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bioRxiv · 10.1101/2020.11.27.401539

Recurrent dynamics of prefrontal cortex during context-dependent decision-making

Cohen, Z.·DePasquale, B.·Aoi, M. C.·Pillow, J. W.
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Abstract

A key problem in systems neuroscience is to understand how neural populations integrate relevant sensory inputs during decision-making. Here, we address this problem by training a structured recurrent neural network to reproduce both psychophysical behavior and neural responses recorded from monkey prefrontal cortex during a context-dependent per-ceptual decision-making task. Our approach yields a one-to-one mapping of model neurons to recorded neurons, and explicitly incorporates sensory noise governing the animals performance as a function of stimulus strength. We then analyze the dynamics of the resulting model in order to understand how the network computes context-dependent decisions. We find that network dynamics preserve both relevant and irrelevant stimulus information, and exhibit a grid of fixed points for different stimulus conditions as opposed to a one-dimensional line attractor. Our work provides new insights into context-dependent decision-making and offers a powerful framework for linking cognitive function with neural activity within an artificial model.

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Cohen, Z., DePasquale, B., Aoi, M. C., Pillow, J. W.. 2020-11-27. Recurrent dynamics of prefrontal cortex during context-dependent decision-making. https://doi.org/10.1101/2020.11.27.401539

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