had lower limiting modulation frequencies than did
neurons in thalamic afferent layers. Those observations suggest that
the low-pass characteristic of cortical phase locking results from
intracortical filtering mechanisms. The results show that cortical
neurons can phase lock to modulated electrical pulse trains across the
range of modulation frequencies and depths presented by cochlear
implant speech processors.
CORTICAL PHASE LOCKING TO COCHLEAR IMPLANT STIMULATION
77
were obtained from 20 pigmented guinea pigs of either sex, ranging
from ⬃500 to 1,000 g in weight. Animals were premedicated with
atropine sulfate (0.1 mg/kg, im) and were anesthetized with a mixture
of ketamine (40 mg/kg, im) and xylazine (10 mg/kg, im). Additional
doses of ketamine were given throughout the experiment to maintain
an areflexive state. Animal preparation typically lasted ⬃2 h, followed
by ⬃12–16 h of data collection. The animal’s right cochlea was
ablated with a carbide burr to eliminate auditory responses to the
animal’s breathing sounds and other acoustic input.
A micropositioner was used to insert a multisite cortical recording
probe (NeuroNexus Technologies, Ann Arbor, MI), one per animal,
into the right auditory cortex through a small hole in the dura. In each
experiment, preliminary mapping of characteristic frequencies identified cortical area A1 on the basis of the rostro-lateral to caudomedial gradient of low-to-high characteristic frequencies. In three
animals, the recording probe was oriented approximately parallel to
the cortical surface, ⬃0.5–1.0 mm deep, aligned with the tonotopic
characteristic-frequency gradient. In the remaining 17 animals, the
probe was oriented approximately perpendicular to the cortical surface, aligned with radial cell columns. Characteristic frequencies
measured from the radial penetrations were relatively constant at all
recording depths in each animal and ranged among animals from 6 to
32 kHz (mean, 14.6 kHz). That high-frequency range was chosen
because it corresponds to the tonotopic location of the cochlear
implants, which lay in the basal half of the basal turn of the cochlea.
The low-frequency region of area A1 was avoided intentionally.
Neurons in the low-frequency region can show phase locking to the
fine structure of acoustic tones in the range of 60 –250 Hz (Wallace
et al. 2002), but the low-frequency region of the cochlea is not
stimulated selectively by cochlear implant electrodes, either in this
animal model or, presumably, in humans. After the recording probe
was at its intended position relative to the tonotopic map, it was fixed
in place.
The left tympanic bulla was opened to expose the round window
and the basal turn of the cochlea. The left cochlea was deafened by
intrascalar infusion of neomycin sulfate, which is toxic to cochlear
hair cells (Nuttall et al. 1977). In our experience with other guinea
pigs (unpublished data), that procedure reliably results in a ⬎80-dB
elevation of thresholds for the cochlear compound action potential
within ⬍10 min.
METHODS
The cochlear stimulating electrode array was a six- or eightelectrode animal version of the Nucleus 22 banded electrode array
(Cochlear, Englewood, CO). Like the human version, it consisted of
platinum electrodes on a silicone-elastomer carrier. The electrodes
were 0.4-mm-diam bands, 0.3 mm in length, spaced 0.75 mm, centerto-center. The electrode array was implanted in the left scala tympani
through a small cochleostomy drilled with a diamond burr. The
electrode array was inserted as far as possible into the scala tympani,
occupying roughly the basal half of the basal turn of the cochlea. The
cochlear electrode array was fixed in place throughout each experiment.
Cochlear electrical stimuli were generated by a custom eightchannel optically isolated current source that was capacitatively coupled to the electrodes with an output time constant of 3 ms. The
current source was controlled by a multi-channel 16-bit D/A converter
(TDT model RV8 or RX8; output rate 48,828 samples/s). Stimuli
were presented in a monopolar configuration. The active electrode
was a single intrascalar electrode, and the return electrode was a wire
inserted in a neck muscle. Typically, the most apical electrodes had
the lowest electrode threshold for cortical activation (Bierer and
Middlebrooks 2002), so one of the two most apical electrodes was
used as the active electrode.
Electrical stimuli were modulated 600-ms trains of biphasic pulses,
initially cathodic, 41 ␮s/phase (Fig. 1). Pulse trains were presented at
Overview
Data collection consisted of extracellular spike recordings from the
auditory cortex of anesthetized guinea pigs in response to acoustical
and electrical cochlear stimulation presented to the ear contralateral to
the cortical recording site. Experiments were controlled by a Windows-based personal computer interfaced with System 3 hardware
from Tucker-Davis Technologies (TDT; Alachua, FL). Custom software was written as MATLAB scripts (The MathWorks, Natick, MA).
Experiments were conducted in a sound-attenuating chamber. Each
animal was studied first in a normal-hearing condition using acoustic
tones presented from a calibrated loudspeaker placed ⬃20 cm from the
animal’s left ear. Based on cortical responses to tones, the recording
probe was positioned in cortical area A1 in an area in which neurons
had mid- to high-frequency characteristic frequencies, and the probe
was fixed in place. The animal was deafened bilaterally, a stimulating
electrode array was implanted in the scala tympani of the left cochlea,
and cortical responses to electrical cochlear stimuli were studied.
Animal preparation
All experiments with animals were conducted with approval of the
University of Michigan Committee on Use and Care of Animals. Data
J Neurophysiol • VOL
Cochlear electrical stimulation
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phase locking in the cortex but that phase locking to frequencies above ⬃20 Hz has been missed in previous studies
because of some aspect of the stimulus or the analysis. Even if
phase-locking at frequencies up to ⬃50 Hz reaches thalamicrecipient neurons of the primary auditory cortex, it is not clear
whether high-frequency phase locking is maintained through
intracortical processing and reaches the neurons that project
out of area A1 to other cortical and subcortical targets. This
study focuses on amplitude-modulation frequencies in the
upper end of the range of envelope frequencies relevant to
speech recognition and contrasts cortical phase locking between granular (i.e., thalamic-recipient) and extragranular laminae of the cortex.
Another unknown is the sensitivity of cortical neurons to
stimuli presented at shallow modulation depths. With a few
exceptions (Liang et al. 2002), studies of cortical phase locking
to stimulus envelopes have used click trains or, in the case of
sinusoidally modulated tones, 100% modulation depths. It is
difficult to extrapolate from such studies to the range of
modulation depths that are available in the amplitude-compressed speech that is presented to a cochlear implant user. For
that reason, this study tests modulation depths as low as 1%,
corresponding to the lowest modulation depths that can be
detected by human subjects (Busby et al. 1993; Cazals et al.
1994; Galvin and Fu 2005; Pfingst et al. 2007; Richardson et al.
1998 Shannon 1992).
The results show robust phase locking in the auditory cortex
across the range of modulation frequencies and modulation
depths relevant to speech recognition by cochlear implant
users. This report quantifies the basic characteristics of cortical
phase locking, including differences among cortical laminae
and an unexpected nonmonotonic dependence on modulation
depth, and contrasts rate and temporal codes for modulation
frequency. Preliminary results have been presented in abstracts
(Middlebrooks 2005; Middlebrooks and Lee 2004). Modulation sensitivity as a function of stimulus parameters relevant to
speech processor design is quantified elsewhere (Middlebrooks
2008).
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J. C. MIDDLEBROOKS
A
40 µs
0
0.1
0.2
0.3
B
0.4
0
10
20
30
40
Time (ms)
FIG. 1. Stimulus waveforms. A: individual electrical pulses were biphasic,
initially cathodic, charge balanced, and 40 ␮s per phase. The trapezoidal
appearance of the waveform reflects the 48,828 samples/s output rate.
B: sinusoidally amplitude-modulated pulse trains had pulse rates of 254
pulses/s (pps). The example shows a modulation frequency of 42.4 Hz and
modulation depth of ⫺5 dB.
Cortical recording and spike sorting
The cortical recording probe consisted of a single silicon-substrate
shank, 15 ␮m thick and 100 ␮m wide, tapering to ⬃15 ␮m in width.
Along the shank were 16 iridium-plated recording sites, 177 or 413
␮m2 in area, centered at 100- or 150-␮m intervals. Neural waveforms
were recorded simultaneously from the 16 sites using a TDT RA16 or
RX5 system consisting of parallel headstages, amplifiers, 16-bit digitizers, and digital signal processors. Waveforms were digitized at a
sample rate of 24,414 samples/s, low-pass filtered, resampled at 12.2
kHz, and stored on computer disk. Neural spikes were detected
on-line for monitoring of the experiment, but all reported data analysis
used spikes that were isolated off-line from the stored neural waveforms.
Electrical artifact from the cochlear stimulus could be detected by
the cortical recording probe and potentially could have contaminated
neural spike detection. Such contamination was eliminated using a
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Data analysis
Stimulus base current levels were specified relative to neural
thresholds for electrical stimulation. Thresholds were based on a
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repetition periods of 1.2 s. The pulse trains had a carrier rate of
254.313 pulses/s (pps; written as 254 for convenience in presentation);
higher carrier rates were tested elsewhere (Middlebrooks 2008). The
carrier rate of 254 pps is close to the 250-pps rate that is used in the
SPEAK strategy that has been widely used in clinical cochlear implant
speech processors (Skinner et al. 1994). Although newer processors
can accommodate faster rates, rates of ⬃250 pps often are prescribed
by audiologists.
Base current levels generally were set to 2, 4, and 6 dB above that
the spike threshold for an unmodulated pulse train threshold. Currents
in modulated pulse trains ranged above and below the base current
level. Pulse trains were sinusoidally amplitude modulated by the
function 1 ⫹ msin(2␲fmt), where m is the modulation index (ranging
from 0 to 1) and fm is the modulation frequency (in Hz). Starting
modulator phases of 0, ␲/2, and ␲ radians were tested, and all gave
equivalent results. The modulation depth is represented in decibels as
20 log10(m). Modulation depths tested routinely ranged from ⫺40
(1% modulation) to ⫺5 dB (56.2% modulation) in 5-dB steps. Tested
modulation frequencies were as high as 63.58 Hz, which was 1/4 of
the carrier frequency. Earlier experiments tested modulation frequencies at even 10-Hz multiples (e.g., 10, 20, etc.) whereas later experiments tested multiples of 21.19 Hz, which was an integer fraction of
the 254-pps carrier pulse rate. No difference in phase locking was
observed between those conditions and, in summary statistics, the data
from 21.19-, 42.39-, and 63.58-Hz modulators were grouped with the
data from 20-, 40-, and 60-Hz modulators, respectively. In six animals, we also tested unmodulated pulse trains that varied parametrically in pulse rate.
Blocks of trials consisted of parametric variations of base current
level, modulator frequency, and modulator depth. The order of stimuli
was randomized such that every combination of parameters was tested
once, and every combination was tested again in a different random
order, and so on until each combination of stimuli had been repeated
20 times.
sample-and-hold function that was programmed into the recording
path. Immediately before the onset of each electrical pulse, the
function sampled the present values of the 16 neural waveforms and
held the outputs at those levels for 143.4 ␮s, which encompassed both
phases of the 40-␮s/phase biphasic pulse and a brief recovery time for
the amplifier. The sample and hold resulted in a loss of data during
⬃3.6% of each period of the 254-pps pulse train. Any residual artifact
resulting from the release of the sample-and-hold function typically
was eliminated by the denoising procedure described below. In a few
instances in which the artifact could not be eliminated entirely, or in
test cases in which the artifact-reject procedure was disabled intentionally, artifact propagated to the cortical recording site with a delay
of ⬍2 ms. The short latency of the artifact made it easy to distinguish
from cortical neural activity, which showed a latency of ⬎7 ms, as
described in RESULTS.
The following procedure was used to identify stimulus-evoked
spikes based on off-line analysis of the stored neural waveforms. First,
a denoising procedure was applied to the waveforms to attenuate
signals that were correlated among the 16 recording channels (Bierer
and Anderson 1999). Such signals to be attenuated included far-field
slow-wave potentials, artifact that remained from the sample-and-hold
function, and any other electrical interference. Second, the denoised
waveforms were interpolated to 48.8-kHz time resolution using Fourier interpolation. Third, a range of peak-to-trough (or trough-to-peak)
times was selected (typically 150 –350 ␮s), and the distribution of
peak-to-trough amplitudes of spikes fitting those time criteria was
displayed. Based on visual inspection of that distribution, a range of
amplitudes was selected that isolated a single unit or, more often,
included a small number of unresolved units. Finally, the entire set of
waveforms was processed to isolate spikes that fit specified ranges of
peak-to-trough times and amplitudes. The data set consisted of 61
well-isolated single units, characterized by discrete distributions of
peak-to-trough amplitudes, and 206 unresolved recordings of 2 or
more units. This report will use the terms “units” or “unit activity” to
indicate both single- and multiunit recordings from a single recording
site and “isolated single unit” to indicate the well-isolated single units.
A small number of units appeared as outliers in distributions of
first-spike latency and/or limiting pulse rates. Those units with latencies ⬍7 ms and/or phase locking to pulse rates ⬎80 pps were
interpreted to be from thalamocortical fibers and were eliminated from
further analysis.
Current source density analysis was used as a functional measure of
the depth of recording sites relative to cortical laminae (Müller-Preuss
and Mitzdorf 1984); this procedure was used only for the 17 animals
in which recording probes were aligned with radial cell columns.
Low-pass-filtered local field potentials elicited by single electrical
pulses or acoustic clicks were recorded simultaneously at each of the
16 recording sites (Fig. 2A). The current-source density is proportional to the second spatial derivative of the field potentials. In Fig.
2B, current sinks are shown as upward deflections, filled, and current
sources as downward deflections. A current sink consistently was
observed in the superficial depths, decreasing in latency over a few
hundred milliseconds of increasing depth. In eight animals, recording
sites were marked with electrolytic lesions, and postmortem tissue
was fixed by immersion in buffered aldehydes, cryostat sectioned, and
Nissl stained. Histological reconstruction showed that the deepest site
at which the short-latency current sink was present (Fig. 2, *)
coincided in depth with the transition from lamina III to IV, corresponding to the major depth of termination of thalamocortical fibers.
That physiologically defined depth was used as a reference for
examination of laminar specificity of response properties; i.e., it
appears as zero relative depth in Fig. 10.
CORTICAL PHASE LOCKING TO COCHLEAR IMPLANT STIMULATION
Rayleigh test of circular uniformity (Mardia 1972) at the level of P ⬍
0.001. The mean phase was given by the orientation of the resultant
vector. The mean phase lag tended to increase linearly with increasing
modulator frequency, as shown in RESULTS. The slope of the bestfitting phase-versus-frequency line gave the group delay, D ⫽ ⌬␾/
2␲⌬f, for mean phase lag (␾) in radians and modulator frequency (f)
in Hertz; group delay was computed only from data points showing
significant vector strength at at least two tested frequencies, one of
which was ⱖ20 Hz. Cortical units tended to respond to the onset of a
pulse train with a temporally compact burst of spikes, regardless of the
modulating waveform. That onset burst tended to produce an erroneous impression of high vector strength. For that reason, all computations of vector strength and mean phase were based only on spikes
occurring between 100 and 600 ms after the onset of the pulse train,
well after the onset burst.
Current−Source Density
Local Field Potentials
A
B
0
100
200
300
400
*
600
RESULTS
General characteristics of cortical phase locking
700
800
900
1000
1100
1200
1300
1400
1500
0
10
20
30
Time (ms)
0
10
20
30
Time (ms)
40
FIG. 2. Use of current-source density analysis to identify cortical depths.
A: local field potentials recorded at 16 sites at 100-␮m intervals along a single
silicon-substrate probe oriented parallel to radial cell columns. B: currentsource density traces derived from the 2nd spatial (i.e., depth) derivative of the
potentials in A. Current sinks are drawn upward and are filled. *Deepest site at
which the short-latency sink was present, which in histological reconstruction
tended to lie at the border of laminae III and IV.
signal-detection analysis (Green and Swets 1966; Macmillan and
Creelman 2005) of trial-by-trial spike counts measured in response to
brief pulse trains varied in current in 1-dB steps; the analysis is
detailed in Middlebrooks and Snyder (2007). Thresholds generally
were stable within ⬃1 dB throughout the durations of recording
sessions.
The strength of phase locking of cortical spikes to the modulator
waveform was represented by the vector strength (Goldberg and
Brown 1969). The vector strength and mean phase were obtained by
expressing spike times relative to the phase of the modulator, representing each spike as a unit vector with orientation given by that
phase, and computing the sum of the unit vectors. The vector strength
was given by the length of the resultant vector divided by the number
of vectors. It could range from 0 (no phase locking) to 1 (all spikes at
identical phase); spike probability exactly following the sine modulator waveform would yield a vector strength of 0.5. Modulation gain
(in dB) was computed as 20 log10 (2r/m), for response vector strength
r and stimulus modulation depth m (Rees and Palmer 1989). The
statistical significance of the vector strength was evaluated by the
J Neurophysiol • VOL
The phase-locking characteristics of one well-isolated single
unit are shown in Figs. 3, 4, 5, and 8A. Those figures all show
data from the same unit to permit comparison across various
data representations; those particular data were collected using
base current levels of ⫾282 ␮A, 1 dB above the spike threshold for an unmodulated pulse train. Like nearly all studied
units, this unit responded to the onset of an unmodulated
254-pps pulse train with a robust burst of spikes followed by
little or no tonic firing (Fig. 3, top panel). In contrast, modulated pulse trains tended to produce a burst of spikes at
stimulus onset, a ⬃50- to 100-ms period of reduced firing, and
periodic tonic firing lasting throughout the duration of the pulse
train. Figure 3 shows poststimulus time histograms for various
modulation frequencies and depths. At the 10- and 20-Hz
modulation frequencies, the periodic tonic cortical firing can be
seen at modulation depths at least down to ⫺25 dB, fading at
⫺35 dB. At higher modulation frequencies, the tonic firing by
this unit was greatest at intermediate modulation depths (i.e.,
⫺15 dB), decreasing at higher and lower modulation depths.
At the ⫺15-dB depth, one can see the frequency of the periodic
cortical firing increasing with modulation frequency ⱕ50 Hz.
For the 60-Hz modulator in this example, one can see a periodicity
in the response at ⬃14 Hz, which likely represents phase
locking to the difference frequency derived from the 254-Hz
pulse train and a multiple of the 60-Hz modulator, i.e., ⬃254
pps – (4 ⫻ 60 Hz) ⫽ ⬃14 Hz. That ⬃14-Hz periodicity was
not present in tests (in other guinea pigs) of responses to a
63.58-Hz modulator, which is an integer factor of the carrier
pulse rate.
The phase-locked periodic firing of units was evident in
period histograms, which show spike counts on a time scale
expressed as phase of the modulating sine function (Fig. 4).
The bottom row of panels also shows one period of the
stimulus envelope for each modulation depth. In nearly all of
the panels, the modulation gain was substantially greater than
unity in that the depth of modulation of spike probability was
greater than the depth of modulation of the stimulus waveform.
A stimulus modulation depth of ⫺25 dB, for instance, corresponds to only 5.6% modulation of the stimulus, whereas the
illustrated period histograms for those modulation depths show
nearly 100% modulation of the neural spike counts. In each
panel, an arrowhead identifies the mean phase of the response,
computed from the vector sum of spikes as described in
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Relative Depth (µm)
500
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80
J. C. MIDDLEBROOKS
unmod
−35 dB
−25 dB
−15 dB
−5 dB
10 Hz
FIG. 3. Poststimulus time histograms of responses of a well-isolated single unit to electrical
stimulation. Top left panel: response to an unmodulated 254-pps pulse train. All other panels show
responses to sinusoidally modulated pulse trains
presented at a base current level 1 dB above the
threshold for an onset response to an unmodulated
train. Panels are arranged by modulation frequency
(ordered by rows as indicated) and modulation
depths (ordered by columns). The solid bars at the
bottom indicate the 600-ms duration of the pulse
trains. Ticks on the vertical axis represent 0.5 spikes
per 3-ms time bin summed across 20 trials.
30 Hz
40 Hz
50 Hz
60 Hz
0
200 400 600 800 0
200 400 600 800 0
200 400 600 800 0
200 400 600 800
Time (ms re onset)
METHODS. The lag of the mean phase relative to the modulator
tended to increase with increasing modulator frequency, as
expected based on a constant delay in the neural pathway from
the ear to the cortex.
Figure 5 quantifies several characteristics of the phase locking of this representative unit as a function of modulation
frequency. The top row of panels shows the vector strength of
phase locking (solid lines and circles). Dotted lines show the
Rayleigh criterion for statistically significant phase locking at
the level of P ⬍ 0.001. This unit is typical of many units in that
the Rayleigh criterion was higher at the shallowest (⫺35 dB)
modulation depth than at greater depths because the spike rate
was reduced at the shallow depth. This unit showed significant
phase locking at all illustrated frequencies at depths ⫺25 and
⫺15 dB. At the greatest modulation depth (⫺5 dB), the vector
strength was greatest at 10 Hz and declined sharply with
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increasing modulation frequency. The decline in vector strength
with increasing frequency was much less prominent at modulation depths of ⫺25 and ⫺15 dB; the trend was inconsistent
at ⫺35 dB.
The distributions of vector strength across the sample population are shown in the top panels of Fig. 6; as noted in
METHODS, data collected for 21-, 42-, and 64-Hz modulators are
grouped with data for 20, 40, and 60 Hz. The box plots show
the distributions across all single- and multiunit recordings,
and the circles indicated the medians of the single-unit values.
Vector strengths varied widely among units, which can be
explained in part by the dependence of vector strength on depth
in the cortex, considered in a later section. The distributions
largely mirror the characteristics demonstrated by the single
unit represented in Fig. 5. Vector strength tended to decline
with increasing modulation frequency. Vector strength in-
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20 Hz
CORTICAL PHASE LOCKING TO COCHLEAR IMPLANT STIMULATION
−35 dB
−25 dB
−15 dB
81
−5 dB
10 Hz
20 Hz
40 Hz
50 Hz
60 Hz
0
π
2π 0
π
2π 0
π
2π 0
π
2π
Modulator Phase (radians)
creased monotonically with increasing modulation depth at the
low modulation frequency, whereas, at the higher modulation
frequencies, vector strength often declined at the greatest
modulation depth. These distributions include vector strengths
of all units, irrespective of whether the vector strength was
significant. The bottom row of panels in Fig. 6 shows the
percentage of units that showed significant phase locking
(Rayleigh test, P ⬍ 0.001) under various stimulus conditions.
At 20-Hz modulation, the percentage of phase-locked units
increased monotonically with increasing modulation depth. At
higher modulation frequencies, however, the percentage declined slightly at the greatest depths.
The second row of panels in Fig. 5 shows modulation gain
for the representative unit, which reflects the modulation of the
cortical response divided by the modulation of the stimulus
waveform (see METHODS). Gains were positive for nearly all
illustrated modulation depths and frequencies. A gain of 20 dB,
for example, indicates that the temporal modulation of spike
probability in the period histogram is 10 times greater than the
amplitude modulation of the stimulus. There is a steady decrease in modulation gain associated with increasing modulaJ Neurophysiol • VOL
tion depth, largely because the range of vector strengths (the
numerator in the gain computation) covers only a factor of ⬃2,
whereas the modulation depths (the denominator) range over a
factor of ⬃32. Comparable levels of modulation gain were
observed across the sampled population of 267 units. For
example, modulation gain across all units and all tested modulation frequencies averaged 4.2 ⫾ 4.1 dB at the ⫺5-dB
modulation depth and 32.4 ⫾ 3.5 dB at the ⫺35-dB depth.
The third row of panels in Fig. 5 shows the cumulative
mean phase lag of responses relative to the modulator phase.
The data tend to follow straight lines, particularly for
modulation depths of ⫺25 and ⫺15 dB, at which the vector
strength was most consistently high. The slopes of the best-fitting
phase-versus-frequency lines give the group delay between
the phase of the stimulus modulating waveform and the
phase of the period histograms. Slopes were computed by
linear regression of all the points that showed significant
vector strength; the group delay is indicated by the number
in each panel. Group delays typically were fairly constant
across modulation depths. In the example, delays ranged
from 18.0 to 18.8 ms except for the value of 15.2 ms for
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FIG. 4. Period histograms of the responses shown in Fig. 3. Spike times occurring 100 – 600 ms after stimulus onset are
binned according to modulator phase, ␲/15radian per bin. Arrowheads indicate mean
phase. Panels are arranged by modulation
frequency (rows) and modulation depth (columns). Ticks on the vertical axis represent 1
spike per bin, 30 bins per period, summed
across 20 trials. The bottom row of panels
shows 1 period of the stimulus envelope for
each modulation depth; the shown phase is
arbitrary.
30 Hz
82
J. C. MIDDLEBROOKS
−35 dB
−25 dB
−15 dB
−5 dB
Modulation
Gain (dB)
Vector
Strength
1.0
0.5
0.0
40
20
Cumulative Phase
Tonic Rate
(Radians)
(Spikes/s/Trial)
0
2π
π
15.2 ms
18.0 ms
18.8 ms
18.1 ms
0
40
20
0
0
20
40
60
0
20 40 60
0
20 40
Modulation Frequency (Hz)
depth ⫺35 dB, which probably reflects an imprecise group
delay measurement caused by the smaller number of significantly phase-locked points at that depth.
Across all tested units, group delays exhibited a significant
positive correlation with first-spike latencies measured from
responses to unmodulated waveforms (r ⫽ 0.36, P ⬍ 0.0001,
n ⫽ 198; r ⫽ 0.35, P ⬍ 0.05, n ⫽ 34 for single units), although
almost all the group delays were longer than the first-spike
latencies (Fig. 7A). Group delays tended to segregate according
to the highest modulation frequency at which units showed
significant phase locking (the limiting modulation frequency).
Units with limiting modulation frequencies ⱖ60 Hz tended to
show short group delays. Most of those 117 units had group
delays ⱕ20 ms [14.9 ⫾ 4.1 (SD) ms; n ⫽ 113; indicated by X
in Fig. 7A]. Conversely, units with limiting modulation frequencies ⬍60 Hz tended to show longer group delays (20.5 ⫾
6.0 ms, n ⫽ 85; indicated by O). The relationship between
limiting modulation frequency and group delay is plotted in
Fig. 7B. The correlation between those two measures was as
follows: r ⫽ ⫺0.66, P ⬍ 0.0001, n ⫽ 198 (r ⫽ ⫺0.60, P ⬍
0.0005, n ⫽ 34 for single units). Note that this analysis
understates the number of units with limiting modulating
frequencies ⱕ21 Hz because group delays could not be computed in cases in which the modulation frequencies were tested
in 20- or 21-Hz intervals and units failed to phase lock to
frequencies ⬎21 Hz. Including units for which group delay
could not be computed, 113 had limiting frequencies ⱖ60 Hz
and 154 had limiting frequencies ⬍60 Hz. There also was a
weak but significant negative correlation between limiting
modulation frequency and first-spike latency (r ⫽ ⫺0.45, P ⬍
0.0001, n ⫽ 267).
J Neurophysiol • VOL
60
0
20
40
60
The observation of physiologically reasonable group delays
confirms that the observed phase locking of cortical neurons
was not contaminated by artifact from the electrical stimulus.
In a few instances in which artifact could not be avoided,
and in test cases in which the artifact-reject system was
disabled intentionally, measured group delays were ⬍2 ms.
Those cases were excluded from further analysis.
The bottom row of panels in Fig. 5 shows mean tonic spike
rates of the example single unit. These spike rates exclude the
onset burst during the first 100 ms after stimulus onset, and
each of these plots includes a datum at 0 Hz, representing the
tonic response to the unmodulated pulse train. The frequency
dependence of spike rates of this unit tended to parallel the
frequency dependence of vector strength in that a sharp decline
in rate with increasing frequency was evident at the ⫺5 dB
modulation depth and was less pronounced at lower modulation depths. This unit was fairly typical in that its spike rates
decreased with decreases in modulation depth, even between
depths at which there was negligible change in vector strength
(i.e., between ⫺15 and ⫺25 dB).
At the greatest modulation depth tested, ⫺5 dB, most units
tended to show maximum spike rates and vector strengths at
modulation frequencies of 10 Hz; frequencies ⬍10 Hz were
not routinely tested. For instance, of 126 units tested with
modulation frequencies as low as 10 Hz, 71% showed maximum spike rates for the 10-Hz modulator, whereas only 10, 8,
and 12% of units showed maximum spikes elicited by modulation frequencies of 20, 40, and 60 Hz, respectively. Peak
responses to a 10-Hz modulator or 10-pps pulse rate likely
reflect an interaction of the extrinsic stimulus with the intrinsic
alpha rhythm of the cortex (Eggermont 1992; Gaese and
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FIG. 5. Phase-locking measures from the
responses shown in Figs. 3 and 4, plotted as
a function of modulation frequency. Panels
are arranged by modulation depth (columns).
All data are based on spikes occurring 100 –
600 ms after stimulus onset (i.e., onset response in the initial 100 ms is omitted). Plots
in the top 3 rows (vector strength, modulation gain, and cumulative phase) are based
on vector addition. Numbers in the 3rd row
indicate group delay, computed from the
slopes of the phase-vs.-frequency plots. The
4th row, tonic rate, plots the mean number of
spikes per trial in the 100- to 600-ms postonset time period. Modulation frequencies
of 0 (bottom row) indicate responses to unmodulated pulse trains.
CORTICAL PHASE LOCKING TO COCHLEAR IMPLANT STIMULATION
20−21 Hz
1
A
40−42 Hz
83
60−64 Hz
B
C
Vector Strength
0.8
0.4
0.2
% Phase−Locked Units
0
80
D
E
F
60
40
20
0
−35
−25
−15
−5 −35
−25
−15
−5 −35
−25
−15
−5
Modulation Depth (dB)
Ostwald 1995; Kenmochi and Eggermont 1997). Consistent
with that notion, neurons often showed a periodic discharge
following the offset of a 10-Hz stimulus. An example can
be seen in Fig. 3 in the 10-Hz, ⫺5-dB panel, in which two
discrete bursts of spikes at 100-ms intervals follow the offset of
the 600-ms stimulus. Among the neurons that were tested at 10
Hz, 18.5% showed significant continuing 10-Hz periodic spike
patterns (P ⬍ 0.001, Rayleigh test) in the interval 20 –200 ms
after the offset of a 10-Hz modulated pulse train, whereas no
units showed 20-, 40-, or 60-Hz periodicity after 20-, 40-, or
60-Hz stimuli. Most of the detailed analysis in this paper used
modulator frequencies of 20 Hz and higher, which tended not
to engage intrinsic cortical rhythms.
Nonmonotonic sensitivity to modulation depth
Phase locking to higher frequencies (e.g., 40 and 60 Hz)
often was weak at very low modulation depths, strengthened
with increasing modulation depth, and weakened again at the
greatest modulation depths. For example, vector-strength data
from the single unit shown in Figs. 3–5 are plotted in Fig. 8A
as a function of modulation depth. For this unit, strong phase
locking to 20 Hz persisted at the greatest modulation depths,
but vector strengths for phase locking to 40 and 60 Hz at a
depth of ⫺5 dB dropped to about one half or less of the
maximum value. The dependence of vector strengths on modulation depth was compared across units by computing a
“monotonicity index,” which was the vector strength at ⫺5-dB
modulation depth expressed as a percentage of the maximum
vector strength observed across all depths. Indices of 100%
indicate units that showed a monotonic increase in vector
strength with increasing modulation depth, and smaller indices
J Neurophysiol • VOL
indicate nonmonotonic depth dependence with phase locking
that declined at the maximum depth. The cumulative distribution of the monotonicity index across the sample population is
shown in Fig. 8B; the population at each frequency includes
only units showing significant phase locking at that frequency.
At a modulation frequency of 20 or 21 Hz, only 17% of the
population (26.1% of single units) had monotonicity indices
⬍75%, indicating that vector strengths of most units increased
essentially monotonically with increasing modulation depth.
At modulation frequencies of 40 – 64, however, ⬃60% of units
had monotonicity indices ⱕ75%, indicating a nonmonotonic
dependence on modulation depth. Figure 8C shows that the
greatest tested modulation depths, ⫺5 or ⫺10 dB, usually
produced the maximum vector strength for modulation depths
of 20 –21 Hz but that maximum vector strengths often were
produced at shallower depths at 40 – 64 Hz.
The results presented to this point have represented responses to a constant 254-pps pulse train modulated at various
frequencies and depths. We also evaluated phase-locked responses to unmodulated pulse trains that varied in pulse rate
from 10 to 60 pps. One could regard these relatively low-rate
pulse trains as showing modulation effectively deeper than that
produced by 100% modulation by a sine function. In six
animals (61 single- and multiunit sites), we compared cortical
phase locking to these unmodulated pulse trains with phase
locking to modulated 254-pps pulse trains. In both conditions,
we determined the maximum pulse rate or modulation frequency that produced significant cortical phase locking (Rayleigh test, P ⬍ 0.001); these are referred to as the limiting pulse
rate and limiting modulation frequency, respectively. Most
units phase locked to higher frequencies in response to the
modulated 254-pps pulse trains than to the unmodulated
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FIG. 6. A–C: distribution of vector strengths across
the unit sample. Panels indicate data from modulation
frequencies of 20 –21, 40 – 42, and 60 – 64 Hz, as indicated. Every vector strength is shown, regardless of
statistical significance. In each panel, each box and
whiskers represents the distribution of vector strengths
at 1 modulation depth: horizontal lines indicate 25th
percentile, median, and 75% percentile, whiskers indicate ranges of data within 1.5 times the interquartile
range, and plus signs indicate outlying points. Circles
indicated the medians values for isolated single units.
D–F: percentage of vector strengths at each modulation
depth that were significant according to a Rayleigh test
(P ⬍ 0.001). The bars represent the population of all
single- and multiunits (n ⫽ 267), and circles represent
isolated single units (n ⫽ 61).
0.6
84
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15
Laminar dependence of phase locking
Limiting Mod. Freq. < 60 Hz
Limiting Mod. Freq. ≥ 60 Hz
40
A
35
Group Delay (ms)
30
25
10
5
r = 0.36, N = 198,
0
0
2
4
p<.0001
6
8
10
12
First−Spike Latency (ms)
14
≥63.6
B
≥60
Limiting Modulation Frequency (Hz)
16
50
40
30
20
r = −0.66, N = 198,
10
0
5
10
15
20
25
Group Delay (ms)
30
p<.0001
35
40
FIG. 7. A: group delay vs. 1st-spike latency. Group delay was computed
from the slope of the best-fitting mean-phase-vs.-modulation-frequency line.
Group delay was computed only for single- or multiunit recordings for which
vector strengths were significant for ⱖ2 modulation frequencies, at least 1 of
which was ⱖ20 Hz. Each value represents the base current level and modulation depth that yielded significant vector strengths at the maximum number
of modulation frequencies, with the highest level and deepest depth selected in
case of ties. First-spike latencies were computed from responses to unmodulated pulse trains and were given by the median latency of the first spike
computed across all trials eliciting ⱖ1 spike. B: limiting modulation rate vs.
group delay. Limiting modulation rate was the maximum tested modulation
rate at which the vector strength was significant. In both panels, O and X
represent units for which the limiting modulation frequency was ⬍60 or ⱖ60
Hz, respectively. Large symbols represent isolated single units, and small
symbols represent multiunit recordings.
J Neurophysiol • VOL
The strength of phase locking to modulated 254-pps pulse
trains or to unmodulated lower-rate pulse trains varied according to the depth of units in the cortex. As described in METHODS,
current source density analysis was used as a functional means
of identifying the depth corresponding to the highest density of
thalamic afferents, and that depth will be taken as the origin of
a relative depth scale. In Fig. 10A, symbols represent first-spike
latencies of individual single- or multiunit recordings,. Firstspike latencies showed a minimum near the 0 ␮m, confirming
that neurons with short first-spike latencies tend to correspond
in laminar depth with the short-latency current sink in currentsource-density plots (P ⬍ 0.0001, ANOVA, depths grouped
with 300-␮m resolution). The across-trial SD of first-spike
latencies for each unit was small, averaging 1.92 ⫾ 1.21 ms
across 209 units, with 95% of units showing SD ⬍4 ms. There
was a weak but significant tendency for the SD of first-spike
latencies to increase with laminar distance from the thalamic
input layers, i.e., with distance from relative cortical depth ⫽
0 (r ⫽ 0.23, P ⬍ 0.001, n ⫽ 209).
Limiting modulation frequencies also varied significantly
with cortical depth. In Fig. 10B, data are grouped in 50-␮m
steps of cortical depth and 20-Hz steps of limiting modulation
frequency. Areas of circles are proportional to the number of
units at each combination of cortical depth and limiting modulation frequency, and shading indicates the range between
25th and 75th percentiles. The 75th percentile extended to 60
Hz at most depths, indicating that ⬎25% of units at most
depths showed significant phase locking to the highest frequency that was tested. The 25th percentile and median
showed a sharp dependence on cortical depth. More than 75%
of neurons located near the thalamic input layers phase locked
to 40 Hz and ⬎50% phase locked to 60 Hz. The limiting
modulation frequency fell off rapidly in supra- and infragranular layers. That suggests that if information about high modulation frequencies is propagated into noninput layers (and out
of cortical area A1), that information either is carried by phase
locking in a reduced population of neurons or is re-coded in
some form that is not evident as phase locking.
Group delay did not vary systematically with cortical depth.
In Fig. 10C, X and O plot the group delay of units that did or
did not, respectively, phase lock to modulation frequencies
ⱕ60 Hz. At any particular range of cortical depths, units with
lower limiting modulation frequencies tended to have longer
group delay (consistent with the data in Fig. 7), and the
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20
pulses. This is shown in the form of a cumulative distribution
in Fig. 9. Few neurons phase locked to unmodulated pulse rates
of 40 pps or higher, whereas 54% of the 61 neurons phase
locked to modulation frequencies as high as 60 Hz; that
percentage is 42.3% across the overall population of 267 units
for which limiting modulation frequencies were measured.
The spike rates in response to unmodulated pulse trains
resembled those to modulated trains in that, in both cases, spike
rates generally were greatest for pulse rates or modulation
frequencies around 10 pulses or modulation cycles per second.
In response to unmodulated pulse trains, 57 of 61 units (93%)
showed maximum spike rates for the 10-pps rate. Of those 57
units, 40 (70%) also responded with more than half-maximal
spike rates to 20-pps stimuli.
CORTICAL PHASE LOCKING TO COCHLEAR IMPLANT STIMULATION
1
100
A
C
20−21 Hz
20 Hz
40 Hz
60 Hz
80
40
40−42 Hz
60−64 Hz
0.4
60
% of Units
0.6
% of Units
Vector Strength
0.8
50
B
85
40
30
20
20 Hz
0.2
20
40 Hz
10
60 Hz
−30
−20
−10
Modulation Depth (dB)
0
0
0
25
50
75
Monotonicity Index
100
0
−35 −30 −25 −20 −15 −10 −5
Best Modulation Depth (dB)
FIG. 8. Nonmonotonic dependence of vector strength on modulation depth. A: vector strength as a function of modulation depth for the unit shown in Figs.
3–5. The parameter is modulation frequency. B: cumulative distribution of monotonicity index across recording sites at which vector strength was significant at
1 or more modulation depths. Monotonicity index is the vector strength at the deepest modulation tested (i.e., ⫺5 dB) as a percentage of the greatest vector
strength across all tested depths. The dotted line indicates a 75% criterion for nonmonotonic depth dependence. Large symbols show the percentage of isolated
single units showing monotonic indices ⱕ75. C: distribution of modulation depths producing the greatest vector strengths. n ⫽ 229.
percentage of units with long group delays was somewhat
higher in extragranular layers (consistent with Fig. 10B). Nevertheless, neither the units with high (P ⫽ 0.21, n ⫽ 95) nor
low (P ⫽ 0.57, n ⫽ 49) limiting modulation frequency showed
a significant dependence of group delay on cortical depth.
100
Units Phase Locked at Stated Rate (%)
Modulated 254−pps
Pulse Trains
Units with low limiting modulation frequencies are underrepresented in this plot, because 65 of the 114 units with low
limiting modulation frequencies did not phase lock to a high
enough frequency to permit computation of group delay; those
units tended to lie in extragranular layers. That probably
accounts for the lack of significant correlation of group delay
with cortical depth across the population of all units with
measurable group delays (P ⫽ 0.33, n ⫽ 144).
Signaling of modulation frequency by spike timing and rate
80
60
40
Unmodulated Pulse Trains
20
0
0
10
20
30
40
50
60
Pulse Rate (pps) or Modulation Frequency (Hz)
FIG. 9. Cumulative distribution of units significantly phase locked to unmodulated pulse trains varying in pulse rate or to modulated 254-pps pulse
trains varying in modulation frequency. The modulation frequency for each
unit was the highest value computed across all tested modulation depths. Small
symbols represent the 61 single- and multiunits (in 6 guinea pigs) that were
tested both with unmodulated and modulated pulse rates in 10-pps steps of
pulse rate and 10-Hz steps of modulation frequency, and large symbols
represent only the 11 isolated single units.
J Neurophysiol • VOL
We compared the ability of cortical neurons to signal the
frequency of a modulated pulse train either by a measure of
the timing of spikes or by frequency-specific spike rates. The
temporal measure was based on the poststimulus time histograms of units, each averaged across 20 repetitions of each
stimulus condition. For each unit and each stimulus frequency,
the poststimulus time histogram was transformed to the frequency domain with a discrete Fourier transform and the power
spectrum was computed; the power spectrum of the poststimulus time histogram is equivalent to the spectrum of the autocorrelogram of the histogram (Oppenheim and Schafer 1989).
For each unit and stimulus frequency, the highest-magnitude
frequency component in the spectrum was identified. This was
done for 120 units in nine animals that were tested with
modulation frequencies of 21, 42, and 64 Hz. Figure 11 shows
the distribution across units of maximum-magnitude frequency
components for the three tested frequencies and for two modulation depths. In all conditions, the most commonly identified
frequency component was the one equal to the stimulus frequency; these are regarded as “correct” identifications. At the
⫺5-dB depth, for instance the percentage of correct frequency
identifications was highest for the 21-Hz stimulus and decreased
with increasing stimulus frequency. This is consistent with the
decrease in the percentage of neurons that phase locked to higher
frequencies, particularly in extragranular layers. With a decrease
in modulation depth from ⫺5 to ⫺20 dB, there was a decrease in
the percentage of correct identifications, consistent with the de-
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0
−40
86
J. C. MIDDLEBROOKS
15
10
5
A
p<.0001, N= 209
−500
0
500
35
50
40
30
20
30
25
20
15
10
10
NPL
5
B
C
p<.01, N= 209
0
−500
0
500
Limiting Mod. Freq. < 60 Hz
Limiting Mod. Freq. ≥ 60 Hz
−500
0
500
Relative Cortical Depth (µm re early sink)
FIG. 10. Laminar dependence of 1st-spike latency, limiting modulation frequency, and group delay. Cortical depths are stated relative to the depth of the
shortest-latency current sink evident in current-source density analysis. Shading in Fig. 10, A and B, indicates the interquartile ranges, and continuous lines in
A–C represent medians. Those ranges and medians were computed by values within a 21-point sliding window. A: symbols represent 1st-spike latencies of
individual single- or multiunit recordings. Latencies varied significantly with cortical depth (P ⬍ 0.0001, ANOVA; depths grouped in steps of 300 ␮m). B: the
area of each circle is proportional to the number of recordings showing a particular limiting modulation frequency (rounded to the nearest 20 Hz) at a particular
cortical depth (rounded to the nearest 50 ␮m). Limiting modulation frequency varied significantly with cortical depth (P ⬍ 0.01, Kruskal-Wallis nonparametric
ANOVA). NPL, for “no phase locking,” indicates units for which no significant phase locking was observed at any tested modulation frequency or depth. A
nonparametric test was selected because of those NPL points and because of the coarse sampling of modulation frequencies. Limiting modulation frequencies
were assigned at the modulation depth that gave the highest value. C: symbols represent group delays of individual single- or multiunit recordings. O and X
represent units for which the limiting modulation frequency was ⬍60 or ⱖ60 Hz, respectively, and thick and thin lines represent the median values of those
respective populations.
crease in the percentage of units phase locked to the ⫺20-dB
depth. Nevertheless, the decrease in accuracy was in the form of
increased scatter of incorrect identifications—there was no systematic bias in errors. In this sample of units, phase locking of
cortical neurons signaled modulation frequency accurately across
a considerable range of modulation depth.
In response to modulated pulse rates, mean tonic spike rates
(i.e., omitting the 1st 100 ms of responses) varied both with
modulation frequency and with depth, as shown for an isolated
single unit in Fig. 5. That unit was fairly typical in that it
showed substantial changes in spike rate between modulation
depths at which there was little change in vector strength (i.e.,
compare the columns showing modulation depths of ⫺25 and
⫺15 dB). Also, the dependence of spike rate on modulation
frequency could differ depending on the modulation depth.
Figure 12 shows the distribution of spike rates across the
sample population (n ⫽ 267). The tonic spike rate for each unit
(the rate during 100 – 600 ms after stimulus onset) is shown
normalized by a constant equal to that neuron’s onset rate (the
rate during 0 –100 ms) to an unmodulated 254-pps pulse train.
The effect of modulation frequency on spike rate is shown for
modulation depths of ⫺20 (Fig. 12A) and ⫺5 dB (Fig. 12B). At
the shallower depth, the distribution across the sample showed
no significant dependence on modulation frequency. At the
deeper modulation, the distribution showed a small but significant decrease in spike rate with increasing modulation frequency. The effect of modulation depth on spike rate is shown
for modulation frequencies of 20 (Fig. 12C) and 60 Hz (Fig.
12D). At the lower modulation frequency, the distribution of
spike rates increased significantly with increasing modulation
depths. At the higher frequency, however, there was no significant dependence of spike rate on depth.
J Neurophysiol • VOL
DISCUSSION
Relation to previous studies
The study by Schreiner and Raggio (1996) is the most
directly related to this study. Those investigators tested cortical
multiunit responses to cochlear implant stimuli consisting of
unmodulated pulse trains that varied in rate from 2 to 38 pps.
The analysis relied on the “stimulus-locked firing rate,” which
was the firing rate measured in a window 10 –30 ms in length,
beginning 5 ms after each electrical pulse. Stimulus-locked
firing rates typically peaked at pulse rates averaging 7– 8 pps,
and stimulus-locked firing rates were reduced to one half at
pulse rates averaging 12–14 pps. In the present data set, 70%
of units showed half-maximal tonic spike rates persisting to
unmodulated pulse rates of 20 pps, although that spike-rate
measure counted all spikes regardless of timing. The preferred measures of stimulus-synchronized cortical responses
in this study were limiting pulse rate (for unmodulated pulse
trains) or limiting modulation frequency (for modulated
254-pps pulse trains), which are based on vector strength.
By those measures, about one half of cortical units in this
data set were synchronized to 30-pps unmodulated pulse
trains, and nearly one half were synchronized to pulse trains
modulated at 60 Hz.
Snyder et al. (2000) have explored phase-locked responses
of neurons in the cat’s inferior colliculus to cochlear implant
stimulation. Most neurons showed low-pass sensitivity to unmodulated pulse rates, with the limiting rate averaging 104 pps.
In response to 100%-modulated pulse trains, the limiting modulation frequency for 100% modulation depth averaged 42.2
Hz, but the distribution extended to 220 Hz. In the present
study, nearly one half of the cortical neurons phase locked to
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0
40
60
Group Delay (ms)
Limiting Modulation Frequency (Hz)
First−Spike Latency (ms)
20
CORTICAL PHASE LOCKING TO COCHLEAR IMPLANT STIMULATION
−20 dB
87
−5 dB
21 Hz
60
40
20
0
42 Hz
40
20
FIG. 11. Signaling of modulation frequency by
neural spike periodicity. Each panel plots the distribution among neurons of the highest-magnitude frequency component in an autocorrelation analysis of
firing patterns. In each panel, an arrowhead indicates
the modulation frequency. Panels are arranged by
modulation frequency (rows) and modulation depth
(columns). n ⫽ 120.
0
64 Hz
60
40
20
0
0
20
40
60
0
20
40
60
80
Highest−Magnitude Frequency Component (Hz)
modulator frequencies of 60 Hz. It is surprising to find higher
limiting modulation rates in the cortex than in the inferior
colliculus. One possible explanation is that phase locking in the
inferior colliculus might have been suppressed by the 100%
modulation depth that was used. Another possible explanation
is that Snyder et al. avoided electrical artifacts by restricting
analysis to inferior colliculus neurons giving the highestamplitude spikes. That might have biased their sample to
neurons with lower limiting phase-locked rates. In this study,
an artifact-rejection procedure enabled recordings from a
broader sample of units.
Cortical phase locking has been addressed in numerous
previous studies using acoustical stimulation with clicks or
modulated tones. Across most studies, phase locking to acoustical and electrical stimulation are similar in many respects.
Best modulation frequencies in both conditions are ⬃5–15 Hz
(Eggermont 1991, 1994, 1998, 2002; Gaese and Ostwald 1995;
Schreiner and Raggio 1996; Schreiner and Urbas 1988). Several studies have shown that best modulation frequencies in the
cortex tend to correlate with intrinsic cortical rhythms, either
spontaneous spindle frequencies or oscillatory rebounds following the offset of stimulation (Eggermont 1992; Gaese and
Ostwald 1995; Kenmochi and Eggermont 1997). Similarly,
neurons in this study often showed oscillatory responses at
⬃10 Hz in response to pulse trains that were presented at
pulse rates well above the rate to which the neuron could
phase lock. Studies using acoustic stimulation in anesthetized preparations have reported low-pass or band-pass
J Neurophysiol • VOL
dependence of cortical phase locking on stimulus rate, with
limiting rates around 20 Hz or lower (Creutzfeldt et al.
1980; Eggermont 1991, 1994, 1998, 2002; Gaese and Ostwald 1995); a median value of 25.1 Hz was reported by Lu
and Wang (2000) for anesthetized cats. Limiting rates generally were higher in this study using electrical stimulation.
In this study, 97% of units tested with unmodulated pulse
trains showed limiting rates of 20 pps or higher, and 49%
showed phase locking at 30 pps or higher.
One caveat in interpreting these results, and those of the
majority of previous studies, is that data were collected
under conditions of general anesthesia, which is known to
influence the temporal firing patterns of cortical neurons.
For instance, anesthesia tends to reduce the trial-by-trial
variance in first-spike latencies (Ter-Mikaelian et al. 2007)
and to increase the stimulus-related information carried by
first-spike latencies (Mickey and Middlebrooks 2003). Reports of the effects of anesthesia on cortical phase locking
are mixed. Several reports have concluded that anesthesia
impairs phase locking. That is, phase locking to sounds
extends to higher frequencies in unanesthetized preparations
(Anderson et al. 2006; Liang et al. 2002; Lu et al. 2001;
Ribaupierre et al. 1972; Steinschneider et al. 1998). In
contrast, Ter-Mikaelian et al. (2007) showed in gerbils that
anesthesia tended to enhance cortical phase locking to
modulation frequencies greater than ⬃20 Hz. Clearly, additional study of cortical phase locking under unanesthetized conditions is needed.
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% of Units
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Modulation Depth −20 dB
2.0
Modulation Depth −5 dB
A
B
p=0.46
p<.0001
1.5
0.5
0
20
40
60
20
40
60
Modulation Frequency (Hz)
2.0
Modulation Frequency 20 Hz
Modulation Frequency 60 Hz
C
D
p<.005
p=0.35
1.5
1.0
0.5
0
−35
−20
−5
−35
−20
−5
Modulation Depth (dB)
FIG. 12. Distribution of tonic spike rates as a function of modulation
frequency and depth. Data from modulation frequencies of 21, 42, and 64 Hz
were combined with data from 20, 40, and 60 Hz, respectively. For each unit,
tonic spike rates in the interval 100 – 600 ms after stimulus onset were
normalized by the onset response (in the interval 5–30 ms) to an unmodulated
pulse train. Base current levels were 4 dB above the threshold for the onset
response for each unit. Characteristics of the box and whisker plots are as
described for Fig. 6. The statistical tests were Kruskal-Wallis ANOVA. A and
B: distributions of normalized tonic spike rates as a function of modulation
frequency at depths of ⫺20 (A) and ⫺5 dB (B). C and D: distributions of
normalized tonic spike rates as a function of modulation depth at frequencies
of 20 (C) and 60 Hz (D).
Sensitivity to modulation frequency and depth
One of the goals of this study was to explore the upper end
of the range of modulation frequencies that might elicit phaselocked activity in cortical neurons. About one half of the tested
cortical neurons exhibited significant phase locking across the
⬃2-to ⬃50-Hz range of modulation frequencies that carry
information for speech recognition by cochlear implant users.
Normal-hearing listeners can detect sinusoidal modulation of
broadband noise at modulation depths as low as approximately
⫺25 dB (Viemeister 1979), and cochlear implant users can
detect modulation of electrical pulse trains at depths of approximately ⫺40 dB (Busby et al. 1993; Galvin and Fu 2005;
Pfingst et al. 2007; Shannon 1992). A likely explanation for the
greater modulation sensitivity observed with electrical stimulation derives from the extremely limited dynamic range of
electric hearing. At a modulation depth of ⫺35 dB, for instance, the range of amplitudes from minimum to maximum is
0.31 dB, which is a sizeable fraction of the dynamic range of
J Neurophysiol • VOL
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Normalized Tonic Spike Rate (spikes/s)
1.0
electric hearing, yet is below the just-noticeable difference for
sound pressure level. Moreover, modulation sensitivity with
cochlear implants might be enhanced by the enhanced acrossfiber synchrony that is observed under conditions of electrical
stimulation. That speculation is consistent with the observation
that relatively widespread cochlear electrical stimulation using
a monopolar configuration promotes greater modulation sensitivity than does stimulation with a more-restricted bipolar
configuration (Middlebrooks 2008).
There have been few previous studies of the effects of
modulation depth on phase locking of cortical units. One
exception is a study of responses to amplitude-modulated
acoustic tones in auditory cortex of unanesthetized marmosets
(Liang et al. 2002). A subset of the studied units tested over
limited ranges of modulation depths showed generally monotonic increases in firing rate with modulation depth increasing
from 20 –50 to 100%. Eggermont (1994) compared phase
locking to acoustic noise modulated with 25, 50, 75, and 100%
modulation depths. Modulation depths of 50% and higher
produced similar temporal modulation transfer functions. In
that study, modulation depths ⬍50% rarely evoked phase
locking to the modulating waveform. Krishna and Semple
(2000) studied responses in the gerbil inferior colliculus to
tones varying in modulation frequency and depth. Most of their
units showed monotonic increases in response rate with increasing modulation depth. Thresholds for phase-locked responses were as low as ⬃10% modulation (⫺20 dB). The
present study examined phase locking systematically to modulation depths as low as 1% modulation (⫺40 dB). As shown
in Fig. 6, significant phase locking often was seen at depths as
low as ⫺35 dB; modulation thresholds as low as ⫺40 dB were
shown using a more sensitive measure introduced elsewhere
(Middlebrooks 2008). Similarly, Litvak et al. (2001) showed
phase locking of auditory nerve fibers to electrical pulse trains
modulated with a depth of ⫺40 dB.
A novel finding of this study was the nonmonotonic dependence of vector strength (and spike rate) on modulation depth.
Most units showed vector strength increasing monotonically
with increasing modulation depth for modulation frequencies
up to ⬃20 Hz, but the majority of units showed a nonmonotonic dependence of vector strength on depth for higher modulation frequencies. Moreover, the results for unmodulated
pulse trains can be interpreted as parallel to the modulation
results if one regards these relatively low-rate pulse trains as
showing modulation effectively deeper than that produced by
100% modulation of a high-rate carrier. That is, nearly all units
showed significant phase locking to pulse trains at rates ⱕ20
pps, and the percentage of phase-locked units declined sharply
at higher pulse rates. It remains to be tested whether nonmonotonic depth dependence is limited to cochlear implant stimulation or whether it also is seen with acoustic stimulation. The
nonmonotonic depth dependence observed in this study combined with the use of click-train or 100%-modulated stimuli in
nearly all previous studies might account for the many observations, particularly in anesthetized animals, that phase locking
in the auditory cortex is largely absent at rates ⬎20 Hz
(Creutzfeldt et al. 1980; Eggermont 1991, 1994, 1998, 2002;
Gaese and Ostwald 1995; Schreiner and Urbas 1988).
A tentative hypothesis to explain the nonmonotonic dependence of phase locking on modulation depth is that a strongly
modulated stimulus could elicit tightly synchronized periods of
CORTICAL PHASE LOCKING TO COCHLEAR IMPLANT STIMULATION
intracortical inhibition, somewhat shorter than the 50- to
100-ms period of inhibition that was observed following the
initial excitatory response to the onset of nearly all suprathreshold stimuli. Recovery from inhibition in somewhat ⬍50
ms would permit phase-locked responses to modulation frequencies of 20 Hz or lower but would suppress responses to
higher modulation frequencies. Hypothetically, inhibition produced by stimuli with shallower modulation depth would be
weaker in magnitude or quicker to recover, thereby permitting
phase locking to higher modulation frequencies. That hypothesis remains to be tested.
Temporal and rate codes for modulation frequency
J Neurophysiol • VOL
The spike rates of the single unit plotted in Fig. 5, for instance,
varied by a factor of ⬃2 between modulation depths of ⫺25
and ⫺15 dB, whereas vector strength was essentially constant
over that range. Similar characteristics are seen in the distribution of spike rates across the entire sample of units (Fig. 12).
One must leave open the possibility that different conclusions
might be reached in a future study in unanesthetized animals.
Nevertheless, these results show that the sensitivity of cortical
neurons to modulation frequency is so confounded by modulation-depth sensitivity that the modulation-frequency information carried by spike rates is essentially uninformative.
Cochlear implant users report increases in perceived pitch
corresponding to increases in the rate of unmodulated pulse
trains to rates as high as 300 pps (Geurts and Wouters 2001;
Kong et al. 2004; McKay et al. 1994; Xu et al. 2002; Zeng
2002). Based on the present results, it seems highly unlikely
that discriminations of such high pulse rates rely on phaselocked activity in the auditory cortex. No data are available for
electrical stimulation in unanesthetized primates, but the
acoustic click data in unanesthetized marmosets (Liang et al.
2002) indicate that only ⬃20% of cortical neurons phase lock
to 128-cps click trains and only a handful phase lock to 256
cps. Evidence for an alternative, non–phase-locked, representation of pulse rate comes from the work of Lu et al. (2001) in
the auditory cortex of unanesthetized marmosets. Those investigators described a class of “nonsynchronized” units that
showed spike rates that increased with increases in acoustic
click rates beginning at rates of 50 cps/s or higher. If such a
rate code exists for electric hearing, it could conceivably
account for rate-pitch perception in cochlear implant users. In
this study, ⬃30% of units showed tonic non–phase-locked
responses that generally increased in spike rate with increases
in pulse rates above ⬃50 pps, but that effect was not robust and
showed considerable trial-by-trial variation (data not shown).
One presumes that the low tonic rates observed in this study
were at least partially caused by the use of anesthetic. Future
studies in unanesthetized animals are needed to test whether
nonsynchronized tonic spike rates could signal rate pitch in
electric hearing.
Low-pass filtering of modulation
The highest phase-locked frequencies in the auditory cortex
are considerably lower than those observed in the subcortical
auditory pathway. Phillips (1989) considered and rejected the
hypothesis that this low-pass characteristic of phase locking
results from the accumulated temporal imprecision of transmission across the multiple synapses in the path from ear to
cortex. Contrary to an imprecision hypothesis is the observation that the trial-by-trial SD in first-spike latencies in the
cortex is less than the temporal periods of frequencies to which
cortical neurons phase lock. In this study, for instance, the SD
of first-spike latencies averaged ⬍2 ms. If imprecision in
transmission through the auditory pathway were the limiting
factor in phase locking, a SD of 2 ms would yield period
histograms with about two thirds of spikes falling within a
4-ms range. That would predict strong phase locking to 125pps pulse trains, which was never observed. Similarly, imprecision in intracortical transmission cannot account for observations that the percentage of neurons showing phase locking
to modulation frequencies of 60 Hz is reduced in extragranular
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A goal of this study was to evaluate the cortical representation of the modulation frequencies of cochlear implant stimuli
in the frequency range relevant to speech reception, i.e., up to
⬃50 Hz. One can consider two general forms of representation: temporal codes, in which the timing of features of stimulus envelopes are mirrored in the timing of cortical spikes,
and rate codes, in which the spike rates of neurons vary
systematically with stimulus frequency. The results showed the
feasibility of a temporal code based on the phase-locking of
cortical neurons. Significant phase locking to modulation frequencies ⱕ60 Hz was observed in nearly all units recorded in
thalamic afferent layers and in no less than 25% of units
recorded in extragranular layers. An analysis based on autocorrelation (Fig. 11) showed that the frequency of phase
locking of many neurons corresponds closely to the stimulus
modulation frequency across a considerable range of modulation depths. A reduction in modulation depth from ⫺5 (56%)
to ⫺20 dB (10%) resulted in some increase in the scatter of
responses but produced no systematic change in the frequency
signaled by the phase-locking firing patterns of neurons. Presumably the frequency signal in the population would have
been even more salient if the analysis had been restricted only
to the population of phase-locked units. The significance of the
apparent loss of phase locking in some 75% of units in
extragranular layers is unclear. One possibility is that temporal
information is transformed within those layers to an as-yetunidentified nontemporal code by which information is transmitted to other cortical and noncortical areas. According to that
scheme, the phase-locked units in those layers might be interneurons and some or all of the non–phase-locked neurons
would be the projection neurons. An alternative possibility is
that temporal information is restricted to specialized pathways
and that the non–phase-locking neurons are part of a pathway
specialized for one or more functions for which stimulus
envelope frequency is irrelevant or for which phase locking is
a liability.
These results are not consistent with a straightforward model
in which modulation frequency in the range of 10 – 60 Hz is
signaled by the spike rate of cortical neurons. A sizeable
majority of units has best modulation frequencies ⬃10 Hz.
That indicates that modulation tuning of spike rates is not a
likely candidate for representation of modulation frequencies
in the range of 20 – 60 Hz. The tonic spike rates of single
neurons can vary with modulation frequency, such that a
change in spike rate might signal a change in modulation
frequency, but spike rates and the frequency dependence of
spike rates are confounded by changes in modulation depth.
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Concluding remarks
The existing literature on phase-locked responses in the
auditory cortex is based primarily on responses to acoustic
stimuli, either unmodulated clicks or 100%-modulated sinusoids. The general impression conveyed by that literature is
that phase locking in the cortex cuts off at modulation frequencies of ⬃20 Hz or lower and that the failure to phase lock to
J Neurophysiol • VOL
higher frequencies is a result of imprecise feed-forward transmission at thalamo-cortical and intracortical synapses and/or
some sort of persistent intracortical inhibition. This study
explored a broader range of modulation frequencies and modulation depths, using cochlear implant stimulation. Tests of the
sensitivity of phase locking to modulation depth showed that
the deepest modulation tended to suppress phase locking,
perhaps accounting for the failure of previous studies to show
phase locking at rates above ⬃20 Hz. Indeed, this study
showed cortical phase locking up to ⱖ64 Hz, extending somewhat beyond the range of modulation frequencies needed for
speech recognition with cochlear implants. Examination of the
relationships among laminar depth of neurons, limiting modulation frequency, first-spike latencies, and group delays lead
us to reject the hypothesis that phase-locking to high modulation frequencies (i.e., ⬎20 Hz) is merely “lost” within the
cortex because of accumulated effects of multiple imprecise
synaptic links. Instead, the results suggest that high modulation
frequencies are low-pass filtered within the cortex, perhaps as
a consequence of recoding in some as-yet unidentified form.
Forthcoming data extend these findings by quantifying modulation detection thresholds as a function of electrical stimulus
parameters that are important for cochlear implant speech
processor design (Middlebrooks 2008).
ACKNOWLEDGMENTS
We thank S. Furukawa, C.-C. Lee, K. Otto, and A. Kirby for participation
in some of the experiments. A. Kirby, B. Pfingst, E. Macpherson, and R.
Snyder made helpful comments on versions of the manuscript. J. Wiler
provided skillful technical assistance, and Z. Onsan helped with the illustrations.
GRANTS
This work was supported by National Institute for Deafness and Other
Communication Disorders Grants RO1 DC-04312 and P30 DC-05188.
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