Medial Prefrontal Cortex Population Activity Is Plastic Irrespective of Learning

Abstract

The prefrontal cortex (PFC) is thought to learn the relationships between actions and their outcomes. But little is known about what changes to population activity in PFC are specific to learning these relationships. Here we characterize the plasticity of population activity in the medial PFC (mPFC) of male rats learning rules on a Y-maze. First, we show that the population always changes its patterns of joint activity between the periods of sleep either side of a training session on the maze, regardless of successful rule learning during training. Next, by comparing the structure of population activity in sleep and training, we show that this population plasticity differs between learning and nonlearning sessions. In learning sessions, the changes in population activity in post-training sleep incorporate the changes to the population activity during training on the maze. In nonlearning sessions, the changes in sleep and training are unrelated. Finally, we show evidence that the nonlearning and learning forms of population plasticity are driven by different neuron-level changes, with the nonlearning form entirely accounted for by independent changes to the excitability of individual neurons, and the learning form also including changes to firing rate couplings between neurons. Collectively, our results suggest two different forms of population plasticity in mPFC during the learning of action-outcome relationships: one a persistent change in population activity structure decoupled from overt rule-learning, and the other a directional change driven by feedback during behavior.SIGNIFICANCE STATEMENT The PFC is thought to represent our knowledge about what action is worth doing in which context. But we do not know how the activity of neurons in PFC collectively changes when learning which actions are relevant. Here we show, in a trial-and-error task, that population activity in PFC is persistently changing, regardless of learning. Only during episodes of clear learning of relevant actions are the accompanying changes to population activity carried forward into sleep, suggesting a long-lasting form of neural plasticity. Our results suggest that representations of relevant actions in PFC are acquired by reward imposing a direction onto ongoing population plasticity.

Keywords: neural ensembles; population activity; statistical inference.

Figure 1.

Task and behavior. A, Top, Y-maze task setup. Each session included the epochs of pretraining sleep/rest, training trials, and post-training sleep/rest (bottom). One of four target rules for obtaining reward was enforced throughout a session: go right, go to the cued arm, go left, and go to the uncued arm. No rat successfully learned the uncued-arm rule. B, Breakdown of each learning session into the duration of its components. The training epoch is divided into correct (red) and error (blue) trials, and intertrial intervals (white spaces). Trial durations were typically 2–4 s (thin lines). The pretraining and post-training epochs contained quiet waking and light sleep states (“Rest” period) and identified bouts of slow-wave sleep (SWS). C, Internally driven behavioral changes in an example learning session: the identified learning trial (gray line) corresponds to a step increase in accumulated reward and a corresponding shift in the dominant behavioral strategy (bottom). The target rule for this session is “go right.” Strategy probability is computed in a 7-trial sliding window; we plot the midpoints of the windows. D, Perilearning cumulative reward for all 10 identified learning sessions: in each session, the learning trial (gray line) corresponds to a step increase in accumulated reward. E, Perilearning strategy selection for the correct behavioral strategy. Each line plots the probability of selecting the correct strategy for a learning session, computed in a 7-trial sliding window. The learning trial (gray vertical line) corresponds to the onset of the dominance of the correct behavioral strategy. F, Strategy selection during stable behavior. Each line plots the probability of selecting the overall dominant strategy, computed in a 7-trial sliding window. One line per session.

Figure 2.

A neural dictionary of population activity in PFC. Top, A snapshot of population activity from N = 23 neurons during 500 ms of pretraining sleep. Bottom, Corresponding binary word structure (black represents 1; white represents 0) for bins of 10 ms. Gray represents one bin of the population activity and its corresponding binary word. Right, The set of binary words and the frequency of their occurrence over the whole pretraining sleep epoch define a dictionary of population activity.

Figure 3.

Distributions of word probabilities change between pretraining and post-training sleep. A, Proportion of words in the pretraining sleep dictionary that are also in the post-training sleep dictionary, per session. B, Proportion of the pretraining sleep epoch's activity that is accounted for by words in common with post-training sleep, per session. C, The joint distribution of the probability of every word occurring in pretraining sleep (distribution P(Pre)) and post-training sleep (distribution P(Post)), for one learning session. D(Pre | Post): the distance between the two probability distributions for words. D, Distance between the word probability distributions for pretraining and post-training sleep (x axis) against the expected distance if the sleep activity was drawn from the same distribution in both epochs (y axis). One symbol per learning session; we plot the mean and 99% CI (too small to see) of the expected distance D(Pre* | Post*). Words constructed using 5 ms bins. E, Same as for D, for stable sessions. F, Bin size dependence of changes in the dictionary between sleep epochs. Difference between the data and mean null model distance are plotted for each session, at each bin size used to construct words.

Figure 4.

Distributions of word probabilities converge only during learning. A, For the training epochs, the proportion of the epoch's dictionary (left) and duration (right) accounted for by words in common with both sleep epochs. One symbol per learning session. B, Schematic of comparisons between epochs, and summary of main results. C, Examples for one learning session of the joint probability distributions for each word in trials and pretraining sleep (left), and trials and post-training sleep (right), using 5 ms bins. D(Trials | X): the distance between the two probability distributions for words. D, Distances for all learning sessions, for words constructed using 5 ms bins. T, Trials. E, Same as for D, for stable sessions. F, Bin size dependence of the relative convergence between the word distributions in trials and in sleep. Each distance was computed using only the dictionary of words appearing in the trials. Numbers are p values from two-sided sign tests. G, Same as for F, for stable sessions.

Figure 5.

Changes between sleep epochs are accounted for by independently changing neurons. A, Example excitability changes between sleep epochs, for one learning session. Each pair of bars plot the distributions of a neuron's interspike intervals in the pretraining and post-training sleep epochs: each bar shows the median (white line), interquartile range (dark shading), and 95% interval (light shading). Neurons are ranked by the difference in their median interval between sleep epochs. We use a log-scale on the y axis: some neurons shift their distribution over orders of magnitude between sleep epochs. The first neuron was silent in the post-training sleep epoch. B, Distances between sleep epochs for dictionaries of independent neurons (x axis), and their expected distances from a null model of the same dictionary in both epochs (y axis). Independent neuron dictionaries are constructed by shuffling interspike intervals within trials or sleep bouts. One symbol per learning session; we plot the mean and 99% CI (too small to see) of the expected distance D(Pre* | Post*). Words constructed using 5 ms bins. S, Shuffled data. C, Same as for B, for stable sessions. D, Independent neuron dictionaries are consistently different between sleep epochs at all bin sizes: compare with results for the data dictionaries in Figure 3F. Each symbol is a mean over 20 shuffled datasets. E, Departure from the expected distance between sleep epochs for each learning session (Data), and the corresponding predicted departure by independent neurons (Shuffle; mean over 20 shuffled datasets). Words constructed using 5 ms bins. F, Same as for E, for stable sessions. G, Difference between the recorded and shuffled data, as a proportion of the data's departure from the expected distance between sleep epochs. Almost all differences are <0.1% of the difference between data and the null model. One symbol per session. H, The proportion of words in the dictionary with two or more active neurons, over all learning sessions. I, Same as for G, using dictionaries that contained only words with coactivity. At all bin sizes, there is no systematic difference between recorded and shuffled data.

Figure 6.

Convergence of dictionaries during learning is partly driven by changes in rate covariation, but not spike timing. A, Distances between sleep and trial distributions for all learning (left) and stable (right) sessions, in an example shuffled dataset. Words constructed using 5 ms bins. D(T_S | X_S): the distance between the trial probability distribution and the probability distribution of sleep epoch X in the shuffled data. B, Difference between the recorded and shuffled data convergence between trial and post-training sleep epochs, in learning sessions. C, Same as for B, using distributions containing only words with coactivity. D, Same as for C, comparing coactivity word distributions from recorded and jittered data, to test for the contribution of precise spike timing. Spike data were jittered at a range of SDs (x axis), and words constructed using 5 ms bins. E, Snapshots of a single neuron's firing rate (black) compared with the simultaneous population firing rate (color) in each epoch. C values in panel (E) represents population coupling in each epoch. F, Joint distribution of the population coupling for each neuron in the training and pretraining sleep epochs of one learning session. R, Pearson's correlation coefficient between the two distributions of population coupling. G, Same as for F, for the training and post-training sleep epochs in the same session. H, Correlations between population coupling in training and sleep epochs for all learning sessions. Population coupling is more correlated between training and post-training sleep (signed-rank test p = 0.02, rank = 5).

Figure 7.

Locations of words during trials of learning sessions. A, Scatter of the spread in location against median location for every word in the training epoch dictionaries of the learning sessions, constructed using 3 ms bins. Spread in location is the interquartile interval, which we also plot as vertical lines. Right, We plot the density of median locations for the data (red area plot) and independent neuron (gray line) dictionaries. B, Density of median locations across all bin sizes, for data (red area plot) and independent neuron (gray line) dictionaries. C, For each word in the training epoch dictionaries, we plot its median location against the closeness between its training epoch and sleep epoch probability. Closeness is in the range [−1, 1], where −1 indicates identical probability between training and pretraining sleep, and 1 indicates identical probability between training and post-training sleep. Colored bars represent the regions of the maze analyzed in D–F. D, Distributions of word closeness to sleep in specific maze segments, for 3 ms bins. All words with median locations within the specified maze segment are divided into terciles of closeness by thresholds of −0.5 and 0.5 (vertical gray lines). Symbols represent proportions of words falling in each tercile. Error bars indicate 99% CIs on those proportions. Blue represents arm end. Orange represents choice point. Red represents prechoice segment. E, Same as for D, for 10 ms bins. F, Same as for D, for 50 ms bins.

Figure 8.

Independent neurons capture a large fraction of population activity structure. A, Proportion of 1's that encode more than one spike (“binary error”), across all emitted words in all learning sessions. Epoch colors apply to all panels. B, Same as for A, for dictionaries of independent neurons derived by shuffling neuron interspike intervals to remove correlations. Proportions are means from 20 shuffled datasets of the learning sessions. C, Mean difference between binary error proportions in the data and predicted by independent neurons, in percentile points. D, Same as for C, expressed as a proportion of the binary errors in the data. E, Proportion of emitted words in each epoch that have more than one active neuron, pooled across all learning sessions (replotted from Fig. 5H). F, Same as for E, for dictionaries of independent neurons. G, Median difference between the proportion of emitted coactivity words in the data and predicted by independent neurons. H, Same as for G, expressed as a proportion of the number of coactivity words in the data.