CRCNS_:Neural Population Coding of Dynamic Natural Scenes

描述（由申请人提供）：该项目旨在实现我们对神经群体如何处理和表示感觉皮层内信息的理解的根本性进展。通过将开创性的记录技术与新的分析工具和理论框架相结合，这项研究工作将首次揭示在处理动态自然场景时，大量神经元如何在皮层内相互作用。硅多电极将用于同时记录皮质中100多个神经元的群体。这些群体的活动将根据响应精度、稀疏性、相关性和LFP一致性来表征。为了阐明导致皮层中刺激诱发反应的因果因素，将用预测模型拟合联合活动和刺激，该预测模型试图捕获大神经元集合的刺激-反应关系。最后，我们将试图通过建立功能模型来解释这些关系，这些功能模型实现了感知和认知的理论动机信息处理目标。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，以回答超出任何学科范围的问题。智力上的优点。皮质如何处理和代表感觉信息的问题，已经成为神经生理学和神经解剖学研究的主题至少40年了。虽然从这些努力中已经学到了很多东西，但关于神经元的动力学特性和该系统的信息处理能力，仍然存在许多基本的未回答的问题。研究单一单位对简单刺激的反应的通常方法是有限的，因为它假设-无论是明确的还是隐含的-系统可以被理解为一次一个组件。在非线性动力系统中，很难预测单独观察到的效应在组合时会如何表现。因此，为了正确表征和理解皮层回路的动态，有必要观察大量同时记录的神经元对动态自然场景产生的复杂时变信号的联合活动。该项目代表了有史以来第一次尝试彻底检查在自然视觉过程中大脑皮层中大量神经元的联合反应。结合计算建模和理论发展，将纳入来自这些研究的发现，该项目有可能从根本上推进我们对皮层回路如何工作的理解。更广泛的影响。该项目将为两名研究生提供研究培训，一名是神经科学（加州大学伯克利分校），另一名是工程学（格鲁吉亚理工学院），这些研究将构成他们博士学位的大部分。论文将努力征聘妇女和任职人数不足的少数民族担任这些职位。从这项研究中开发的方法和获得的结果将被纳入加州大学伯克利分校，格鲁吉亚理工学院和蒙大拿州立大学的课程，并将在NSF资助的CRCNS共享设施提供数据。推进我们对皮层内神经回路动力学的理解，可能会导致开发各种神经系统疾病的可行疗法，这对神经假体的开发至关重要。此外，拟议的工作将加强我们的基础设施，进一步研究皮层开拓新的同步记录技术，并使数据公开提供的CRCNS数据共享项目的一部分。

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.