Computation by recurrent circuits of the cerebral cortex

National Institute Of Mental Health

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Abstract

Cortical neurons fire in complex patterns during behavior and cognition. In sensory regions of the cortex, animals' interactions with the sensory world evoke neural activity in the cortex. But not all patterns of cortical activity can be elicited by sensory stimuli. We study how non-sensory, artificially induced activity patterns can be used by animals to make behavioral responses. These studies shed light on the limits of cortical function, and how circuit properties like neural connections constrain the set of activity patterns the cerebral cortex can process. We have obtained results on changes in excitatory neurons responses that affect cortical computation and are dependent on the cortical recurrent network in mouse V1. The relationship between neurons' spiking input and output is central to brain computation. Studies in vitro and in anesthetized animals suggest nonlinearities emerge in cell's input-output (activation) functions as network activity increases, yet how neurons transform inputs in vivo has been unclear. Here, we characterize cortical principal neurons' activation functions in awake mice using all-optical approaches. We find responses to fixed optogenetic input are nearly unchanged as neurons are excited, reflecting a linear response regime above neurons resting points. In contrast, responses are dramatically attenuated by suppression. This attenuation is a powerful means to filter inputs that arrive to suppressed cells, privileging other inputs which arrive to excited neurons. Two major implications arise from the data: neural activation functions in vivo accord with the activation functions used in recent machine learning systems, and neurons' IO functions can enhance sensory processing by filtering inputs based on prior levels of neural activity. Together with other work in the lab, these findings contribute to our understanding of how cortical networks compute that is, how the connectivity within and between brain areas transforms inputs to process information, a function that is at the core of how brains work.