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.

项目摘要/摘要主要视觉皮层（V1）是大脑皮层研究最多的区域之一，但我们缺乏理论对V1神经生理学的全面理解的框架。通过拟议的研究，我们的目标提供一个。一类称为振荡性复发的门控神经积分电路的电路模型（有机物），模拟许多关键的神经生理现象。我们的目标是为完整的理论发展一个理论 V1中的神经生理现象范围，即具有生物物理现实主义的单个预测模型参数，并以先前发布的数据集测试该理论，以广泛的范围获取方法论。初步结果证明了该理论与V1的实验观察的预测响应动力学（包括发作瞬变和γ振荡的刺激依赖性），注意调制的动力学，反复扩增的实验证据和抑制作用稳定，有关适应的实验观察，包括调整变化和去相关，噪声淬灭，噪声相关对方向偏好和注意力相似性的依赖性，以及心理物理对比歧视。 AIM 1关键贡献：1）一种分析理论（即封闭形式表达式），使实验性地关于与V1活性动力学相关的广泛现象的可检验预测； 2）封闭形式源自LFP功率谱的理论得出的表达； 3）一种新颖的解释振荡活性视觉皮层。目标2关键贡献：1）V1中适应的分析理论，该理论使实验检验关于与适应有关的广泛神经生理现象的预测； 2）演示这种适应维持有效的神经法规，但要受到有限资源（整体活动），尽管动态变化了刺激统计。 AIM 3关键贡献：1）一种分析理论，该理论对实验进行了实验检验的预测神经反应的可变性和协方差； 2）关于心理物理的实验检验预测歧视。拟议的研究有可能具有变革性。我们将提供一组新的分析结果以及用于表征广泛神经电路模型的计算工具，这将具有重要的对实验数据和实验设计的分析的影响，并将在实验中成为新的有机物和替代模型的可测试预测。我们将提供理解的路线图基础电路机制（细胞类型，它们的互连和生物物理学）以及如何操纵这些机制可能会改变电路功能以纠正视觉感知和注意力的障碍。