Incorporation of new information into prefrontal cortical activity after learning working memory tasks

Abstract

The ability to learn new tasks requires that new information is integrated into neural systems that already support other behaviors. To study how new information is incorporated into neural representations, we analyzed single-unit recordings from the prefrontal cortex (PFC), a brain region important for task acquisition and working memory, before and after monkeys learned to perform two behavioral tasks. A population-decoding analysis revealed a large increase in task-relevant information, and smaller changes in stimulus-related information, after training. This new information was contained in dynamic patterns of neural activity, with many individual neurons containing the new task-relevant information for only relatively short periods of time in the midst of other large firing rate modulations. Additionally, we found that stimulus information could be decoded with high accuracy only from dorsal PFC, whereas task-relevant information was distributed throughout both dorsal and ventral PFC. These findings help resolve a controversy about whether PFC is innately specialized to process particular types of information or whether its responses are completely determined by task demands by showing there is both regional specialization within PFC that was present before training, as well as more widespread task-relevant information that is a direct result of learning. The results also show that information is incorporated into PFC through the emergence of a small population of highly selective neurons that overlay new signals on top of patterns of activity that contain information about previously encoded variables, which gives insight into how information is coded in neural activity.

Fig. 1.

Brain regions and the feature task. (A) Dorsal (yellow) and ventral (magenta) regions of lateral PFC where the recordings were made. (B) Stimuli used in the feature task. The stimuli extended 2° of visual angle. (C) Passive fixation task that was used before training. (D) Feature task. The monkeys viewed the same sequence of images as in the passive task; however, at the end of the experiment, monkeys needed to make a saccade to the green target if the stimuli matched or to the blue target if the stimuli did not match.

Fig. 2.

Information in PFC pre- and posttraining in the feature task. (A) Comparison of information about the identity of the first stimulus pretraining (blue) and posttraining (red). The gray shaded regions indicate the times when the first, second, and decision stimuli were shown, the black horizontal line indicates the level of decoding expected by chance, the color shaded regions indicate 1 SE in the decoding accuracy if different neurons were used, and the red and blue bars at the bottom of the figure indicate times when the decoding accuracy was above chance (permutation test, P < 0.005). As can be seen, training had little effect on stimulus identity information. (B) Comparison of information about the match/nonmatch trial status pretraining (blue) and posttraining (red). As can be seen, there is a large increase in match/nonmatch status information after training. C, Match/nonmatch selectivity of individual neurons before training (Left) and after training (Right). (The black horizontal line is for visualization purposes to make the pre- and posttraining differences easier to compare.) The η² statistic measures the proportion of the trial-by-trial variance in firing rates explained by whether a trial is a match or a nonmatch trial. Each point corresponds to the η² value of a single neuron at the latency when the neuron had its maximal selectivity (see Methods).

Fig. 3.

Dynamic coding of task relevant information after training in the feature task. (A) Results from training a classifier at one time period (y axis) and testing the classifier at a second time period (x axis) for decoding the match/nonmatch trial status, either pretraining (Left) or posttraining (Right). The black solid vertical lines indicate the times when the first, second, and match/nonmatch stimuli appeared, and the black dashed lines indicate the offset times of the first and second stimuli. After the monkey was trained, high classification accuracies are seen only when the classifier is built and tested using data from around the same time periods, which shows that different patterns of neuron activity contain the task relevant information at different time points in the experiment. (B) Firing rates for the match trials (blue) and nonmatch trials (red) for the three most selective neurons in feature task. As can be seen, differences in firing rates between match and nonmatch trials appear to be added on top of other firing rate changes that are occurring over the course of a trial (and are carrying information about other variables). Additionally, some neurons (e.g., Middle) only contain large firing rate differences between match and nonmatch trials for short periods of time, which give rises to the dynamic coding of information at the population level. (These neurons are typical examples of the population of neurons that have large amounts of match/nonmatch information). Error bars indicate 1 SEM.

Fig. 4.

Comparing information in dorsal PFC (magenta) vs. ventral PFC (green). (A) Match/nonmatch information pretraining (Left) and posttraining (Right) reveals that, after training, there is task relevant match/nonmatch information in both dorsal and ventral PFC. (B) In contrast, information about which stimulus was shown (stimulus identity information) was seen only in dorsal PFC in both the pretraining and posttraining data (left and right plots, respectively).