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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Mixed selectivity morphs population codes in prefrontal cortex
Aishwarya Parthasarathy1,2, Roger Herikstad3, Jit Hon Bong3
1NUS Graduate School of Integrative Science and Engineering, National University of Singapore (NUS), Singapore, Singapore.
Abstract:
The prefrontal cortex maintains working memory information in the presence of distracting stimuli. It has long been thought that sustained activity in individual neurons or groups of neurons was responsible for maintaining information in the form of a persistent, stable code. Here we show that, upon the presentation of a distractor, information in the lateral prefrontal cortex was reorganized into a different pattern of activity to create a morphed stable code without losing information. In contrast, the code in the frontal eye fields persisted across different delay periods but exhibited substantial instability and information loss after the presentation of a distractor. We found that neurons with mixed-selective responses were necessary and sufficient for the morphing of code and that these neurons were more abundant in the lateral prefrontal cortex than the frontal eye fields. This suggests that mixed selectivity provides populations with code-morphing capability, a property that may underlie cognitive flexibility.

