| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Arizona Research Labs, Division of Neural Systems, Memory and Aging and Department of Psychology, University of Arizona, Tucson, Arizona
Submitted 9 February 2007; accepted in final form 14 May 2007
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
; Groenewegen et al. 1997). The mPFC is therefore situated in a unique position to integrate sensory and emotional information for the organization of adaptive and goal-directed behavior. Central to such behavior is the capacity to learn associations and contingencies among stimuli, actions, and outcomes. Evidence from lesion studies suggests that the mPFC may be critical for such learning (Balleine and Dickinson 1998; Cardinal et al. 2003; Parkinson et al. 2000). Furthermore, this learning may be facilitated by the strong input the region receives from various neuromodulatory centers such as the nucleus basalis, raphe nucleus, ventral tegmental area (VTA), and the locus coeruleus (reviewed in Robbins 2000). Neurons in these regions respond preferentially to stimuli that consistently predict reward (e.g., Aston-Jones et al. 1997; Bouret and Sara 2004; Schultz 1998; Wilson and Rolls 1990). Furthermore, the neuromodulators associated with these regions are all capable of facilitating associative neural plasticity (e.g., Baskerville et al. 1997; Hotte et al. 2005; Izumi and Zorumski 1999; Jay et al. 2004; Matsuda et al. 2006). It was hypothesized that the action of such neuromodulatory signals will facilitate the formation of associative connections between neurons that respond to sequentially presented stimuli that reliably predict reward. In contrast, associative connections should not form between stimuli that are consistently paired but do not reliably predict reward. This follows from the observation that neurons in most neuromodulatory systems do not respond to frequently presented stimuli that are not associated with reinforcement. It was further hypothesized that the strength of association would be indicated by the degree to which the presentation of the first stimulus of the sequence triggered the representation of the paired associate (e.g., pattern completion). Therefore an appropriate measure of the strength of the association would be the degree to which the delay activity between the two stimuli was selective for the anticipated stimulus. Selective delay activity in the prefrontal cortex has been frequently reported in the primate (Fuster and Alexander 1971; Kubota and Niki 1971) and, more recently, in the rat (Baeg et al. 2003; Batuev et al. 1990; Narayanan and Laubach 2006; Sakurai and Sugimoto 1986). To test this hypothesis, activity from multiple, simultaneously recorded single neurons in the rat mPFC was recorded as animals performed a discrimination task in which sequentially presented stimuli and rewards were delivered according to a probabilistic schedule. The task involved three conditions. In the "predictive" condition, the first stimulus of a two-stimulus sequence predicted the delivery of the second stimulus with a probability of 0.5, whereas the second stimulus predicted the delivery of reward with a probability of 1.0. In this condition, the absence of the second stimulus indicated that no reward would be presented. In the "nonpredictive" condition, the first stimulus also predicted the second stimulus with a probability of 0.5; however, the presence or absence of the second stimulus was entirely random with respect to reward delivery (P = 0.5). In the "never-rewarded" condition the stimulus–stimulus contingencies were identical to the previous two conditions, but no rewards were ever delivered. It was hypothesized that activity during the delay interval that separated the first and second stimuli would be more selective for the anticipated CS2 stimulus in the predictive condition relative to the nonpredictive condition.
This hypothesis was evaluated by using a go/no-go protocol with discrete stimuli and delay intervals. Furthermore, animals were required to maintain their nose within a small nosepoke hole for the duration of a trial. This requirement was adopted to minimize the potential contribution of sensorimotor effects to delay activity. The potential contribution of such effects to delay activity was identified in some of the earliest studies of the primate prefrontal cortex (Kubota and Niki 1971). The difficulty of disambiguating sensorimotor responses from memory-guided activity has been a persistent challenge to the study of delay activity (reviewed in Wang 2005) and it is a challenge that has not necessarily been completely resolved for any experimental paradigm.
The results of this investigation suggest that responses during delay periods were indeed selective to predictive stimulus sequences; however, these responses were often associated with the sensory or motor consequences of slight variations in head position. This result was surprising, considering the restrictions imposed on the animal's position. The variations in head position were also selective to specific stimulus sequences and thus may have been the result of a behavioral strategy used by the animal to solve the task. Such a strategy might obviate the need for an intrinsically sustained memory, at least in such highly overtrained contexts. The observed sensitivity of mPFC neurons to slight changes in head position suggests that, in the absence of precise measurement and analysis, the contribution of sensorimotor information to putative memory-driven delay activity may be seriously underestimated. Although it is conceivable that the observed behaviors served no adaptive function, it is also possible that the behaviors formed a component of an "embodied" strategy used to solve short-interval delay tasks.
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
Neurophysiological studies were conducted on three Brown Norway/Fisher 344 hybrid male rats between 14 and 20 mo old. The rats were housed individually and maintained on a 12:12 light–dark cycle. Recordings took place during the dark phase of the cycle. Surgery was conducted according to National Institutes of Health guidelines for rodents and approved IACUC protocols. Before surgery, the rats were administered bicillin (30,000 units intramuscularly in each hind limb). The rats were implanted, under isoflurane anesthesia, with an array of 14 separately movable microdrives ("Hyperdrive"). This device, general implantation methods, and the parallel recording technique have been described in detail elsewhere (Gothard et al. 1996). Briefly, each microdrive consisted of a drive screw coupled by a nut to a guide cannula. Twelve guide cannulae contained tetrodes (McNaughton et al. 1983; Recce and O'Keefe 1989; Wilson and McNaughton 1993). Each tetrode consisted of four polyimide-coated nichrome wires 14-µm diameter, electroplated with gold to a final impedance of 300–1,000 k
(Kanthal Palm Coast, Palm Coast, FL). Two additional tetrodes with their individual wires shorted together served as an indifferent reference and an EEG recording probe.
Two craniotomies were drilled over the right and left mPFCs (±1.3 mm mediolateral, +3.2 mm anteroposterior) and 1.5-mm craniotomies were drilled over the left and right hippocampi (±2.0 mm mediolateral, –3.0 mm anteroposterior to bregma). A hyperdrive was cemented in place over either the right or the left prefrontal craniotomy. The drive was implanted at a 9° angle toward the midline (see Fig. 1). The unused craniotomy was sealed with KwikSil (World Precision Instruments, www.wpiinc.com) and covered with dental acrylic. EEG probes (coated diameter of 0.0045 in., 300-k impedance; 316SS3T; twisted pair Teflon-coated stainless steel wire; Medwire, Mt. Vernon, NY) were lowered into the hippocampal craniotomy contralateral to the hyperdrive implant. The EEG electrode was lowered into the hippocampal fissure [2.7 mm dorsoventral (DV)]. Data from the hippocampal EEG were not analyzed for this study. An EMG electrode was also implanted into the neck muscle (coated diameter of 0.0045 in.; 316SS3T; Teflon-coated stainless steel wire; Medwire). The implant was cemented in place with dental acrylic anchored by dental screws. After surgery, rats were given ibuprofen (1 ml/1 kg of children's Motrin, McNeil PPC). They also received oral ampicillin (Bicillin, Wyeth Laboratories, Madison, NJ) on a 10-days-on/10-days-off regimen for the duration of the experiment. In two animals, after 3 mo of successful recording, the unused craniotomy was opened and implanted with a new hyperdrive.
|
Tetrodes were lowered after surgery into a region just above the anterior cingulate cortex (
1.3 mm DV), allowed to stabilize for 1 mo, and then gradually advanced. Analysis was restricted to cells located between 2 and 4 mm DV. The neutral reference electrode was located in the superficial layers of the cortex (0.5 mm from brain surface). The four channels of each tetrode were each attached to a separate channel of a 50-channel unity-gain headstage (Neuralynx, Tucson, AZ). A multiwire cable connected the headstage to digitally programmable amplifiers (Neuralynx). The spike signals were amplified by a factor of 1,000–5,000, band-pass-filtered between 600 Hz and 6 kHz, and transmitted to the Cheetah Data Acquisition system (Neuralynx). Signals were digitized at 30 kHz and events that reached a predetermined threshold were recorded for a duration of 1 ms. Spikes were sorted off-line on the basis of the amplitude and principal components from the four tetrode channels by means of a semiautomatic clustering algorithm (KlustaKwik, authored by K. D. Harris, Rutgers–Newark). The resulting classification was corrected and refined manually with custom-written software (MClust, authored by A. D. Redish, University of Minnesota; Waveform Cutter, authored by S. L. Cowen, University of Arizona), resulting in a spike-train time series for each of the well-isolated cells (0 to 16 cells per tetrode). No attempt was made to match cells from one daily session to the next and therefore the numbers of recorded cells reported do not take into account possible recordings from the same cells on consecutive days. EEG signals were band-pass filtered between 1 and 300 Hz and sampled at 2.4 kHz. The EEG signals were amplified on the headstage with unity gain and then again with variable-gain amplifiers (
5 K).
Apparatus
All behavior was performed in a custom-designed operant chamber (see Fig. 2 A). The chamber consisted of a narrow platform surrounded by four speakers. Two cell-phone vibration motors, mounted on flexible aluminum plates, rested on either side of the animal. Trials were initiated when the animal placed its nose in a nosepoke hole (2-cm diameter) located to the front and right of the animal. Nosepoke entry and withdrawal were identified by an infrared emitter-detector mounted within the hole. An automatically controlled aluminum door was located in front of the animal. Liquefied rat mash food reward was delivered by an automatically controlled dipper located behind the door. The apparatus was controlled by a microcontroller (BasicX-35, NetMedia) using custom software written in the BasicX programming language (BasicX, NetMedia).
|
1-mm resolution). Tracking information was collected at 60 frames/s. Behavioral protocol
To examine how reinforcement contingencies can influence associative plasticity, it was important to develop a task that required animals to distinguish among a variety of stimuli and reinforcement schedules. A diagram of the overall organization of the task and the various reinforcement and stimulus delivery schedules is presented in Fig. 2. In general, the task was a go/no-go discrimination task that required animals to distinguish between stimuli presented sequentially as the animals maintained their noses within the nosepoke hole (see Supplementary Table 1 for a summary of the specific stimuli used in each sequence).1 Reward was delivered at the end of the stimulus sequence according to the schedule described in Fig. 2 (bottom). There were three experimental conditions in the task and two unique stimulus sequences were assigned to each condition so that stimulus selectivity might be assessed within condition. In the "predictive" condition, reward was always delivered if the second stimulus was presented and the animal's nose remained within the nosepoke hole until the "go" signal. In the "nonpredictive" condition, reward was delivered randomly with respect to the delivery of the second stimulus. In the "never-rewarded" condition, reward was never delivered. Performance was measured as the percentage of trials in which the animal withdrew from the nosepoke hole (aborting the trial) during conditions in which reinforcement was unlikely and remained within the nosepoke hole in conditions when reinforcement was likely.
Trials began with a variable delay interval (random between 500 and 1,000 ms), termed the "fixation" interval, during which the animal had to remain with its nose within the nosepoke hole ("nose fixation") to receive the first stimulus of the sequence (CS1). The CS1 stimuli consisted of six unique sounds (mixed frequency). These sounds were pulsed in triplets during the CS1 interval (133-ms pulse duration, 100-ms interpulse interval) and presented from the front speaker. The termination of CS1 was followed by a 700-ms delay interval (D1). This interval was followed by either the presentation of the paired second stimulus (CS2), the delivery of a foil stimulus, or the omission of any stimuli (see Fig. 2). The second stimulus was either a pulsating white noise sound presented from the left, right, or rear speaker or a pulsating vibration stimulus delivered from either the left, right, or simultaneously from both vibration motors that rested on either side of the animal (Supplementary Table 1). Stimulus duration was typically 600 ms, during which the stimulus was pulsed three times (133-ms pulse duration, 100-ms interpulse interval). The paired second stimulus was delivered on about 50% of trials and the foil stimulus was delivered on about 17% of trials according to a pseudorandom schedule. No second stimuli were presented on about 33% of trials. Foil stimuli were not unique to any particular stimulus sequence (i.e., they were not paired associates). The second stimulus interval was followed by a second delay interval (D2), which lasted 800 ms. This delay was followed by either the opening of the door that blocked entry into the reward chamber or by a 1.8-s pulsating tone (and no reward) that was delivered 1.2 s after the typical time the door would have opened, had the trial been a rewarded trial. A trial could be aborted at any time if the animal withdrew from the nosepoke hole. Withdrawal before the completion of a trial was not followed by any error signal.
As briefly discussed, the contingency between the delivery of reward and the presentation of the first (CS1) and second (CS2) stimuli was varied in three different conditions (predictive, nonpredictive, and never-rewarded). Reward was delivered in the predictive condition if the paired second stimulus was presented, but reward was omitted if the foil stimulus was delivered or if no stimuli were presented. In the nonpredictive condition, the omission or presentation of any stimuli during the CS2 interval did not influence the probability of reward delivery on a given trial (P = 0.5). In the never-rewarded condition, animals were never rewarded regardless of the stimuli presented in the CS1 or CS2 intervals. Stimulus sequences from the predictive and nonpredictive conditions constituted 80% of all trials, whereas stimulus sequences from the never-rewarded condition constituted 20% of trials. Animals typically completed 90 trials in each predictive or nonpredictive stimulus sequence on a given behavioral session (180 trials per condition). Sequences from the predictive or nonpredictive conditions were presented in blocks of eight trials. The order of the blocks during an experimental session was random. The use of blocks was originally implemented to facilitate training and the original design called for the elimination of blocked trials once behavior improved. From a practical perspective, however, it was observed that animals would not sample trials in the predictive and nonpredictive conditions equally without blocked trials. An analysis of neural activity and animal behavior during trials within each block is presented in Supplementary Fig. 5.
Animals were considered to have learned the task when they successfully performed the following three behaviors: First, they maintained nose fixation during the predictive sequences when the paired-associate CS2 was presented and withdrew from the hole during the presentation of the foil or during the CS2-omission trials. Second, they maintained nose fixation for the duration of the trial during the nonpredictive conditions. Third, they withdrew from the hole during the never-rewarded conditions.
Pretraining
All animals used in this study required a minimum of 5 mo of daily training (2 h/day, 6 day/wk) to reach criterion. Performance was evaluated after each training session by visually inspecting plots similar to the summary plot presented in Fig. 4. Criterion was reached when several conditions were met. First, animals were required to withdraw from the nosepoke hole during 70% of trials in the never-rewarded condition, but maintain nose fixation during 70% of trials in all other conditions. Second, animals were required to maintain nose fixation for the duration of 70% of nonpredictive trials if the paired CS2 stimulus was delivered but to withdraw during the majority (>70%) of trials if the CS2 stimulus was absent (foil or omission). Criterion was achieved only if animals could accurately perform the task with delay intervals (D1 and D2) of at least 700 ms. A total of five animals reached criterion; however, two animals were eliminated because one died from liver cancer and another was omitted because of problems with the microdrive implant. Performance at the time of recording was more accurate than the level set by the criteria described earlier, as a result of the extra training the animals received during the postsurgery recovery period (1 mo).
|
|
1 mo) was followed by the introduction of a second stimulus that followed the auditory cue. The contingency relationship between the second stimulus and reward delivery was also introduced at this stage. Delay intervals and foil stimuli were introduced only after animals could successfully discriminate between stimulus sequences in the predictive, nonpredictive, and never-rewarded conditions (2 mo). Successful discrimination between the stimulus sequences resulted in the introduction of the foil stimuli and delay intervals. This final stage of training typically lasted 2 mo. Surgery was performed once the animals could discriminate between the CS2, omit, and foil stimuli in the predictive condition. Analysis
MEASURES OF SELECTIVITY TO HEAD POSITION AND STIMULUS SEQUENCE. The analysis of behavior revealed the presence of small but sequence-selective variations of head position (see RESULTS). As a result, it was important to determine whether sequence-selective delay activity was a result of the sensorimotor consequences of such postural shifts. To address this issue, two methods were adopted to assess the degree of sensorimotor contribution to selective delay activity. The first approach measured sensitivity to head position as the R2 value of the regression between trial-by-trial values of head position and firing rate during the first delay interval. Only correct trials were included in this analysis. This approach is described in more detail in RESULTS.
The second approach was developed under the assumption that if head position influences stimulus-specific delay activity, then the difference in firing-rate activity between stimulus sequences should be greatest during trials in which the head position was most divergent. This analysis was accomplished by separating trials into groups that depended on the degree to which head position overlapped or diverged between stimulus sequences within either the predictive or the nonpredictive conditions. The measure of selective delay activity was subsequently and independently calculated for each of these two groups.
This method was implemented by segmenting trials into two groups of equal size according to the position of the animal's head during the delay interval. The "diverge" group contained trials in which head position was to the outside of the median of the distributions of head position, whereas trials in the "overlap" group were those trials on the inside of the medians of the distributions (see Fig. 3 for an illustration). Segmentation was performed separately for the two sequences in either the predictive or the nonpredictive conditions. The strength and significance of delay activity within the diverge and overlap groups were compared by using a two-factor ANOVA with stimulus sequence being the first factor ("sequence 1," "sequence 2") and head position ("diverge" or "overlap") being the second. Neurons were considered to be modulated by sensorimotor information if the effect of delay activity was significantly enhanced or reduced respectively as a function of membership to the diverge or overlap group. It should be mentioned that the animal's go/no-go choice performance during the trials in the two groups was identical. As a result, it can reasonably be assumed that a significant difference in delay activity between diverge and overlap groups was not a result of differences in factors such as recall or attention.
CLASSIFICATION OF NEURONS BY SELECTIVE DELAY ACTIVITY. A two-factor ANOVA was used to categorize cells into groups based on the sensitivity of the firing rate to the stimulus sequence (factor 1) or to head position (factor 2). Firing rate was determined by counting spikes on each trial within a 400-ms window during the delay interval (D1) that began 150 ms after the offset of the CS1 interval and ended 150 ms before the onset of the CS2 interval. The trial-by-trial firing rates were evaluated by ANOVA (P < 0.05) and the results of that ANOVA were used to assign cells to one of the following four groups.
The Sequence and Sequence/Position groups could be considered to be the categories most often associated with intrinsically generated (e.g., memory-related) delay activity. In these categories, head position did not significantly influence the selectivity of the delay activity. Membership in the Interaction category, however, would suggest that position does influence selective delay activity for that cell.
MEASURES OF NEURAL SELECTIVITY TO STIMULI, SEQUENCE, AND POSITION. The degree of stimulus selectivity during the delay interval was quantified as the absolute value of the difference between the mean firing rate activity in the two within-condition sequences (e.g., predictive or nonpredictive). An advantage of this measure was its simplicity and ease of interpretation. A disadvantage was the fact that it does not take into account the variance of the two distributions and thus the degree of confidence that the difference was significant. For most analyses, this was not a major concern because neurons were screened for significant selective activity before the analysis of effect size.
Selectivity in the population of simultaneously recorded cells was measured using linear discriminant classification (classify.m in Matlab, The MathWorks). The classifier was trained to discriminate between the two within-condition sequences given the trial-by-cell matrix of spike counts (bin size = 400 ms) associated with a given stimulus epoch (e.g., CS1, D1, CS2, or D2). All population vectors (rows in the matrix) were normalized to have a vector length of one to emphasize relative change in firing activity in the population of cells and eliminate the influence of global changes in rate on the quality of the discrimination. Leave-one-out cross-validation (Bishop 1995) was used to determine a percentage-correct score of the classification. The degree to which these scores deviated from chance (P = 0.5) was interpreted as the strength of selectivity. The significance of the difference was determined by evaluating the table of correct and incorrect classifications (the "confusion matrix") with a chi-square test (P < 0.05). Experimental sessions with fewer than ten simultaneously recorded cells (30% of sessions) were eliminated from the analysis. The degree of prospective and retrospective activity was identified through a similar procedure; however, in this procedure the classifier was trained on either the CS1 or CS2 intervals but tested on the population vectors associated with the delay (D1) interval. Significantly higher percentage-correct performance of the classifier trained on the CS1 interval relative to the CS2 interval was interpreted as suggesting retrospective coding, whereas enhanced performance for the CS2 classifier was interpreted as suggesting prospective coding.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
Accuracy in the sequence-discrimination task was extremely high; average performance across all sessions and animals is summarized in Fig. 4. All animals reliably withdrew from the nosepoke hole during the presentation of the never-rewarded CS1 stimuli, but maintained nose fixation during all other CS1 stimuli. Furthermore, animals maintained nose fixation during the presentation of the paired stimulus in the predictive condition but withdrew during foil and omission trials. In contrast, animals continued to nosepoke during omission, foil, and CS trials in the nonpredictive condition. This behavior was also clearly identifiable in the distribution of withdrawal latencies for each condition (Supplementary Fig. 1) and was consistent across sessions and animals (Supplementary Fig. 2). These results are in clear accord with the reinforcement contingencies adopted in this task (see METHODS, Fig. 2). The differential response to the foil stimuli in the predictive and nonpredictive conditions is particularly interesting. In the predictive condition, the foil stimulus was 100% predictive of the absence of future reward; however, in the nonpredictive condition, the same stimulus had no predictive value. The differential response to the identical foil stimuli in the two conditions suggests that the animals were able to discriminate between the different sequence types and conditions based in predictions developed during the presentation of CS1. Alternatively, the presence of the foil stimulus makes the task context driven in the sense that the identical stimulus had different meanings depending on the preceding stimulus.
In addition to evaluating performance accuracy, head movement and EMG activity were also analyzed. This analysis revealed that the animals expressed patterns of head movement and EMG activity that were associated with specific stimulus sequences. This observation was surprising given the restrictions placed on the physical movement of the animal (e.g., nosepoke-maintenance requirement, narrow platform). Analysis also revealed that these patterns of behavior were most selective for individual sequences in the predictive condition (Fig. 5), suggesting that, on average, the LEDs on the animal's headstage deviated 1 cm (10° of head rotation) between the two sequences.
|
Evaluation of the perievent time histograms (PETHs) for individual neurons identified numerous clear examples of cells with selective activity during the various stimulus and delay intervals as predicted by the hypothesis motivating this study. Almost all of the selective responses were observed during the two predictive sequences. Examples of individual neurons with selective responses are presented in Fig. 6.
|
|
Delay responses
EFFECT OF CONTINGENCY. The majority of the analyses that follow evaluate the neural activity that occurred during the delay interval between the presentation of the CS1 and CS2 stimuli. It was initially hypothesized that strong, stimulus-selective, and prospective activity would be observed during this interval, in which both stimuli in the sequence contribute to the prediction of reward (predictive condition). Examples of cells with selectivity to the stimulus sequence and to the reinforcement contingency relationship are presented in Fig. 6. In these examples, delay responses were selective to sequences in the predictive condition but not the nonpredictive condition. This pattern of response was also observed in the population.
The presence of selective delay activity in the population of recorded cells was evaluated by examining the binned, trial-by-trial firing rates during the various trial conditions. Activity was binned during a 400-ms window in the center of the 700-ms delay period (D1). Comparisons were made between activity during the two paired stimulus sequences, to produce a measure of selectivity. It should be noted that this measure of selectivity is agnostic with regard to whether the activity during the delay interval is prospective (CS2), retrospective (CS1), or some combination thereof. For this reason, selective activity is termed sequence-selective instead of stimulus-selective.
Of the 1,310 neurons included in this study, a total of 248 (19%) exhibited activity during the delay that was selective for specific sequences in the predictive condition. A total of 122 (9%) neurons were selective in the nonpredictive condition (Fig. 7A). The mean effect size, quantified as the absolute value of the difference in firing rate, was also largest in the predictive group [Fig. 7B, Predictive: 1.4 ± 0.25 Hz (mean ± SE), Nonpredictive: 0.7 ± 0.12 Hz, P < 0.05, ANOVA]. Similar results were obtained following the analysis of the population of simultaneously recorded cells (Fig. 7C). The ensemble expressed significant selectivity for individual sequences during D1 and D2 and the degree of selectivity was notably stronger in the predictive condition [P < 0.05, paired ANOVA (factor: epoch) with Tukey–Kramer test for multiple comparisons]. Results from the analysis of single units and the population are in agreement with previous reports that suggest that delay activity in the prefrontal cortex is selective for task-relevant stimuli (Freedman et al. 2001; Rainer et al. 1998); however, as will be discussed, this activity was strongly influenced by sensorimotor information.
PROSPECTIVE AND RETROSPECTIVE CODING. To determine whether the activity during the delay intervals was prospective or retrospective, it was necessary to quantify the degree to which the activity was selective for either the previously presented stimulus or the anticipated stimulus. Simply comparing the delay activity to the activity in the CS1 or CS2 intervals within a given sequence did not address this issue because such a comparison does not account for the selectivity of the response relative to responses to stimuli in other sequences. To address this issue, linear discriminant classification was used to quantify the degree to which delay activity could discriminate between the two within-condition sequences. This analysis was performed separately for the CS1 and CS2 intervals (see METHODS). Higher percentage-correct performance of the classifier trained on the CS1 interval relative to the CS2 interval was interpreted as evidence for retrospective coding, whereas higher values for the CS2 classifier were interpreted as evidence for prospective coding. Results of this analysis are consistent with the presence of prospective coding (Fig. 8) because the percentage correct was significantly higher in the predictive condition (P < 0.001, paired t-test). This result was consistent for both single units and for the population of recorded cells. No significant difference (P > 0.05, paired t-test) was observed during the nonpredictive condition. Put more concisely, the results suggest that in the predictive condition, but not in the nonpredictive condition, the delay activity is significantly more similar to the CS2 activity than to the CS1 activity.
|
Sensitivity to position variation: regression of position and spike rate.
The essential difficulty with the previously described covariation between head position and sequence was the ambiguity surrounding whether neural activity was driven by memory-guided processes or by sensorimotor information. One strategy to address this issue is to restrict the analysis of neural activity and head position to trials associated with an individual stimulus sequence. This approach is advantageous because both the stimuli and the reinforcement contingencies are held constant within a stimulus sequence. As a result, this approach avoids any selective effect that may arise from either memory-guided activity or differences in motivation/expectation. It should be noted that this analysis presents a conservative lower-bound estimate of position sensitivity because it is restricted to a relatively small portion of the total head variation (variation within a sequence).
This analysis was accomplished by performing a multivariate regression on the trial-by-trial firing rate (dependent variable) as a function of position (independent variable). Head position was binned into four 200-ms bins during the 800 ms that preceded the onset of the CS2 stimulus (four variables for the regression). Significance of the full regression model and the R2 value of the regression were determined for each neuron and compared with values obtained from a shuffled control where the trial order of firing rates, but not position, was randomly resorted. The analysis was restricted to only those trials in which the second stimulus was presented and the animal maintained nose fixation for the duration of the trial (e.g., A 
B Reward; see Fig. 2).
Of the 248 neurons that exhibited sequence-selective delay activity, 105 (42%) expressed significant covariation with position during one of the two sequences (P < 0.05). Only 11 (4%) neurons exhibited such covariation in the shuffled control condition. The degree of sensitivity of neurons to head position was quite high during all delay intervals, including the prestimulus fixation interval (Fig. 9 A). Interestingly, the strength of position sensitivity was greatest during the second delay interval of the predictive condition (ANOVA, P < 0.05, Tukey–Kramer test for multiple comparisons) even though the average firing rates of neurons during the predictive and nonpredictive conditions were not different (P > 0.05, ANOVA). In this condition, the animal was presumably certain about future reward delivery because the presence of the second stimulus always indicated that the reward would be delivered. This analysis was also repeated with the rectified EMG data. In contrast to head position, EMG activity was a poor predictor of trial-by-trial variations in firing rate (mean R2 = 0.036 during D1 for EMG compared with R2 = 0.125 for the position data).
|
The relationship between sequence selectivity and the measure of delay activity was further investigated by evaluating the activities of cells with or without a significant correlation with position (P < 0.05 or P > 0.15 for the full regression model between rate and position). The firing rates of cells in these two groups are presented in Fig. 10 A. Position-modulated cells fired at significantly higher rates relative to position-insensitive cells (P < 0.001, ANOVA; factor: position sensitivity). The degree of selectivity, measured as the absolute value of the firing rate differences in the two sequences, was significantly larger in the position-sensitive group (P < 0.0001, ANOVA).
|
For a given neuron, there are three potential outcomes of the analysis. First, selective delay activity may not be affected by segmenting trials into the overlap and diverge groups, suggesting that head position may not influence activity in the cell (e.g., Fig. 11 A). Second, delay activity may be nearly abolished in the overlap group, suggesting that delay was almost entirely a function of sensorimotor information (e.g., Fig. 11B). Finally, delay activity may become enhanced in the overlap condition, suggesting that large deviations in head position between the two sequences may interfere with sequence-selective activity.
|
|
CELL TYPE.
The sensitivity of a cell to a stimulus sequence or to head position could be a function of the cell class (e.g., principal cell or interneuron). To examine this question, cells with selective delay activity were classified into putative interneurons (13% of cells) and into two classes of principal cells [PK10 (32%), PK100 (55%)] by evaluating the waveform shape (Bartho et al. 2004) and the autocorrelogram (see Supplementary Materials and Supplementary Figs. 3 and 4). No difference in the degree of selectivity was identified between cell classes (P > 0.05, Kruskal–Wallis; Supplementary Fig. 4A). In addition, no cell type demonstrated a significant bias toward a particular sequence (P > 0.05, Kruskal–Wallis, Supplementary Fig. 4B). The PK10 neurons did exhibit a significantly lower degree of sensitivity to position (P < 0.05, Kruskal–Wallis); however, this effect may have been attributable to the notably lower baseline firing rate observed for the PK10 neurons (1.0 Hz compared with 4.0 and 8.0 Hz for the PK100 and interneurons, respectively).
DEPTH.
A growing body of evidence suggests that there is a high degree of functional segregation within the mPFC (Corbit and Balleine 2003; Gabbott et al. 2005; Haddon and Killcross 2005; Killcross and Coutureau 2003). As a result, it was important to investigate the relationship between the depth of the electrode and the degree of sequence selectivity and sensitivity to head position. Regression of the measure of sensitivity of cells to position (R2 of the regression of firing rate and head position) versus the depth of the electrode revealed a significant negative correlation (P < 0.01). A significant negative correlation was also observed (P < 0.01) for the regression of sequence selectivity (absolute value of the difference in firing rate) versus depth. These results indicate that both position sensitivity and selective delay activity decrease with increasing depth. Average values of position and sequence selectivity at various depths are presented in Fig. 13.
|
). It is also consistent with functional studies that suggest that ventral regions may be more involved in action selection and action sequencing (e.g., Ragozzino and Kesner 2001). It is conceivable that the effect of depth was influenced by the degree of training that the animal received in the task, in that depth did covary with the number of recording sessions (electrodes were typically lowered, not raised). This seems unlikely given the fact that the animals' performance measures remained relatively constant through the course of the experiment. The animals were overtrained on this task, receiving at least 5 mo of pretraining and maintaining peak performance levels for 
1 mo before recording. |
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
; Batuev et al. 1990; Narayanan and Laubach 2006; Sakurai and Sugimoto 1986). In accord with these hypotheses, prospective delay activity was observed in 19% of neurons in the mPFC, with the majority of responding neurons being located in the dorsal region. In addition, this activity was strongest during sequences in which the first and second stimuli both contributed to the prediction of reward. Although these observations support the original hypotheses, a critical assumption was that the activities of neurons during delay intervals would be driven primarily by mnemonic processes intrinsic to the mPFC. Evaluation of the behavioral and neural data indicated that this assumption was not correct. Analysis of neural activity revealed that 42% of the selective neurons were significantly influenced by slight variations in head position. Furthermore, the head position of the animals varied as a function of the stimulus sequence, suggesting that the animals may not have used intrinsically sustained memory-guided activity to solve the task. Although subsets of neurons in mPFC were extremely sensitive to head position, the degree to which this selectivity accounts for the reported selective delay responses is unclear. For example, it could be argued that the 42% of position-sensitive cells represents a unique subset of neurons that are tuned to sensorimotor parameters, whereas the remaining 58% are driven by the hypothesized stimulus–stimulus associations. It could also be argued that individual neurons in the mPFC combine extrinsic sensorimotor input with stored sensory-sensory associations. A number of factors argue against such interpretations and suggest that sensory–sensory associations may not be strongly stored in the mPFC and that sensitivity to sensorimotor variation may be the principal factor behind the reported selective delay response. First, the estimate of the contribution of sensorimotor activity most likely underestimates its true contribution. This seems likely because only two behavioral parameters (head position, EMG), were measured, ignoring the myriad other behavioral parameters (e.g., movement of the hindquarters, vibrissae, tongue, etc.). Furthermore, the degree of sensitivity to position was significantly correlated with the strength of sequence-selective delay activity (Fig. 9B). In addition, in two separate analyses, the effect size in the group of cells that was modulated by position was twofold as large as the level observed in the unmodulated group (Figs. 10B and 12B), suggesting that the magnitude of selectivity was principally a function of head position. Finally, both selectivity during the delay intervals and position sensitivity covaried strongly with recording depth.
The conclusion that sensitivity to sensorimotor information is a major factor driving mPFC neurons is supported by the results of a number of recent studies. For instance, Euston and McNaughton (2006) observed strong spatial sequence-selective delay activity in the mPFC of the rat as the animal performed an open-field variant of the classic figure-eight delayed alternation test of working memory. After careful analysis of the animal's movement in the task, it was determined that the majority of this activity was a result of sensorimotor parameters related to slight variations in the animal's running path. The present findings extend these results to an experimental paradigm (go/no-go, "fixation," etc.) that shares much in common with traditional tests of delay activity. Also in accord with the results of the present study, Euston and McNaughton identified fewer neurons with selective delay activity or position sensitivity at deeper electrode locations. A study by Gemmell et al. (2002) also reported that neurons in the mPFC appear to be extremely sensitive to sensorimotor information. In that study, neural activity in the anterior cingulate and the dorsal prelimbic cortex was monitored as animals performed a foraging task. Although the authors observed activity that appeared to be related to allocentric spatial location, further analysis revealed that apparent spatial selectivity was principally a function of unique behaviors, such as grooming or rearing, performed at specific locations in the apparatus. Involvement of the mPFC in sensorimotor processing is further supported by the results of studies by Whishaw (1992), which suggest that the mPFC is involved in the control of skilled limb movement, and the results of stimulation studies of the dorsal mPFC by Hall (1974), which produce oculomotor responses.
An unexpected result from the present study was the observation that the sensitivity of neurons to position was greatest during delay intervals in which the animal was presumably certain about the future delivery of reward (Fig. 9A). This observation is in accord with the results of other studies that report that neurons in the mPFC respond during the performance of actions related to future delivery of reward. For example, Chang et al. (1997, 2000) reported that neurons in the mPFC respond selectively to sequential behaviors that lead to the infusion of cocaine, Mulder et al. (2003) observed that delay activity in the mPFC was most sensitive to reward-related behaviors that were triggered by predictive cues, and results from Kargo et al. (2007) suggest that neurons in the far dorsal mPFC (precentral cortex) respond to action–reward contingencies. In the primate, neurons in the dlPFC respond to both anticipated actions and the rewards associated with those actions (Watanabe 1996). Similarly, neurons in the rostral cingulate motor area of the primate appear to respond to the selection of appropriate reward-related actions (Shima and Tanji 1998). These observations suggest that subregions of the PFC may link actions to rewarding outcomes. It is also possible that this function may extend to nonrewarding outcomes. For instance, previous studies in the primate have reported single-unit responses in the anterior cingulate region to errors (Gemba et al. 1986; Niki and Watanabe 1979). A detailed analysis of the error-related responses was not performed in the present experiment given the scarcity of error trials. As a result, future studies will be required to resolve whether neurons in the mPFC of the rat are primarily sensitive to behaviors related to rewarding outcomes or whether neurons in this region respond to actions related to both positive and negative outcomes.
Although neurons in the dorsal mPFC responded to slight changes in behavior, the utility of such behaviors is not entirely clear. It is possible, for instance, that these behaviors served no function. For instance, Skinner (1948) observed that pigeons naturally develop idiosyncratic and repetitive behaviors during delayed-reinforcement tasks, even when reinforcement rates and intervals are fixed and reinforcement is independent of the animal's actions. Alternatively, the observed postural adjustments may indicate an adaptive behavioral strategy, adopted to sense anticipated stimuli (e.g., lean to feel the vibration motor or tilt head to better hear an auditory stimulus). It is also possible that the animal solved the present task by using body posture as a mnemonic cue. For example, at the time of the presentation of the second CS, the animal could compare its current posture with the delivered stimulus and make a decision based on a few simple rules (e.g., leaning left + CS2 stay, leaning left + no CS2 withdraw, leaning left + Foil withdraw). Granted, such decision rules must also be learned and maintained through delay intervals; however, such behavior is likely the product of a gradual learning process acquired through the course of training (Mulder et al. 2003).
There are a number of advantages of using behavioral strategies to solve highly repetitive short-interval delay tasks. For instance, such strategies may be less prone to interference from external stimuli because, once initiated, changes in body position or running trajectory have a physical inertia that is difficult to disrupt aside from physically moving the animal. Furthermore, storing information in the "egocentric" reference frame of the body may be ideal for tasks that have an egocentric component such as delayed-alternation tasks that are often used as tests of working memory in the rat. The usefulness of embodied strategies is also illustrated by evidence that animals use "mediating" behaviors to estimate the duration of delay intervals (Hodos et al. 1962; Wilson and Keller 1953). Furthermore, it has been suggested that animals can and do use the body and environment to link neural computations to an allocentric reference frame to support processes such as working memory (Ballard et al. 1997). Whether the mPFC is necessary for organizing such embodied strategies is an open question and resolving this issue will require future inactivation or lesion studies.
Embodied strategies are clearly not advantageous in all short-term delay tasks. For example, such strategies could easily be disrupted during tasks that utilize long timescales (e.g., >1 min) in which intervening behaviors (e.g., grooming) may disrupt any ongoing behavioral strategy. In such tasks, it may be far more advantageous for memories to be stored through active maintenance processes or through short-term synaptic plasticity; however, almost all published studies of delay activity involve intervals of <1.5 s and most short-duration (<10 s) memory tasks in the rat do not necessarily encourage nonbehavioral strategies (e.g., by disrupting stereotyped behaviors during the delay interval). It is therefore suggested that great care must be taken when interpreting delay responses that appear to be driven by intrinsic mnemonic processes, such as those proposed by the active maintenance hypothesis of prefrontal function.
In conclusion, the reported results suggest that quite subtle changes in head position can result in significant changes in the activity of medial prefrontal neurons. This activity was very similar to selective delay activity and could be easily misinterpreted as memory driven, even in tasks that impose restrictive controls on head and body position. Furthermore, responses were sensitive to the degree of expectancy regarding the future delivery of reward. These findings are in accord with the powerful inputs this region receives from the sensory system and its connection with the motor cortex, basal ganglia, and the VTA. It is also in accord with evidence that suggests the involvement of the anterior cingulate cortex in the organization of goal-relevant behavioral sequences. Finally, and from a technical perspective, the present results suggest that the separation of intrinsic and memory-guided processes from sensorimotor effects poses a significant challenge for the study of delay activity, especially if animals readily develop subtle idiosyncratic behaviors or adopt embodied strategies to solve highly repetitive short-term delay tasks.
|
GRANTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
|
ACKNOWLEDGMENTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
|
FOOTNOTES |
|---|
Address for reprint requests and other correspondence: B. McNaughton, Department of Psychology, The University of Arizona, Tucson, AZ 85721 (E-mail: bruce{at}nsma.arizona.edu)
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONGRANTSACKNOWLEDGMENTSREFERENCES |
|---|
Aston-Jones G, Rajkowski J, Kubiak P. Conditioned responses of monkey locus coeruleus neurons anticipate acquisition of discriminative behavior in a vigilance task. Neuroscience 80: 697–715, 1997.[CrossRef][Web of Science][Medline]
Baeg EH, Kim YB, Huh K, Mook-Jung I, Kim HT, Jung MW. Dynamics of population code for working memory in the prefrontal cortex. Neuron 40: 177–188, 2003.[CrossRef][Web of Science][Medline]
Ballard DH, Hayhoe MM, Pook PK, Rao RP. Deictic codes for the embodiment of cognition. Behav Brain Sci 20: 723–742, 1997.[CrossRef][Web of Science][Medline]
Balleine BW, Dickinson A. Goal-directed instrumental action: contingency and incentive learning and their cortical substrates. Neuropharmacology 37: 407–419, 1998.[CrossRef][Web of Science][Medline]
Bartho P, Hirase H, Monconduit L, Zugaro M, Harris KD, Buzsáki G. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J Neurophysiol 92: 600–608, 2004.
Baskerville KA, Schweitzer JB, Herron P. Effects of cholinergic depletion on experience-dependent plasticity in the cortex of the rat. Neuroscience 80: 1159–1169, 1997.[CrossRef][Web of Science][Medline]
Batuev AS, Kursina NP, Shutov AP. Unit activity of the medial wall of the frontal cortex during delayed performance in rats. Behav Brain Res 41: 95–102, 1990.[CrossRef][Web of Science][Medline]
Bishop CM. Neural Networks for Pattern Recognition. New York: Oxford Univ. Press, 1995.
Bouret S, Sara SJ. Reward expectation, orientation of attention and locus coeruleus-medial frontal cortex interplay during learning. Eur J Neurosci 20: 791–802, 2004.[CrossRef][Web of Science][Medline]
Cardinal RN, Parkinson JA, Marbini HD, Toner AJ, Bussey TJ, Robbins TW, Everitt BJ. Role of the anterior cingulate cortex in the control over behavior by pavlovian conditioned stimuli in rats. Behav Neurosci 117: 566–587, 2003.[CrossRef][Web of Science][Medline]
Chambers JM, Cleveland WS, Kleiner B, Tukey PA. Graphical Methods for Data Analysis. Belmont, CA: Wadsworth & Brooks/Cole, 1983.
Chang JY, Janak PH, Woodward DJ. Neuronal and behavioral correlations in the medial prefrontal cortex and nucleus accumbens during cocaine self-administration by rats. Neuroscience 99: 433–443, 2000.[CrossRef][Web of Science][Medline]
Chang JY, Sawyer SF, Paris JM, Kirillov A, Woodward DJ. Single neuronal responses in medial prefrontal cortex during cocaine self-administration in freely moving rats. Synapse 26: 22–35, 1997.[CrossRef][Web of Science][Medline]
Conde F, Maire-Lepoivre E, Audinat E, Crepel F. Afferent connections of the medial frontal cortex of the rat. II. Cortical and subcortical afferents. J Comp Neurol 352: 567–593, 1995.[CrossRef][Web of Science][Medline]
Connors BW, Gutnick MJ. Intrinsic firing patterns of diverse neocortical neurons. Trends Neurosci 13: 99–104, 1990.[CrossRef][Web of Science][Medline]
Corbit LH, Balleine BW. The role of prelimbic cortex in instrumental conditioning. Behav Brain Res 146: 145–157, 2003.[CrossRef][Web of Science][Medline]
Duda RO, Hart PE, Stork DG. Pattern Classification. New York: Wiley, 2001.
Euston DR, McNaughton BL. Apparent encoding of sequential context in rat medial prefrontal cortex is accounted for by behavioral variability. J Neurosci 26: 13143–13155, 2006.
Freedman DJ, Riesenhuber M, Poggio T, Miller EK. Categorical representation of visual stimuli in the primate prefrontal cortex. Science 291: 312–316, 2001.
Fuster JM, Alexander GE. Neuron activity related to short-term memory. Science 173: 652–654, 1971.
Gabbott PL, Warner TA, Jays PR, Salway P, Busby SJ. Prefrontal cortex in the rat: projections to subcortical autonomic, motor, and limbic centers. J Comp Neurol 492: 145–177, 2005.[CrossRef][Web of Science][Medline]
Gemba H, Sasaki K, Brooks VB. "Error" potentials in limbic cortex (anterior cingulate area 24) of monkeys during motor learning. Neurosci Lett 70: 223–227, 1986.[CrossRef][Web of Science][Medline]
Gemmell C, Anderson M, O'Mara SM. Deep layer prefrontal cortex unit discharge in a cue-controlled open-field environment in the freely-moving rat. Behav Brain Res 133: 1–10, 2002.[CrossRef][Web of Science][Medline]
Gothard KM, Skaggs WE, Moore KM, McNaughton BL. Binding of hippocampal Ca1 neural activity to multiple reference frames in a landmark-based navigation task. J Neurosci 16: 823–835, 1996.
Groenewegen HJ, Wright CI, Uylings HB. The anatomical relationships of the prefrontal cortex with limbic structures and the basal ganglia. J Psychopharmacol 11: 99–106, 1997.
Haddon JE, Killcross AS. Medial prefrontal cortex lesions abolish contextual control of competing responses. J Exp Anal Behav 84: 485–504, 2005.[CrossRef][Web of Science][Medline]
Hall RD, Lindholm EP. Organization of motor and somatosensory neocortex in the albino rat. Brain Res 6: 23–38, 1974.[Medline]
Hodos W, Ross GS, Brady JV. Complex response patterns during temporally spaced responding. J Exp Anal Behav 5: 473–479, 1962.[CrossRef][Web of Science][Medline]
Hotte M, Naudon L, Jay TM. Modulation of recognition and temporal order memory retrieval by dopamine D1 receptor in rats. Neurobiol Learn Mem 84: 85–92, 2005.[CrossRef][Web of Science][Medline]
Izumi Y, Zorumski CF. Norepinephrine promotes long-term potentiation in the adult rat hippocampus in vitro. Synapse 31: 196–202, 1999.[CrossRef][Web of Science][Medline]
Jay TM, Rocher C, Hotte M, Naudon L, Gurden H, Spedding M. Plasticity at hippocampal to prefrontal cortex synapses is impaired by loss of dopamine and stress: importance for psychiatric diseases. Neurotox Res 6: 233–244, 2004.[Web of Science][Medline]
Jung MW, Qin Y, McNaughton BL, Barnes CA. Firing characteristics of deep layer neurons in prefrontal cortex in rats performing spatial working memory tasks. Cereb Cortex 8: 437–450, 1998.
Kargo WJ, Szatmary B, Nitz DA. Adaptation of prefrontal cortical firing patterns and their fidelity to changes in action-reward contingencies. J Neurosci 27: 3548–3559, 2007.
Kesner RP, Farnsworth G, DiMattia BV. Double dissociation of egocentric and allocentric space following medial prefrontal and parietal cortex lesions in the rat. Behav Neurosci 103: 956–961, 1989.[CrossRef][Web of Science][Medline]
Killcross S, Coutureau E. Coordination of actions and habits in the medial prefrontal cortex of rats. Cereb Cortex 13: 400–408, 2003.
Kubota K, Niki H. Prefrontal cortical unit activity and delayed alternation performance in monkeys. J Neurophysiol 34: 337–347, 1971.
Matsuda Y, Marzo A, Otani S. The presence of background dopamine signal converts long-term synaptic depression to potentiation in rat prefrontal cortex. J Neurosci 26: 4803–4810, 2006.
McNaughton BL, O'Keefe J, Barnes CA. The stereotrode: a new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records. J Neurosci Methods 8: 391–397, 1983.[CrossRef][Web of Science][Medline]
Mulder AB, Nordquist RE, Orgut O, Pennartz CM. Learning-related changes in response patterns of prefrontal neurons during instrumental conditioning. Behav Brain Res 146: 77–88, 2003.[CrossRef][Web of Science][Medline]
Narayanan NS, Laubach M. Top-down control of motor cortex ensembles by dorsomedial prefrontal cortex. Neuron 52: 921–931, 2006.[CrossRef][Web of Science][Medline]
Niki H, Watanabe M. Prefrontal and cingulate unit activity during timing behavior in the monkey. Brain Res 171: 213–224, 1979.[CrossRef][Web of Science][Medline]
Parkinson JA, Cardinal RN, Everitt BJ. Limbic cortical-ventral striatal systems underlying appetitive conditioning. Prog Brain Res 126: 263–285, 2000.[Web of Science][Medline]
Paxinos G, Watson C. The Rat Brain in Stereotaxic Coordinates. San Diego, CA: Academic Press, 1998.
Ragozzino ME, Kesner RP. The role of rat dorsomedial prefrontal cortex in working memory for egocentric responses. Neurosci Lett 308: 145–148, 2001.[CrossRef][Web of Science][Medline]
Rainer G, Asaad WF, Miller EK. Selective representation of relevant information by neurons in the primate prefrontal cortex. Nature 393: 577–579, 1998.[CrossRef][Medline]
Recce ML, O'Keefe J. The tetrode: an improved technique for multi-unit extracellular recording. Soc Neurosci Abstr 15: 1250, 1989.
Reynolds JN, Hyland BI, Wickens JR. A cellular mechanism of reward-related learning. Nature 413: 67–70, 2001.[CrossRef][Medline]
Robbins TW. Chemical neuromodulation of frontal-executive functions in humans and other animals. Exp Brain Res 133: 130–138, 2000.[CrossRef][Web of Science][Medline]
Sakurai Y, Sugimoto S. Multiple unit activity of prefrontal cortex and dorsomedial thalamus during delayed go/no-go alternation in the rat. Behav Brain Res 20: 295–301, 1986.[CrossRef][Web of Science][Medline]
Schultz W. Predictive reward signal of dopamine neurons. J Neurophysiol 80: 1–27, 1998.
Shima K, Tanji J. Role for cingulate motor area cells in voluntary movement selection based on reward. Science 282: 1335–1338, 1998.
Skinner BF. "Superstition" in the pigeon. J Exp Psychol 380: 168–172, 1948.
Wang XJ. Discovering spatial working memory fields in prefrontal cortex. J Neurophysiol 93: 3027–3028, 2005.
Watanabe M. Reward expectancy in primate prefrontal neurons. Nature 382: 629–632, 1996.[CrossRef][Medline]
Whishaw IQ, Pellis SM, Gorny BP. Medial frontal cortex lesions impair the aiming component of rat reaching. Behav Brain Res 50: 93–104, 1992.[CrossRef][Web of Science][Medline]
Wilson FA, Rolls ET. Neuronal responses related to the novelty and familiarity of visual stimuli in the substantia innominata, diagonal band of Broca and periventricular region of the primate basal forebrain. Exp Brain Res 80: 104–120, 1990.[Web of Science][Medline]
Wilson MA, McNaughton BL. Dynamics of the hippocampal ensemble code for space. Science 261: 1055–1058, 1993.
Wilson MP, Keller FS. On the selective reinforcement of spaced responses. J Comp Physiol Psychol 46: 190–193, 1953.[CrossRef][Web of Science][Medline]
This article has been cited by other articles:
|
|
 |
|
  E. L. Rich and M. Shapiro Rat Prefrontal Cortical Neurons Selectively Code Strategy Switches J. Neurosci., June 3, 2009; 29(22): 7208 - 7219. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 N. S. Narayanan and M. Laubach Delay Activity in Rodent Frontal Cortex During a Simple Reaction Time Task J Neurophysiol, June 1, 2009; 101(6): 2859 - 2871. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. C. Lapish, D. Durstewitz, L. J. Chandler, and J. K. Seamans Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex PNAS, August 19, 2008; 105(33): 11963 - 11968. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
