November 28, 2020

Emergence of Non-Linear Mixed Selectivity in Prefrontal Cortex after Training

Neurons in the prefrontal cortex are typically activated by multiple factors when performing a cognitive task, and by different tasks altogether. The selectivity of single neurons for the same stimulus dimension often changes depending on context or task performed, a phenomenon known as nonlinear mixed selectivity. It has been hypothesized that neurons with such mixed selectivity offer a computational advantage for performing cognitive tasks due to high-dimensional neural representations. In this study, we sought to determine how nonlinear mixed selectivity is affected by training to perform a cognitive task by examining the neural responses of monkeys before and after they were trained to perform visual working memory tasks. We also compared nonlinear mixed selectivity in different sub-regions of the prefrontal cortex that play different roles in these tasks. Our findings indicate that a small population of prefrontal neurons exhibit nonlinear mixed selectivity even prior to any training to perform cognitive tasks. Learning to perform working memory tasks induces a modest increase in the proportion of neurons with both linear and non-linear mixed selectivity. However, we saw little evidence that nonlinear mixed selectivity is predictive of task performance. Our results provide insights on the representation of stimulus and task information in neuronal populations.

 bioRxiv Subject Collection: Neuroscience

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