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Recurrent Circuit Model of Neural Response Dynamics in V1

V1 中神经反应动力学的循环电路模型

基本信息

批准号：
10710967
负责人：
DAVID J HEEGER
金额：
$ 47.41万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-01 至 2027-05-31
项目状态：
未结题

项目摘要

Project Summary/Abstract Primary visual cortex (V1) is one of the most studied areas of the cerebral cortex, but we lack a theoretical framework for a comprehensive understanding of V1 neurophysiology. Through the proposed research, we aim to provide one. A class of circuit models, called Oscillatory Recurrent Gated Neural Integrator Circuits (ORGaNICs), simulates many key neurophysiological phenomena. Our goal is to develop a theory for the full range of neurophysiological phenomena in V1, i.e., a single predictive model with biophysically-realistic parameters, and to test that theory with previously published datasets acquired with a wide range of methodologies. Preliminary results demonstrate predictions of the theory commensurate with experimental observations of V1 response dynamics (including onset transients and the stimulus-dependence of gamma oscillations), the dynamics of attentional modulation, experimental evidence for recurrent amplification and inhibitory stabilization, experimental observations about adaptation including tuning changes and decorrelation, noise quenching, the dependence of noise correlations on similarity in orientation preference and attention, and psychophysical contrast discrimination. Aim 1 key contributions: 1) an analytical theory (i.e., closed-form expressions) that makes experimentally- testable predictions about a wide range of phenomena related to the dynamics of V1 activity; 2) closed-form expressions derived from the theory for LFP power spectra; 3) a novel explanation for oscillatory activity in visual cortex. Aim 2 key contributions: 1) an analytical theory of adaptation in V1 that makes experimentally-testable predictions about a wide range of neurophysiological phenomena related to adaptation; 2) the demonstration that adaptation maintains an efficient neural code, subject to finite resources (overall activity in the circuit), despite dynamically changing stimulus statistics. Aim 3 key contributions: 1) an analytical theory that makes experimentally-testable predictions about the variability and covariability of neural responses; 2) experimentally-testable predictions about psychophysical discrimination. The proposed research has the potential to be transformative. We will provide a new set of analytical results and computational tools for characterizing a broad range of neural circuit models, which will have a significant impact on the analysis of experimental data and experimental design, and will make new experimentally- testable predictions for both ORGaNICs and alternative models. We will provide a roadmap for understanding the underlying circuit mechanisms (the cell types, their interconnections and biophysics), and how manipulating those mechanisms may change circuit function to correct disorders of visual perception and attention.

项目总结/文摘