CRCNS: Neural Population Coding of Dynamic Natural Scenes

University Of California Berkeley, Berkeley CA

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Abstract

This project aims to achieve a fundamental advance in our understanding of how neural populations process and represent information within visual cortex. By combining pioneering recording technology with new analytical tools and theoretical frameworks, this research effort will provide the first glimpse at how large numbers of neurons interact within the cortex during the processing of dynamic natural scenes. Silicon polytrodes will be used to record simultaneously from populations of lOO-i- neurons in primary visual cortex. The activity of these populations will be characterized in terms of response precision, sparsity, correlation, and LFP coherence. In order to elucidate the causal factors that contribute to stimulus-evoked responses in the cortex, the joint activity and stimuli will be fit with predictive models that attempt to capture the stimulus-response relationships of large neuronal ensembles. Finally, we will attempt to account for these relationships by building functional models that achieve theoretically-motivated information processing objectives for perception and cognition. The project is highly interdisciplinary in nature, combining the expertise of neurophyslologists, theoreticians, and engineers to answer questions that are beyond the scope of any one discipline. RELEVANCE (See instructions): The data obtained and models developed in this work will open a new window into the operation of cortical circuits, providing a first glimpse of the simultaneous activity of large numbers of neurons responding to dynamic natural scenes. These new insights will pave the way for the development of neural prosthetic devices (cortical implants) and new forms of treatment for visual disorders.