A geometric representation unveils rule-learning dynamics in primate neurons

Abstract

Primates can quickly and advantageously adopt new behaviors based on changing stimuli relationships. We studied acquisition of a classification task while recording single neurons in the dorsal-anterior-cingulate-cortex (dACC) and the Striatum. Monkeys performed trial-by-trial classification on a rich set of multi-cue patterns, allowing de-novo learning every few days. To examine neural dynamics during the learning itself, we represent each rule with a spanning set of the space formed by the stimuli features. Because neural preference can be expressed by feature combinations, we can track neural dynamics in geometrical terms in this space, allowing a compact description of neural trajectories by observing changes in either vector-magnitude and/or angle-to- rule. We find that a large fraction of cells in both regions follow the behavior during learning. Neurons in the dACC mainly rotate towards the policy, suggesting an increase in selectivity that approximates the rule; whereas in the Putamen we also find a prominent magnitude increase, suggesting strengthening of confidence. Additionally, magnitude increases in the striatum followed rotation in the dACC. Finally, the neural representation at the end of the session predicted next-day behavior. The use of this novel framework enables tracking of neural dynamics during learning and suggests differential yet complementing roles for these brain regions.