CRCNS_:Neural Population Coding of Dynamic Natural Scenes

CRCNS_:动态自然场景的神经群体编码

• 批准号: 7778022
• 负责人: Charles M Gray
• 金额: $ 35.89万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7778022
• 关键词: Accounting; Afferent Neurons; Area; Arts; Code; Cognition; Complex; Computer Simulation; Data; Development; Discipline; Doctor of Philosophy; Engineering; Felis catus; Funding; Goals; Human Resources; Institutes; Investigation; Joints; Lead; Learning; Linear Models; Methods; Modality; Modeling; Montana; Nature; Neurobiology; Neurons; Neurophysiology - biologic function; Neurosciences; Pattern; Perception; Population; Population Dynamics; Population Process; Positioning Attribute; Principal Investigator; Process; Property; Reaction Time; Recruitment Activity; Recurrence; Relative (related person); Research; Research Infrastructure; Research Training; Response to stimulus physiology; Sensory; Signal Transduction; Silicon; Sorting - Cell Movement; Stimulus; Structure; System; Techniques; Technology; Testing; Thalamic structure; Theoretical model; Time; Training; Underrepresented Minority; Universities; Vision; Visual Cortex; Visual Perception; Woman; Work; analytical tool; area striata; base; computerized data processing; data sharing; feeding; graduate student; improved; information processing; insight; interdisciplinary approach; model development; neocortical; nervous system disorder; neural circuit; neural prosthesis; neurophysiology; predictive modeling; receptive field; relating to nervous system; research study; response; sensory cortex; simulation; skills; statistics; technology development; theories

DESCRIPTION (provided by applicant): This project aims to achieve a fundamental advance in our understanding of how neural populations process and represent information within sensory 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 100+ neurons in 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 neurophysiologists, theoreticians, and engineers to answer questions that are beyond the scope of any one discipline. Intellectual merit. The question of how the cortex processes and represents sensory information has been the subject of neurophysiological and neuroanatomical investigation for at least four decades. While much has been learned from these efforts, there remain many fundamental, unanswered questions regarding the dynamical properties of neurons and the information processing capabilities of this system. The usual approach of studying single-unit responses to simple stimuli is limited in that it assumes - either explicitly or implicitly - that the system can be understood one component at a time. In a non-linear dynamical system it is difficult to predict how effects observed in isolation will behave when combined. Thus, in order to properly characterize and understand the dynamics of cortical circuits, it is necessary to observe the joint activities of large numbers of simultaneously recorded neurons in response to complex, timevarying signals arising from dynamic natural scenes. This project represents the first-ever attempt to thoroughly examine the joint responses of large numbers of neurons in the cortex during natural vision. Combined with the computational modeling and theoretical developments that will incorporate findings originating from these studies, this project has the potential to fundamentally advance our understanding of how cortical circuits work. Broader impacts. This project will provide research training to two graduate students, one in neuroscience (UC Berkeley) and one in engineering (Georgia Tech), and these studies will constitute the bulk of their Ph.D. theses. Efforts will be made to recruit women and underrepresented minorities into these positions. The methods developed and the results obtained from this study will be incorporated into coursework at UC Berkeley, Georgia Institute of Technology and Montana State University, and data will be made available on the NSF-funded CRCNS datasharing facility. Advancing our understanding of neural circuit dynamics within the cortex could lead to the development of viable therapies for myriad neurological disorders, and it is crucial to the development of neural prostheses. Furthermore, the proposed work will strengthen our infrastructure for further studies of the cortex by pioneering new simultaneous recording techniques and making the data publicly available as part of a CRCNS data sharing project.

描述（由申请人提供）：该项目旨在在我们对神经种群过程中的理解中取得基本进步，并在感觉皮层中表示信息。通过将开拓性记录技术与新的分析工具和理论框架相结合，这项研究工作将为您在处理动态自然场景期间在皮质中如何在皮质中相互作用。硅层状将用于从皮质中100+神经元的种群同时记录。这些人群的活性将以响应精度，稀疏性，相关性和LFP相干性来表征。为了阐明导致皮质中刺激诱发反应的因果因素，关节活动和刺激将与预测模型相吻合，这些模型试图捕获大型神经元集合的刺激反应关系。最后，我们将尝试通过构建实现理论动机的信息处理目标的功能模型来考虑这些关系，以感知和认知。该项目本质上是高度跨学科的，结合了神经生理学家，理论家和工程师的专业知识，以回答超出任何一学科范围的问题。智力优点。至少四十年，关于皮质过程和代表感觉信息的问题如何成为神经生理和神经解剖学研究的主题。尽管从这些努力中学到了很多东西，但有关神经元的动力学特性以及该系统的信息处理能力，仍然存在许多基本的，未解决的问题。研究对简单刺激的单单元响应的通常方法受到限制，因为它假定（明确或隐式）可以一次理解系统。在非线性动力学系统中，很难预测合并时孤立观察到的效果的表现。因此，为了正确地表征和理解皮质回路的动力学，有必要观察大量同时记录的神经元的联合活动，以响应于动态自然场景引起的复杂，时变的信号。该项目代表了有史以来第一次尝试在自然视觉期间彻底检查皮层中大量神经元的联合反应的尝试。结合计算建模和理论发展，将结合这些研究的发现，该项目有可能从根本上促进我们对皮质电路如何工作的理解。更广泛的影响。该项目将为两名研究生提供研究培训，一个是神经科学（UC Berkeley）和一个工程学（佐治亚理工学院）的研究培训，这些研究将构成其博士学位的大部分。这些。将努力招募妇女和代表性不足的少数民族担任这些职位。开发的方法和从这项研究中获得的结果将纳入加州大学伯克利分校，佐治亚州理工学院和蒙大拿州立大学的课程中，并且将在NSF资助的CRCNS数据库设施上提供数据。促进我们对皮层内神经电路动态的理解可能会导致为无数神经系统疾病的可行疗法发展，这对于神经假体的发展至关重要。此外，拟议的工作将通过开创新的同时录制技术并将数据作为CRCNS数据共享项目的一部分，通过开创新的同时录制技术来加强我们的基础架构，以进一步研究皮质。