Abstract
Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The dorsal-lateral prefrontal cortex (dlPFC) and the dorsal striatum (dSTR), which are strongly interconnected, are key nodes in this circuitry. Substantial experimental evidence, including neurophysiological recordings, have shown that neurons in these structures represent key aspects of learning. The computational mechanisms that shape the neurophysiological responses, however, are not clear. To examine this, we developed a recurrent neural network (RNN) model of the dlPFC-dSTR circuit and trained it on an oculomotor sequence learning task. We compared the activity generated by the model to activity recorded from monkey dlPFC and dSTR in the same task. This network consisted of a striatal component which encoded action values, and a prefrontal component which selected appropriate actions. After training, this system was able to autonomously represent and update action values and select actions, thus being able to closely approximate the representational structure in corticostriatal recordings. We found that learning to select the correct actions drove action-sequence representations further apart in activity space, both in the model and in the neural data. The model revealed that learning proceeds by increasing the distance between sequence-specific representations. This makes it more likely that the model will select the appropriate action sequence as learning develops. Our model thus supports the hypothesis that learning in networks drives the neural representations of actions further apart, increasing the probability that the network generates correct actions as learning proceeds. Altogether, this study advances our understanding of how neural circuit dynamics are involved in neural computation, revealing how dynamics in the corticostriatal system support task learning.
Highlights
Human and nonhuman primates are capable of complex adaptive behavior
In the original experiment we focused on differences between choices driven by reinforcement learning and choices driven by immediately available information, in alternating blocks of trials
While the animal exectued these movement trajectories, recordings were obtained from demixed principal component analysis (dPCA) and lateral prefrontal cortex (lPFC) (Fig. 1B)
Summary
Human and nonhuman primates are capable of complex adaptive behavior. Adaptive behavior requires predicting the values of choices, executing actions on the basis of those predictions, and updating predictions following the rewarding or punishing outcomes of choices. Reinforcement learning (RL) is a formal, algorithmic framework useful for characterizing these behavioral processes. Experimental work suggests that RL maps onto fronto-striatal systems, dopaminergic interactions with those systems, and other structures including the amygdala and thalamus [Averbeck and Costa, 2017]. About how the RL formalism and the associated behaviors map onto mechanisms at the neural population level across these systems. How do neural population codes evolve with learning across these systems, and what are the underlying network mechanisms that give rise to these population codes?
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