J Neurophysiol 108: 3276 –3288, 2012.
First published September 26, 2012; doi:10.1152/jn.01192.2011.
Working with memory: evidence for a role for the medial prefrontal cortex
in performance monitoring during spatial delayed alternation
Nicole K. Horst1,2 and Mark Laubach1,3
1
The John B. Pierce Laboratory, New Haven, Connecticut; 2Interdepartmental Neuroscience Program, Yale University
School of Medicine, New Haven, Connecticut; 3Department of Neurobiology, Yale University School of Medicine,
New Haven, Connecticut
Submitted 26 December 2011; accepted in final form 22 September 2012
executive function; working memory; error processing; reward; persistent activity
THE GOAL OF THIS STUDY was to understand the role of prefrontal
regions of the rodent cerebral cortex in the performance of
spatial delayed alternation (DA) tasks. DA tasks are simple
tests of spatial working memory function in which the subject
is rewarded for responding at alternating locations after a delay
period (e.g., Mishkin and Pribaum 1955). Damage in dorsal
parts of the prefrontal cortex of primates produces lasting
impairments in spatial DA (e.g., Goldman et al. 1971). Neurons in these cortical regions are thought to maintain information about the spatial choice over delay periods based on
persistent and spatially selective spike activity (Fuster and
Alexander 1971; Kubota and Niki 1971; Preuss 1995). Rodents
do not have dorsal (granular) prefrontal regions, but they do
have medial prefrontal regions (mPFC) that may be homol-
Address for reprint requests and other correspondence: M. Laubach, The
John B. Pierce Laboratory, 290 Congress Ave., New Haven, CT 06519 (e-mail:
[email protected]).
3276
ogous to cingulate and ventromedial prefrontal regions in
primates (Laubach 2011). Lesions in the mPFC of primates
have produced mixed results in spatial DA tasks. Some studies have reported impairments (Pribram et al. 1962), while
others have reported only transient or variable effects (Murray
et al. 1989; Pribram and Fulton 1954; Rushworth et al. 2003).
In rodents, damage in the mPFC, especially in the prelimbic
area, impairs spatial DA performance in the T maze (Brito et
al. 1982) and operant spatial DA tasks (Dunnett et al. 1999; van
Haaren et al. 1988). These effects tend to be transient in nature
(e.g., Brito et al. 1982; Delatour and Gisquet-Verrier 2001) and
may be due to reductions in spontaneous alternation (Delatour
and Gisquet-Verrier 1996). Interestingly, lesions of mPFC do
not consistently impair performance in another test of spatial
working memory, the radial arm maze (Delatour and GisquetVerrier 1996; Gisquet-Verrier and Delatour 2006; Ragozzino
et al. 1998). Data from primate mPFC, including from studies
of human subjects, suggest that this region is involved in
conflict monitoring and action selection (reviewed in Seamans
et al. 2008). Therefore, a plausible theory of rodent mPFC
function is not that it is involved in spatial working memory
per se, but that it has a role in performance monitoring or
“working-with-memory” to optimize behavioral responding
(Moscovitch and Winocur 1992).
The idea of “working-with-memory” fits well with modern
views on the functional significance of the mPFC in cognitive
and motivational control (e.g., see Rushworth et al. 2011 for
review). A deficit in action and/or outcome monitoring following damage in the mPFC would lead to erratic performance of
the spatial DA task. A lapse of control over performance, even
with an intact spatial memory system, would lead to an increased frequency of errors. Only a few studies that have used
spatial DA-style designs have examined any nonspatial effects
of mPFC lesions or inactivations. Dunnett et al. (1999) reported that lesions of mPFC do not lead to perseverative
responding (i.e., repeated responding at an incorrect location),
and this result was recently confirmed in a reversible inactivation study by Horst and Laubach (2009). In that study, we also
noted that errors were associated with longer and more variable
intertrial intervals and that inactivation of mPFC increased
temporal variability prior to correct responding. Control of
response timing is a key nonspatial factor for spatial DA
performance, as a prompt response (e.g., rapid travel to the
required location at the end of the delay period) would minimize demands on working memory.
Neuronal recording studies have, unfortunately, not helped
resolve the role of mPFC in spatial DA performance. We are
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Horst NK, Laubach M. Working with memory: evidence for a
role for the medial prefrontal cortex in performance monitoring during
spatial delayed alternation. J Neurophysiol 108: 3276 –3288, 2012.
First published September 26, 2012; doi:10.1152/jn.01192.2011.—
Neuronal spike activity was recorded in the medial prefrontal cortex
(mPFC) as rats performed an operant spatial delayed alternation task.
The sensitivities of neurons to choice, outcome, and temporal information-related aspects of the task were examined. About one-third of
neurons were sensitive to the location of delayed responding while
animals were at one of two spatially distinct response ports. However,
many fewer neurons (⬍10%) maintained choice information over the
delay, each exhibiting persistent differences in firing rates for only a
portion of the delay. Another third of cells encoded information about
behavioral outcomes, and some of these neurons (⬎20% of all cells)
fired at distinct rates in advance of correct and incorrect responses
(i.e., prospective encoding of outcome). Other cells were sensitive to
reward-related feedback stimuli (⬎20%), the outcome of the preceding trial (retrospective encoding, 5–10%), and/or the time since a trial
was last performed (10 –20%). An anatomical analysis of the recording sites found that cells that were sensitive to choice, temporal, and
outcome information were commingled within the middle layers of
the mPFC. Together, our results suggest that spatial processing is only
part of what drives mPFC neurons to become active during spatial
working memory tasks. We propose that the primary role of mPFC in
these tasks is to monitor behavioral performance by encoding information about recent trial outcomes to guide expectations and responses on the current trial. By encoding these variables, the mPFC is
able to exert control over action and ensure that tasks are performed
effectively and efficiently.
PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
METHODS
Rats had regulated access to water for 1 wk prior to training and
throughout the period of training and testing. Food was available ad
libitum. During training sessions, rats were reinforced with water,
which was supplemented in the home cage to maintain them at ⬃90%
of their free access body weight. All procedures were approved by the
Animal Care and Use Committee at the John B. Pierce Laboratory and
conformed to the standards of the National Institutes of Health Guide
for the Care and Use of Laboratory Animals. All efforts were taken to
minimize the number of animals used and to reduce pain and suffering.
Behavioral Task
Adult male Long-Evans (N ⫽ 5) or Brown Norway (N ⫽ 4) rats
were trained to perform a spatial DA procedure (Caetano et al. 2012;
Horst and Laubach 2009). They were trained in an operant box housed
within a sound-attenuating chamber (ENV-008, Med Associates). A
spout located between two barriers (which controlled for the posture
of rats during lever pressing and reward consumption) was located at
one end of the chamber, and two choice ports were located on the
opposite wall (Fig. 1A). All response locations were equipped with
infrared beams that detected head entries into the choice ports and
licks to the reward spout. Behavioral chambers and devices were
custom made by the Instruments Shop at the John B. Pierce Laboratory and were controlled with software (MedPC) and a computer
interface from Med Associates.
Five rats were trained and tested in a self-paced version of the
spatial DA task that we have previously shown depends upon functional mPFC (Horst and Laubach 2009; Fig. 1B, top). To rule out the
possibility that allowing rats to set their own pace would permit them
to circumvent the use of spatial working memory, we trained a
separate group of four rats in a modified version of the task with
greater temporal control over the delays between successive trials
(Fig. 1B, bottom).
Self-paced spatial delayed alternation. Five rats were trained to
alternate between spatial choices at their own pace, with methods
described by Horst and Laubach (2009). Rats were trained first to
collect water from the reward spout at periodic intervals (every 5–20 s).
Next, they were required to make head entries into either choice port
to trigger a reward. In the final stage, rats were required to alternate
their choices between left and right ports in order to receive a reward.
An example of a successful alternation trial is shown in Fig. 1A. In
this case, the rat has just made a correct response in the right choice
port (Fig. 1A, left) and must traverse the chamber to collect the reward
(Fig. 1A, center), which is delivered in a pulsatile fashion (six 0.25-s
pulses, separated by 0.50-s pauses) to encourage the rat to remain in
this position over a short delay. Once contact with the spout is made,
the rat is free to make the next spatial choice (Fig. 1A, right). A
response in the opposite choice port (in this case, the left port) is
rewarded. If the same port is chosen on consecutive trials, it is scored
as an error; the lights in the chamber extinguish, and the rat must make
contact with the reward spout before making the next choice, although
no reward is delivered. After an error, the target location for the
correct choice is always in the choice port opposite to the one chosen
erroneously.
Modified spatial delayed alternation task with controlled delays.
Four rats were trained in a modified version of the spatial DA
procedure that permitted experimenter control over delays by
requiring rats to press a lever on a variable-interval schedule
between spatial choices, similar to the paradigm used by Caetano
et al. (2012). Rats were first trained to press a lever below the
reward spout to trigger reward delivery. In the next phase of
training, a single lever press initiated the choice phase, in which a
head entry into either choice port resulted in availability of fluid at
the reward spout. A light above the choice ports illuminated during
the choice phase (the “Go” stimulus), and a light above the lever
illuminated during the reward/delay phase (the “Collect” stimulus).
Rats were then trained to alternate spatial choices. Once rats were
alternating well (⬎65% correct), they were required across separate sessions to press the lever on successively increasing fixed
ratio schedules (FR1, 2, 4, 8) to initiate the choice phase. In this
phase of training, rats learned to stay at the lever until a press
triggered the Go stimulus.
Fully trained rats had to press the lever on a variable-interval
schedule (0-, 5-, 10-, 15-, 20-s delays, pseudorandomly interleaved)
between spatial choices. The first press occurring after the assigned
delay initiated the choice phase (Fig. 1B, darkest tick below delay for
the “Controlled” task). Successive choices in the same location were
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not aware of any recordings in the mPFC of primates performing spatial DA tasks. Similar to the lesion literature reviewed
above, recording studies in rodents have resulted in a mixed set
of findings. Batuev et al. (1990) claimed to find neurons that
fired persistently in a choice-sensitive fashion over the delay
period in a maze-based DA task, but they did not control, or
even monitor, the animals’ behavior in the start box during the
delay period. Jung et al. (1998) used several maze-based DA
designs and found that few, if any, mPFC neurons showed
choice-sensitive persistent activity during the delay period.
Finally, Baeg et al. (2003) used maze-based tasks and reported
that simultaneous recordings of groups of neurons could be
used to predict the spatial location of the delayed response. As
in the Jung study, few neurons were both choice sensitive and
persistently active over the delay period, and these neurons did
not need to be included in the neuronal population for successful decoding of the spatial response.
In the present study, we report data from mPFC neurons that
were recorded during an operant spatial DA task. The task was
designed to minimize postural variability during the delay
period with the same methods as in a recent reversible inactivation study from our laboratory (Horst and Laubach 2009).
This issue is important because postural variability could lead
to apparent encoding of choice by mPFC neurons, e.g., due to
differences in posture and/or heading direction during the delay
period (Cowen and McNaughton 2007; Euston and McNaughton 2006). Indeed, behavioral studies have shown that, if
allowed, rats tend to use postural strategies to mediate information about the spatial choice over delay periods (Chudasama
and Muir 1997; Ennaceur et al. 1997).
Using the operant spatial DA task, we assessed how the
spike counts of mPFC neurons encoded choice-, outcome-, and
temporally related information at two key time points in the
task: 1) when rats entered choice ports after the end of the
delay period and 2) during the initial portion of the delay
period when rats consumed fluid at a reward port. We found
many neurons that encoded information about the spatial
choice when rats were in distinct locations in the environment.
However, fewer neurons maintained choice information over
the delay period. Many other neurons were sensitive to nonspatial aspects of the task (i.e., the outcome of the delayed
response and the time since the last trial). Few neurons were
jointly sensitive to multiple task attributes or to single task
attributes across multiple phases of the task. Together, our
findings suggest that mPFC encodes a diverse set of task
attributes (spatial and nonspatial) that could be used to monitor
behavioral performance in order to control spatial working
memory processing.
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PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
A
Choice
Reward
Alternate
= 0.25 sec ON
Syringe pump
B
= 0.50 sec OFF
SELF-PACED
Go to Reward
First lick /
press
Choice
Go to Choice
Last lick /
press
Go to Reward
Delay = VI lever press
Go to Choice
= delay press
= trial initiating press
Lever press
C
100
Behavioral Accuracy
*
D
35
#
Inter-response Interval
*
30
90
IRI (s)
% Correct
25
80
70
= Rat 1
= Rat 2
= Rat 3
= Rat 4
= Rat 5
20
15
10
60
0
Q1 Q2 Q3 Q4
Self-paced
0
5
10 15 20
Controlled
Cor
Err
50
Cor
Err
5
Selfpaced
0
5
10
15
20
Controlled
Fig. 1. Spatial delayed alternation task and performance. A: schematic of behavioral arena with sequence of primary events. In the “Choice” phase, the rat selects
the right nose poke port and then traverses the chamber to collect a reward. Reward collection starts the delay period. At the end of the delay, the rat crosses
back to the other side of the chamber to the alternate (left) choice port for another correct response. B: detailed sequence of events in the self-paced and controlled
versions of the spatial delayed alternation (DA) task. In the self-paced version (top), after correctly responding in the choice port the rat licks the reward spout,
which triggers pulsed delivery of fluid (six 0.25-s pulses of water separated by 0.50-s pauses). The rat is then free to make the next spatial choice. Choosing the
opposite choice port results in another reward. In the controlled version (bottom), the rat makes a choice and then travels to the reward spout. In addition to
collecting a reward during the ensuing delay, the rat must also press a lever on a variable-interval (VI) schedule (0, 5, 10, 15, or 20 s, randomly interleaved)
to initiate the next choice period. Selection of the opposite choice port is rewarded. In either task variation, selection of the same choice port in consecutive trials
is scored as an error and no reward is delivered. After an error, rats must initiate the next trial by contacting the reward spout (self-paced) or pressing the lever
on a variable interval schedule (controlled). The trial following an error is a correction trial, in which rats must select the choice port that would have been correct.
C: performance accuracy of rats in the self-paced (left, N ⫽ 3 rats that performed postoperatively) and controlled (right, N ⫽ 2 rats with isolated single units
only) versions of the spatial DA task, with symbols denoting individual subjects. For the self-paced version, accuracy is presented by interresponse interval (IRI)
quartile (Q1, Q2, Q3, Q4). For the controlled version, accuracy is presented by assigned delay (0, 5, 10, 15, 20 s). *P ⬍ 0.01 for shortest 3 vs. longest quartile
of IRIs in the self-paced version. #P ⫽ 0.07 for shortest 2 vs. longest 2 assigned delays in the controlled version. D: median IRI of rats in the self-paced (left,
N ⫽ 3) and controlled (right, N ⫽ 2) task variations, with symbols denoting individual subjects. The overall median of IRIs is presented for self-paced rats, while
IRIs are presented by assigned delay for the controlled-delay rats. *P ⬍ 0.01 for IRIs preceding correct vs. error responses in the self-paced version. (All P values
reported are from t-tests of paired samples).
scored as errors. The choice phase was reset via lever pressing on the
0- to 20-s variable-interval schedule, and trial correction proceeded as
in the self-paced version of the task.
Surgeries
Rats were trained until they performed the task with an accuracy
of ⬎75% correct. They were then given 1 wk of full access to
water and implanted with microwire arrays into mPFC. After
initial anesthesia with ⬃4% halothane, intraperitoneal injections of
ketamine (80 –100 mg/kg) and diazepam (8 –10 mg/kg) or ketamine and xylazine (10 mg/kg) were administered. Supplements
(1/3 of the initial dose) of the two drugs administered in the
procedure were given approximately every 60 min. By standard
methods, arrays of microwire electrodes were placed in mPFC with
a craniotomy that was centered at 3.2 mm rostral to bregma and
⫾1.4 mm lateral to bregma. The arrays were placed to avoid major
vessels within the craniotomy and were lowered to a depth of
2.8 –3.6 mm at an angle of 12° from the midline. Eight rats were
implanted with fixed arrays made with 50-␮m stainless steel wire,
which had in vitro impedance between 200 and 300 k⍀, arranged
in a 2 ⫻ 8 configuration with ⬃200 ␮m between electrodes (NB
Labs). One rat (trained to perform the self-paced version of the
spatial DA task) was implanted with a drivable array of microelectrodes (1 ⫻ 8 configuration, CD Neural Technologies), placed at
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CONTROLLED
Delay = pulsed pump delivery
PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
the following coordinates: AP: ⫹3.2, ML: ⫹1.4, DV: ⫺2.8 mm at
12° from the midline. The microdrive was lowered in steps of 0.05
mm every day throughout the period of the recordings (45 days).
Of the five rats trained in the self-paced version of the task, one
became ill and a second did not complete any trials after microelectrode implantation. Two of the rats trained in the controlled version of
the task did not have clear single-unit activity. Data from these four
rats were excluded from all aspects of the study.
Histological Procedures
Once experiments were complete, rats were anesthetized with 100
mg/kg pentobarbital sodium and transcardially perfused with 10%
formalin. Brains were sectioned horizontally on a freezing microtome,
mounted on subbed slides, and stained for Nissl with thionin. Histological examination of electrode tracts showed that recording sites
were located in the mPFC of all rats (see Fig. 2).
1
5
4
FrA
3
PrL
2
Cg2
1
Bregma
Self-paced
B
1
0
2
+5
Controlled delays
3
4
0
Data were averaged across sessions for each subject prior to
statistical comparisons. Comparisons were made between variables
with t-tests for independent or paired samples, as appropriate. For
both versions of the spatial DA task, performance was assessed in
terms of accuracy (number of correct trials/total number of trials) and
the median length of interresponse intervals (IRIs) preceding correct
versus error responses. To determine whether the length of time
between successive choices, i.e., the delay, affects performance in the
self-paced version of the task, accuracy was assessed for each IRI
quartile. For the controlled-delay version of the task, accuracy was
assessed for each of the assigned delays (0 –20 s). Comparisons were
also made between median IRI lengths preceding correct versus error
responses in both task versions. Finally, to assess whether there were
gross differences in movements depending on the spatial choice made,
comparisons between the movement times (“Go to Choice” in Fig.
1B) were made for left versus right choices. To visualize differences
in performance across rats, data are presented as bar plots with
individual rats represented as symbols (Fig. 1, C and D). Only rats that
were subsequently assessed for neuronal responses (i.e., those with
clear single-unit activity and sufficient behavioral data during recording sessions) were included in these analyses.
Assessment of Neuronal Sensitivity to Behavioral Events
2
Horizontal section
Rats
Exploratory analyses of neuronal activity and behavior were performed with NeuroExplorer (Nex Technologies). Statistical analyses
of neuronal and behavioral data were performed with MATLAB
(MathWorks) and R (www.r-project.org). Computer code used for
the data analysis in the study is available upon request from the
corresponding author.
5
-5
-3.38 mm
5
Region explored
with microdrive
in rat 1 ( )
Fig. 2. Neuronal ensemble recordings in the medial prefrontal cortex (mPFC).
A: locations of implanted microwire electrode arrays in the 5 behaving rats
with isolated single units are shown on a horizontal section through the mPFC.
B: the depth of the section in A is shown in this diagram (as the gray horizontal
line), and the region explored with the microdrive in rat 1 is depicted by the
blue box. FrA, frontal association area; PrL, prelimbic area.
Neuronal firing patterns around the primary behavioral events in
the task (choice port entry and reward collection) were compared
between self-paced (N ⫽ 91 neurons) and controlled (N ⫽ 92
neurons) versions of the spatial DA task to evaluate whether subsequent analyses could be carried out on the combined data set. The
average firing rate and 95% confidence intervals (with bootci.m in
MATLAB) were calculated and plotted over a 10-s window centered
on the event of interest. Deviations from the overall mean firing rate
around the event of interest suggest that the population is generally
modulated around the event. Divergence versus overlap of the confidence bands can be used to assess differences and similarities between
the firing patterns observed in the two task variations.
Nonparametric rank sum and rank correlation tests were used to
assess the identity and fraction of neurons that were sensitive to
various aspects of DA performance, including the animal’s current
choice (left vs. right port), the outcome of the current trial (correct vs.
error), the outcome of the previous trial, and the (log transformed)
time since the last response was made. A neuron was included in the
analysis if its mean firing rate was ⬎0.1 Hz (⬎98% of neurons) and
if the rat performed the task with an accuracy of ⬎75% correct and
had ⬎10 errors in that session. This minimum error requirement was
needed to explore outcome-related differences in firing rates. The
analysis was done either around the time of choice port entry or
around the time of reward collection with a sliding window of 1 s, in
steps of 0.1 s. Neurons were considered sensitive to differences in
performance if P ⬍ 0.05 (with Bonferroni correction for 4 behavioral
variables, P ⬍ 0.0125) by a rank sum test (effects of spatial choice
and current and past behavioral outcomes) or a rank correlation test
(effects of time since the last trial was performed).
The fraction of sensitive neurons was then assessed in each of the
0.1-s bins around the time of the choice or reward collection to
determine the task epoch in which particular classes of neurons
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One week after surgery, neuronal recordings were made during
behavioral sessions with a multielectrode recording system (Plexon).
Single units were identified online with an oscilloscope and audio
monitor and off-line with the Plexon Offline Sorter. Online sorting
was done with the “boxes” feature in the Plexon software, in which
waveforms were manually selected on the basis of their amplitude and
deviation from background firing. Artifacts due to cable noise and
behavioral devices were removed during off-line sorting. Single units
were identified as having 1) consistent waveform shape, 2) average
amplitude estimated at least three times larger than background
activity, and 3) a consistent refractory period of at least 2 ms in
interspike interval histograms.
A
Data Analyses
Behavioral Analyses
Electrophysiological Recordings
Midline
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PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
RESULTS
Spatial Alternation Performance Diminished as a Function
of Increasing Delay
Rats performed the spatial DA task with high levels of
accuracy and tended to perform less accurately as the time
between responses increased, a hallmark of tasks that depend
on working memory processing. Accuracy in the self-paced
version of the task was significantly less at the longest IRIs
(fourth quartile ⫽ 80.2 ⫾ 1.43%) compared with all other IRIs
(quartiles 1–3 ⫽ 88.9 ⫾ 1.76%; t-test of paired samples, t ⫽
⫺16.8, P ⫽ 0.004). There was a trend toward less accurate
performance at the longest assigned delays (15 and 20 s ⫽ 62.4 ⫾
4.67%), compared with the shortest delays (0 and 5 s ⫽ 91.2 ⫾
1.29%) in the task version with controlled delays (t-test of
paired samples, t ⫽ 8.51, P ⫽ 0.07). Accuracy data are
presented in Fig. 1C. In the self-paced version, correct trials
were preceded by shorter IRIs compared with errors (27.5 ⫾
1.44 s ⬍ 30.0 ⫾ 1.45 s; t-test of paired samples, t ⫽ ⫺113.5,
P ⬍ 0.001; Fig. 1D). There were no differences in IRIs
preceding correct versus error responses at any of the temporally controlled delays (Fig. 1D), reflecting the fact that the
length of the assigned delay was the dominant factor determining the IRI in this version of the task. These behavioral data
indicate a potential role for spatial working memory in the
performance of our behavioral procedures and allowed us to
examine neuronal activity in mPFC to look for evidence of
encoding of the spatial choice, especially during the delay
period.
To assess behavioral differences as a potential source of
variability in neuronal response patterns, we also compared
performance across task variations and analyzed movement
time preceding left versus right choices. Accuracy and length
of IRIs before correct or error responses did not differ between
versions of the spatial DA task. There were no differences in
the latency to cross the chamber during the choice phase in
either task version, and choice latency did not depend on the
location of the response. Thus it appears that, in general, the
animals perform the two variations of the spatial DA task in a
similar fashion.
Anatomical Distribution of Recording Sites
Most recordings in this study were made in the prelimbic
area (PrL, aka Cg3). Some neurons were recorded in the frontal
association area (FrA, aka medial agranular cortex). A few
neurons were recorded in the more posterior Cg2 region.
Recording sites spanned across the superficial and deep layers
of the cortex. Recordings from the rat implanted with a microdrivable array of electrodes were all in superficial layers of
PrL. Approximate locations of electrode tips are shown in Fig.
2A. Figure 2B depicts the approximate dorso-ventral location
of single units recorded in the rat implanted with the microdrive.
Neuronal Population Activity Is Modulated Around Choice
and Reward
Neuronal population averages from the self-paced and controlled versions of the spatial DA task both showed evidence
for modulation of firing rates around the time of the choice
(Fig. 3A) and around reward collection (Fig. 3C). (Perievent
histograms of behavioral responses are shown in Fig. 3, B and
D, to put the population averages into a behavioral context). In
fact, the overall pattern of firing was similar in animals trained
in the self-paced (N ⫽ 3) and controlled (N ⫽ 2) versions of the
task, with the exception that neurons from the controlled
version showed stronger modulation 1–2 s before the choice
(Fig. 3A). This was likely due to the salience of the Go stimulus
in those animals, which was presented around that time. The
controlled-delay rats were trained to lever press until the Go
stimulus was presented, and this stimulus is known to be
associated with changes in firing rates in the mPFC in this type
of task (e.g., Caetano et al. 2012). Such differences in activity
would not be expected to be differentially affected by choice,
outcome, or pace. The overall pattern of firing was nearly
identical for the two versions of the task around the times of the
choice and the reward (Fig. 3C). The only difference between
the two task variations was that neurons were more sharply
modulated at the end of the delay period in the controlled
variation, because of the visual stimulus that served to trigger
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encoded behavioral information, i.e., when the mean and confidence
bands (bootci.m in MATLAB) exceeded a fraction of 0.05.
Conjunction analysis [i.e., in MATLAB: sum(pChoice⬍0.0125 &
pOutcome⬍0.0125)/length(pChoice)] was used to assess the potential for
neurons to encode multiple task variables. The analysis was performed with results from the nonparametric analyses described above
in three of the 1-s data windows (⫺1 to 0 s, ⫺0.5 to 0.5 s, and 0 to
1 s). A criterion of P ⬍ 0.0125 (Bonferroni correction, P ⬍ 0.05/4)
was used. ␹2-Tests (proportions tests) were applied to assess relative
fractions of cells that were sensitive to each behavioral variable or
conjunction of variables.
To examine firing patterns associated with choice, outcome, prior
outcome, and pace, we used the MATLAB function imagesc.m to
make time-sorted firing rate difference plots. We plotted the normalized differences in spike counts for left vs. right choices (Choice),
correct vs. erroneous responses on the current (Outcome) or preceding (Prior) trial, and trials in the upper and lower quartiles for pace
defined by the IRIs (Pace). Neurons were sorted based on the time
of the maximum difference in spike counts (normalized to have a
maximum value of 1).
To examine whether neurons fired more around correct versus
incorrect choices, a preference score (PS) was calculated by taking the
absolute difference in median spike counts (SC) from trials with
correct versus incorrect responses during the 1-s window prior to the
choice. The raw preference score was calculated for each neuron
as PSraw ⫽ median(SCC) ⫺ median(SCE), with MATLAB notation.
The normalized preference score was calculated for each neuron as
PSnorm ⫽ [median(SCC) ⫺ median(SCE)]/[median(SCC) ⫹ median(SCE)]. We examined the distribution of the preference scores using
histograms and defined outcome-sensitive neurons as those with
preference scores greater than the median[abs(PSraw/norm)]. The preference scores were useful for assessing the fractions of neurons that
fired more prior to errors compared with prior to correct responses
(i.e., neurons below the median score fired more on error trials).
Fractions of these preference scores were compared for correctpreferring and error-preferring neurons with a proportions test.
To examine whether outcome-preceding activity reflected differences in left and right choices in the task, we used the same metric,
defined for the difference in median spike counts on trials with correct
left and right choices: PSnorm ⫽ [median(SCL) ⫺ median(SCR)]/
[median(SCL) ⫹ median(SCR)]. We plotted histograms of the distributions of this metric and compared the distributions of outcomesensitive and outcome-insensitive neurons, using the KolmogorovSmirnov test.
PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
C
Population averages
1
0
Self-paced (N=91)
Controlled delays (N=92)
−1
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2
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Z score
A
Population averages
0
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B
D
Enter reward port
Travel to reward port
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4
2
4
Behavior from one rat
Enter choice ports
Lick spout
2
4
Time from choice (s)
−4
Travel to reward port
−2
0
Time from reward (s)
Fig. 3. Neuronal population activity around the choice and the reward. A: neuronal population averages around the time of the choice. The overall pattern of firing
was similar in animals trained in the self-paced (N ⫽ 3) and controlled (N ⫽ 2) versions of the task. B: behavioral data from 1 of the rats, showing the distribution
of when the rat entered the reward port and licked on the spout in relation to the time of the choice. C: neuronal population averages around the time of the reward.
The overall pattern of firing was nearly identical in animals trained in the 2 versions of the task. One difference between the task variations was the visual stimulus
that was triggered by lever pressing at the end of the delay period. This stimulus served as a sort of “Go” cue (see Caetano et al. 2012), and neuronal activity
was more sharply modulated around the time of the stimulus in the animals tested in the controlled version of the task. D: behavioral data from the same rat in
A, showing the distribution of when the rat entered the choice ports and licked on the spout in relation to the time of the reward.
rapid approaches to the choice ports. Given the overall similarity among neuronal activity in the two task variations, data
from all animals in the study were combined for the analyses
reported below.
Neurons in Medial Prefrontal Cortex Encode Choice,
Outcome, and Temporal Information
Neurons exhibiting choice-related activity were found
around the time of choice port entry (Fig. 4A) and near the time
of the first lick (Fig. 4D). A large fraction of neurons (⬎20%)
in rat mPFC were sensitive to choice (left vs. right) as rats
entered and then departed from the choice ports (Fig. 4B).
None of these neurons fired throughout the analysis epoch, but
they showed sequential onsets and offsets of differential firing
patterns (Fig. 4C) that together spanned the choice period. By
contrast, the fraction of choice-sensitive neurons dwindled
during the time between the choice and the reward collection,
with choice-related firing effectively disappearing at the time
of reward collection (Fig. 4E). None of the neurons fired at
distinct rates throughout the period of reward consumption
based on the preceding choice (Fig. 4F).
Many neurons were also differentially modulated by the
outcome of the current trial around the time of choice and
reward collection (Fig. 5). A small but significant fraction
(⬃10%) of neurons encoded correct versus incorrect choices
prior to the time of choice port entry (Fig. 5C, before 0 s),
indicating prospective encoding of outcome. Examples of prospective outcome-encoding neurons are shown in Fig. 5A
(Effect of outcome, neurons 1 and 2). To examine whether the
error trials reflected a “miscoding” of the forthcoming choice,
we plotted the spike activity of these neurons for correct trials
only and sorted the trials by the location of the choice (left or
right; Fig. 5A: Effect of choice, neurons 1 and 2). Both neurons
fired at low, equivalent rates during entries into the two ports,
indicating that the differences in encoding prior to an error
versus a correct response were not the consequence of a
miscoding of choice.
The fraction of outcome-sensitive neurons increased substantially after the choice was made and feedback (e.g., change
in illumination) was given (Fig. 5C, after 0 s), reflecting
retrospective encoding of outcome. Examples of neurons retrospectively encoding trial outcome are shown in Fig. 5B
(neurons 3 and 4). None of these outcome-sensitive neurons
fired at distinct rates throughout the period of the choice based
on the outcome of the trial, either before or after the choice
(Fig. 5D). Approximately 20% of neurons were differentially
modulated by outcome throughout the period surrounding
reward collection (Fig. 5E). While most neurons fired selectively after correct and incorrect responses for no more than 1
s, some neurons did fire persistently during this period of the
task (Fig. 5F; arrowheads near 60 and 140 on the y-axis denote
persistently firing neurons). More neurons in the population
fired at a higher rate prior to errors versus correct responses
(Fig. 5, G and H; proportions test: ␹2 ⫽ 7.9, P ⬍ 0.001 for raw
scores; ␹2 ⫽ 5.4, P ⬍ 0.03 for normalized scores). The
histograms in Fig. 5G show the distributions of raw (Fig. 5G,
top) and normalized (Fig. 5G, bottom) preference scores that
were calculated as the difference between median spike counts
in a 1-s window before the time of the choice on trials with
correct and error responses. Neurons to the left of the median
(Fig. 5G, dashed lines) fired more to errors versus correct
responses. Neurons to the right of the median fired more to
correct responses. Figure 5H shows the relative proportions of
neurons that fired more prior to correct versus error trials.
A similar metric (defined in METHODS) was used to examine
preferences for left and right choices by the outcome-sensitive and
outcome-insensitive neurons, as defined by the preference score
metric shown in Fig. 5G. The distributions of these preference
scores are shown Fig. 5I. Some outcome-sensitive neurons fired at
distinct rates before left and right choices. Others, such as the two
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−4
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Time from reward (s)
Behavior from one rat
Lick spout
Fluid delivered in reward port
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PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
B
Choice ports
Right
Right
0
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0
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1
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1
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Time of choice (s)
F
ranksum(SCL, SCR)
0
abs(median(SCL) - median(SCR))
180
0.4
140
0.3
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100
1
60
0.5
0.1
20
0
0
−1
0
1
-2
Time of reward (s)
0
2
Time of reward (s)
Fig. 4. Neurons encoded the choice made at the end of the delay period. A: example of a neuron that fired more when the rat entered the left choice port compared
with the right port. Spike rasters are shown at top, and the average firing rates (0.1-s bins) on trials with left and right choice are shown at bottom. The neuron
fired at a tonically elevated rate throughout the choice epoch, starting from the end of the delay period. B: fraction of neurons that was sensitive to the spatial
location of the choice when rats entered the choice ports. The fraction of cells with P ⬍ 0.05 (with Bonferroni correction, P ⬍ 0.0125) from a rank sum test
of spike counts(SC) in a sliding 1-s window (steps of 0.1 s) is depicted by the black line. The 95% confidence interval is shown as gray shading. C: normalized
difference in spike counts around the time of choice port entry for all neurons (1 per row) recorded in the 5 rats. None of the neurons fired at distinct rates
throughout the period of the choice. Each pixel represents the difference in spike counts in a 0.1-s bin and was calculated as abs[median(SCL) ⫺ median(SCR)].
Differences in spike counts are sorted by the time of maximum difference in spike counts on left and right trials. D: example of a neuron that fired more when
the rat entered the reward port after responding in the left choice port. E: fraction of neurons that was sensitive to the spatial location of the preceding choice
when rats entered the reward port. F: normalized difference in spike counts based on the location of the preceding choice during the period of travel to the reward
port and the initial period of reward consumption. As in C, none of the neurons fired at distinct rates throughout the period of reward consumption based on the
preceding choice.
neurons shown in Fig. 5A, fired at equivalent rates prior to the
choice. Overall, there was no difference in the preference
scores for choice from the subpopulations of outcome-sensitive
and outcome-insensitive neurons (Kolmogorov-Smirnov test
statistic: 0.0778, P ⬎ 0.9).
Remarkably, there were neurons present in mPFC that fired
differentially depending on the outcome of the previous trial.
That is, neuronal firing around the time of choice (Fig. 6A) or
reward collection (Fig. 6D) reflected whether the previous trial
had been correct or incorrect, regardless of the current response. A small but significant fraction of the neuronal population (⬃10 –20% of all recorded neurons) fired in this manner.
Several neurons (Fig. 6A and those denoted by arrowheads in
Fig. 6C) fired persistently at distinct rates during the time of the
choice following the encoded outcome. Most, however, fired
briefly, with outcome-dependent firing occurring sequentially
across multiple neurons around both the choice and the reward
collection (Fig. 6, C and F).
As can be seen in Fig. 1D, IRIs varied across trials either
because of variation in the rat’s self-determined pace or because of the constraints on responding built into the controlled
version of the task. Interestingly, there were a number of
neurons with firing rates that reflected the time that had passed
since the previous choice (see examples around choice port
entry and reward collection in Fig. 7, A and D, respectively).
Such neurons comprised a small but significant portion of the
total neuronal population (Fig. 7, B and E), with differences in
firing appearing in sequential neurons during the course of the
trial (Fig. 7, C and F).
Choice, Outcome, and Temporal Information Are Segregated
over Neurons and Phases of the Task
A conjunction analysis was used to summarize the fractions
of cells that were sensitive to choice, outcome, and temporal
information within and between each phase in the task (response and reward ports) and to examine whether neurons
encoded information about single or multiple behavioral variables. We chose to compare neuronal selectivity in two clearly
defined task epochs: during three 1-s windows preceding,
encompassing, and following either 1) entry into the choice
ports, or 2) the start of reward consumption (Fig. 8C). These
task epochs showed nearly identical population activity in
animals tested in the self-paced and controlled-delay variations
of the task (Fig. 3). Over all conjunctions of events, more
neurons were sensitive to the individual task variables com-
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Right
Left
Fraction of neurons (N=183)
E
Left
20
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Time of choice (s)
Right
Spikes per s
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0
Reward port
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abs(median(SCL) - median(SCR))
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Time of choice (s)
D
C
ranksum(SCL, SCR)
Neuron
Spikes per s
Left
Left
30
Fraction of neurons (N=183)
A
Neuron
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PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
Prospective encoding of outcome at choice ports
Effect of outcome
B
Effect of choice
2
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ranksum(SCC, SCE)
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Neuron
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H
+/- median
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Normalized
20
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X =7.9, p<0.001
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Error
> median
< median
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Choice ports
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Raw
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Fraction of neurons greater or less
than abs(median( SCC − SCE ))
Neuron
1
abs(median(SCC) − median(SCE))
0
Neuron
0
Correct trials only
Time of choice (s)
G
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Time of choice (s)
D
Choice ports
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X =5.4, p<0.03
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Sensitivity to Choice (-1 to 0 sec before port entry)
20 Outcome sensitive neurons
10
0
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(SCL − SCR) / (SCL + SCR)
1
20 Outcome insensitive neurons
0.1
0
Correct
Error
> median
< median
10
0
−1
−0.5
0
0.5
(SCL − SCR) / (SCL + SCR)
1
Fig. 5. Neurons encoded trial outcomes, both prospectively and retrospectively. A: examples of neurons that fired at distinct rates at the choice ports on trials
with correct and incorrect responding. Spike activity is shown for 2 simultaneously recorded neurons. In plots on left, trials were scored as correct or incorrect
independent of the location of the choice. Neuron 1 fired persistently at a higher rate during the period before incorrect entries into the choice ports compared
with correct port entries. Neuron 2 fired at a higher rate immediately prior to the incorrect port entries and also fired irregularly after the outcome was revealed,
during the period when the rat traveled to the reward port. To examine whether the error trials reflected a “miscoding” of the forthcoming choice, in plots on
right we plotted the spike activity for correct trials only and sorted the trials by the location of the choice (left or right). Both neurons fired at low, equivalent
rates during entries into the 2 ports, providing evidence that neurons in the mPFC can prospectively encode trial outcomes prior to the rat’s choice. B: examples
of neurons that fired differently after correct and incorrect responding. Neuron 3 fired more spikes after incorrect responses were made. Neuron 4 fired more after
correct responses were made. Feedback about the trial outcome was given at a latency of 0.04 s. (Note for A and B: Rasters for effects of outcome were sorted
by the travel time to the reward port. Rasters for the effects of choice were plotted in the observed trial orders.) C: fraction of neurons that was sensitive to the
trial outcome when rats entered the choice ports. D: normalized difference in spike counts around choice port entry for trials with correct and incorrect responses.
As in Fig. 4C, none of the neurons fired at distinct rates throughout the period of the choice based on the outcome of the trial, neither before nor after the choice.
Many neurons showed differences in spike counts immediately after feedback was given (0.1– 0.5 s after port entry). E: fraction of neurons that was sensitive
to the trial outcome when rats entered the reward port. About 20% of neurons were sensitive to outcome throughout this period. F: normalized difference in spike
counts around choice port entry for trials with correct and incorrect responses. While most neurons fired selectively after correct and incorrect responses for no
more than 1 s, some neurons did fire persistently during this period of the task (arrowheads near 60 and 140 on the y-axis). G: distribution of preference scores
for correct and error trials based on raw (top) and normalized (bottom) spike counts for the database of 183 neurons. The median values of the absolute preference
scores (shown as dashed lines) were used to characterize whether the cells’ firing rates were sensitive to the trial outcome before the choice was made. H: the
fractions of cells that showed differences in spike counts that were greater than the median value of the absolute difference in spike counts for the correct and
error trials are summarized. Results are summarized as fractions based on raw (left) and normalized (right) measures of activity. For both measures of activity,
significantly more cells fired more spikes prior to incorrect responses compared with correct responses based on a proportions test. I: the distributions of
preference scores for left and right choices on correct trials were similar for the subpopulations of outcome-sensitive (top) and outcome-insensitive (bottom)
neurons.
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Fraction of neurons (N=183)
C
Spikes per s
0
-2
40
Neuron
2
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Neuron
1
Fraction of neurons (N=183)
0
Left
Right
Fraction of neurons greater or less than
abs(median( (SCC − SCE ) / (SCC + SCE ))
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Spikes per s
Spikes per s
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Correct
Error
Error
4
Left
Error
10
Effect of outcome
3
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1
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2
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1
Retrospective encoding of outcome at choice ports
Neuron
A
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PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
Spikes per s
Post-Cor Post-Err
Post-Cor
4
0
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Post-Err
0
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Time of reward (s)
1
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ranksum(SCpC, SCpE)
0
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F
0.4
abs(median(SCpC) - median(SCpE))
180
140
0.3
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100
1
60
0.5
0.1
20
0
−1
0
1
0
-2
Time of reward (s)
0
2
Time of reward (s)
Fig. 6. Neurons encoded the outcome of the preceding trial. A: example of a neuron that fired more spikes if the preceding response was correct (Post-Cor)
compared with if it was an error (Post-Err). B: fraction of neurons that was sensitive to the outcome of the preceding trial during the period when rats entered
the choice ports. C: normalized difference in spike counts around choice port entry for trials that were preceded by correct and incorrect responses. A few cells
fired persistently at distinct rates throughout the period of choice (cell in A and those indicated by arrowheads in C). D–F: same plots as A–C for the period of
reward port entry and fluid consumption.
pared with those that were sensitive to conjunctions of the
variables (␹2-test, P ⬍ 0.01). The only conjunction that was
above the fraction expected by chance (i.e., product of fractions of Choice and Outcome neurons) was the 14.75% of cells
that were jointly sensitive to Choice and Outcome during the
choice phase of the task (gray asterisk in Fig. 8D, bottom).
Notably, the fraction of neurons that were sensitive to Choice
was significantly less when rats arrived at the reward port and
consumed fluid compared with when rats responded at the
choice ports and traveled to the reward port (␹2-test, P ⬍ 0.01;
blue asterisks in Fig. 8E, middle and bottom).
Spatial Convergence of Choice-, Outcome-, and PaceRelated Information Within Medial Prefrontal Cortex
Despite the functional segregation of choice and outcome
encoding at the level of single-neuron activity, an anatomical
mapping of neurons revealed a spatial convergence of choice-,
outcome-, and pace-related information within mPFC. This
mapping was possible because of our use of microwire arrays
and our approach to histological processing. We cut brains in
horizontal sections, and this allowed us to localize electrode
tips precisely by following electrode tracts to their terminations. A summary of all recording sites is shown in Fig. 2A. A
summary of sites that contained neurons that were sensitive to
one or more task variables during each epoch is shown in Fig.
9. Some sites (electrodes) allowed for recording more than one
neuron.
DISCUSSION
The goal of this study was to examine both spatial and
nonspatial aspects of mPFC neuronal activity during performance of a spatial DA task that depends on mPFC function
(Horst and Laubach 2009). Recordings in mPFC during task
performance found many neurons that fired at distinct rates as
a function of the spatial location of responding, the outcome of
responding (correct or incorrect) on the current and previous
trials, and the time since the last response (based on the IRI).
Sensitivity of firing rates to the spatial choice was maximal at
the time of the delayed response (Fig. 4), and outcome
sensitivity was maximal after sensory feedback about the
choice was delivered and at the time of reward consumption
(Fig. 5). Outcome-related activity was found both before
and after feedback about success was given, a finding that is
suggestive of a prospective encoding of outcome by mPFC
(Fig. 5), and more neurons showed an increase in firing
when an error was committed compared with a correct
choice (Fig. 5). Neurons also displayed the ability to maintain outcome-related information between trials (Fig. 6) and
could encode behavioral pace (Fig. 7). Choice, outcome, or
temporal sensitivities were segregated over neurons, as few
cells encoded multiple aspects of the task (Fig. 8). Anatomical reconstructions of the electrode recording sites showed
that neurons with distinct behavioral sensitivities could be
recorded from the same electrodes and were distributed
broadly and across layers within mPFC (Fig. 9). These
findings suggest that spatial and nonspatial information is
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Post-Cor Post-Err
-1
Post-Err
Fraction of neurons (N=183)
E
Rasters sorted by time since last reward
0
-2
100
Time of choice (s)
Reward port
Post-Cor
abs(median(SCpC) - median(SCpE))
180
140
0.3
Time of choice (s)
30
C
0.4
0
2
ranksum(SCpC, SCpE)
Neuron
Rasters sorted by time since last reward
D
Spikes per s
B
Choice ports
Neuron
A
Fraction of neurons (N=183)
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PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
100
80
Spikes per s
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After left response
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5 1 2 3
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All correct trials
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log(IRI) sqrt(SC)
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trial
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0.3
0
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Time of reward (s)
-2
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0
Time of reward (s)
Fig. 7. Neurons encoded the time since the last choice was made (i.e., pace). A: a neuron is shown that fired more during left responses compared with right
responses (left). Raster on right shows the spike data collapsed over response ports and sorted by the IRI. A clear effect of the time since the last choice was
made (“pace”) is revealed in this plot (Spearman’s rank-correlation, P ⬍ 0.0125). The 2 plots on right show the IRI associated with each row in the raster plot
and the spike count (SC) from the neuron during the ⫾0.5-s epoch around port entry. B: fraction of neurons that was sensitive to the time since the last choice
was made when rats entered the choice ports. C: normalized difference in spike counts around choice port entry for trials in the upper and lower halves of the
IRI distribution [i.e., Long ⫽ IRI ⬎ median(IRI)]. D: spike activity from a neuron that showed pace-related activity at the reward port. E and F: same plots as
B and C for the period of reward port entry and fluid consumption.
encoded by spatially and temporally distributed patterns of
neuronal ensemble activity.
Spatial Encoding by Medial Prefrontal Cortex
Our recordings support other studies (Baeg et al. 2003;
Chang et al. 2002; Jung et al. 1998; Pratt and Mizumori 2001)
that did not find neurons in mPFC that fired persistently in a
spatially selective manner during the delay period. By constraining the posture and spatial position of the rats, requiring
them to remain within a narrow gap during the delay period,
we were able to reduce the potential for behavioral variability
associated with differences in posture or position on left and
right choice trials. Many neurons (⬎30% of the population)
encoded information about the spatial choice when animals
responded at distinct spatial locations at the end of the delay
period (Fig. 4B). However, many fewer neurons (⬃5% of the
population) encoded spatial information during the delay period (Fig. 4E, after 0 s). These findings support the view that
mPFC is “not involved in the temporary on-line storage but
rather in the control of information required to prospectively
organize the ongoing action” (Gisquet-Verrier and Delatour
2006).
A key finding was based on measuring differences in mean
firing rates on left and right choice trials, sorting neurons by
their peak differences in choice-sensitive firing rates, and
plotting the differences in activity over the neuronal population
(Fig. 4, C and F). These plots resembled those in recent studies
on the hippocampus (MacDonald et al. 2011; Pastalkova et al.
2008) and parietal cortex (Harvey et al. 2012). We interpret
these results as evidence for choice information being encoded
by brief periods of choice-sensitive firing that are sequentially
propagated throughout the neuronal population. We found no
evidence for cells in mPFC firing persistently throughout the
entire delay period in a spatially mnemonic manner.
Nonspatial Encoding by Medial Prefrontal Cortex
Nonspatial processing was the dominant factor influencing
the activity of mPFC neurons during DA. Similar to a recent
study by Hyman and colleagues (2012), trial outcomes had a
major effect on mPFC activity. Reward feedback was encoded
by up to 40% of the population (Fig. 5). Prospective outcomerelated activity was also commonly observed and comprised
10 –20% of the population. Such neurons fired at distinct rates
before rats made errors in the DA task (Fig. 5A). These cells
are reminiscent of the persistently active, outcome-encoding
cells that we have previously found in mPFC during a simple
reaction time task (Narayanan and Laubach 2008). In the
context of the spatial DA task, prospective outcome encoding
does not necessarily reflect the animal’s prediction of the trial
outcome. Instead, it may represent uncertainty about whether
the forthcoming response will yield reward. This is supported
by the observation that behavior (and most likely the neuronal
activity underlying behavior) is more variable prior to an
incorrect versus a correct response (Horst and Laubach 2009).
The greater prevalence of increased firing prior to errors versus
correct responses may reflect the increased demands on prefrontal cortex for selecting an appropriate response in the face
of an uncertain outcome.
A second novel finding with regard to nonspatial processing
in mPFC is based on a subpopulation of neurons (⬍20%) that
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80
15
10
5
0
C
Time of choice (s)
Time of choice (s)
100
15
10
5
0
rankcor(SC, IRI)
0.5
Neuron
B
log(IRI) sqrt(SC)
Neuron
All correct trials
Right choice port
Fraction of neurons (N=183)
Left choice port
Fraction of neurons (N=183)
A
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PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
A
B
Choice ports
0.5
Choice
0.3
Prior
Pace
0.1
0
−1
0.4
−1 to 0 s
0
0.4
0.3
Choice
Prior
Pace
0.1
−1
0
E
−1 to 0 sec before choice
C
O
pO
P
C+O C+pO C+P O+pO O+P pO+P
−0.5 to 0.5 sec around choice
C
O
pO
P
0 to 1 sec after choice
*
0.2
0
C+O C+pO C+P O+pO O+P pO+P
Fraction of neurons
Fraction of neurons
0.4
0.4
0
1
−1 to 0 sec before reward
0.2
0.2
0
−1
Time of choice / reward (s)
C
O
pO
P
0
0.4
0.2
0
0.4
0.2
0
C+O C+pO C+P O+pO O+P pO+P
C
O pO
P
C+O C+pO C+P O+pO O+P pO+P
−0.5 to 0.5 sec around reward
*
C
O
pO
P
C+O C+pO C+P O+pO O+P pO+P
0 to 1 sec after reward
*
C
O
pO
Behavioral variables
P
C+O C+pO C+P O+pO O+P pO+P
Behavioral variables
Fig. 8. Segregated encoding of choice, outcome, and pace by single mPFC neurons. A and B: fractions of neurons sensitive to choice (blue), outcome (gold),
preceding outcome (purple), and pace (green) around the time of choice (A) and reward (B). These data are the same as in the individual summaries in Figs. 4 –7
and are shown together here to allow for comparison of the time courses of encoding across variables. C: to assess the degree of overlap in the encodings of
the behavioral variables, a conjunction analysis was carried out using 3 of the 1-s data windows. D and E: conjunction analysis at the choice (D) and reward
(E) ports. Fractions of neurons that were sensitive to each variable are shown on left (C, Choice; O, Outcome; pO, prior Outcome; P, Pace). Degree of overlap
for pairs of behavioral variables is shown on right (e.g., C⫹O ⫽ Choice and Outcome).
showed variability in firing rates associated with the time since
the last trial, a measure that we describe as “pace” (Fig. 7).
These cells were distinct from the subpopulations that encoded
choice- and outcome-related information, as few pace-related
neurons (⬍5%) were sensitive to these other variables (Fig. 8,
D and E). As pace-sensitive neurons accounted for temporal
variability in task performance, they might have a role in the
maintenance of working memory or could reflect changes in
Sensitivity to Choice
A
Reward period Choice period
motivation over the duration of the trials. In a related reversible
inactivation study (Horst and Laubach 2009), we found that
behavioral variability was altered after infusions of muscimol
into mPFC but not into orbital and insular areas in the lateral
frontal cortex. Previous lesion studies in monkeys (Chen et al.
1995; Thaler et al. 1995) and stroke studies in human beings
(Stuss et al. 2003) have also reported changes in temporal
variability following impairments of mPFC function. To our
Sensitivity to Pace
Sensitivity to Outcome
B
Reward period Choice period
C
Reward period Choice period
Overlap of variables
D
Reward period Choice period
1
2
3 variables
Fig. 9. Anatomical convergence of neurons encoding choice, outcome, and pace. The locations of cells with sensitivities to choice, outcome (collapsed over the
current and prior trial), and pace are shown with horizontal sections through the mPFC in A–C. In each plot, electrodes where at least 1 cell with a specific type
of sensitivity was recorded are depicted as black dots. Electrodes with neurons that encoded other task variables are depicted as white dots. Neurons that encoded
the task variables during the reward and choice periods are shown in the left and right hemispheres, respectively. (Note that the neurons were not actually located
explicitly in these hemispheres.) D: summary of sensitivities to 1 or more task variables. Electrodes with neurons that were sensitive to 1 variable are depicted
as light gray. Those with sensitivity to 2 variables are shown as dark gray. Those with sensitivity to 3 variables are shown as black. Overall, there was no clear
segregation of cells with distinct behavioral sensitivities (e.g., choice-encoding cells were found across regions and layers). Instead, there was a commingling
of cells with sensitivities to choice, outcome, and pace within the mPFC.
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0.4
1
Time of reward (s)
0.2
0
0 to 1 s
Outcome
0.2
0
1
−0.5 to 0.5 s
Time of choice (s)
D
Windows for conjunction analysis
N=183
Outcome
Fraction of neurons
Fraction of neurons
N=183
0.4
0.2
C
Reward port
0.5
PERFORMANCE MONITORING DURING SPATIAL DELAYED ALTERNATION
knowledge, the present study is the first to report pace-related
encoding in mPFC during delayed response performance.
Functional Significance of Spatial and Nonspatial
Processing by mPFC
ACKNOWLEDGMENTS
We thank the Instruments Shop at the John B. Pierce Laboratory for
technical support throughout this project, Nandakumar Narayanan for assistance with surgeries, and Trevor Bekolay, Marcelo Caetano, and Hannah
Clarke for helpful comments on this manuscript.
Present address of N. K. Horst: University of Cambridge, Cambridge,
United Kingdom.
GRANTS
This research was supported by National Institutes of Health (NIH) Grant
2-T32-NS-007224 (N. K. Horst), the Kavli Foundation (M. Laubach), and NIH
Grant P01-AG-030004-01A1 (M. Laubach).
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: N.K.H. and M.L. conception and design of research;
N.K.H. performed experiments; N.K.H. and M.L. analyzed data; N.K.H. and
M.L. interpreted results of experiments; N.K.H. and M.L. prepared figures;
N.K.H. and M.L. drafted manuscript; N.K.H. and M.L. edited and revised
manuscript; N.K.H. and M.L. approved final version of manuscript.
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