CRCNS_:Neural Population Coding of Dynamic Natural Scenes

CRCNS_:动态自然场景的神经群体编码

基本信息

批准号：
8329658
负责人：
Charles M Gray
金额：
$ 32.42万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-08-01 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8329658
关键词：
Accounting Afferent Neurons Area Code Cognition Complex Computer Simulation Data Development Discipline Engineering Felis catus Goals Human Resources Implant Instruction Joints Learning Linear Models Methods Modality Modeling Nature Neurobiology Neurons Neurophysiology - biologic function Pattern Perception Population Population Dynamics Population Process Principal Investigator Process Property Prosthesis Reaction Time Recurrence Relative (related person)Research Response to stimulus physiology Sensory Signal Transduction Silicon Sorting - Cell Movement Stimulus Structure System Technology Testing Thalamic structure Theoretical model Time Training Vision Disorders Visual Cortex Visual Perception Work analytical tool area striata base computerized data processing feeding improved information processing insight interdisciplinary approach model development neocortical neural circuit neural prosthesis neurophysiology operation predictive modeling receptive field relating to nervous system research study response simulation skills statistics technology development theories

项目摘要

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.

该项目旨在在我们了解神经种群过程和表示视觉皮层中的信息。通过将开拓性记录技术与新的分析工具相结合和理论框架，这项研究工作将对大量神经元的相互作用提供第一个瞥见在加工动态自然场景期间的皮层内。硅层将用于记录同时来自原发性视觉皮层中的loo-神经元种群。这些人群的活动将是以响应精度，稀疏性，相关性和LFP相干性为特征。为了阐明因果关系导致刺激诱发反应的因素，关节活动和刺激将与试图捕获大型神经元集合的刺激反应关系的预测模型。最后，我们将尝试通过构建实现理论动机的功能模型来考虑这些关系信息处理目标的感知和认知目标。该项目本质上是高度跨学科的，结合神经物理学家，理论家和工程师的专业知识，以回答超出范围的问题任何一个学科的范围。相关性（请参阅说明）：在这项工作中获得的数据和模型将为皮质操作打开新窗口电路，最初瞥见了大量神经元的同时活动动态的自然场景。这些新见解将为发展神经假体铺平道路设备（皮质植入物）和视觉疾病的新形式。