CRCNS: Linking Synaptic Populations and Computation Using Statistical Mechanics

CRCNS：使用统计力学将突触群体和计算联系起来

• 批准号: 10830119
• 负责人: Krishnan Padmanabhan
• 金额: $ 26.13万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10830119
• 关键词: Address; Architecture; Area; Awareness; Beauty; Behavior; Biology; Books; Brain; Characteristics; Child; Clinical; Code; Complex; Dendritic Spines; Development; Disease; Education; Educational workshop; Ensure; Entropy; Ferrets; Functional disorder; General Population; Image; Individual; Interdisciplinary Study; Knowledge; Lead; Link; Location; Measures; Mind; Modeling; Nature; Neurobiology; Neurons; Neurosciences; Output; Pattern; Pennsylvania; Persons; Physics; Population; Probability; Program Development; Property; Research Personnel; STEM field; Science; Scientific Advances and Accomplishments; Scientist; Sensory; Statistical Mechanics; Structure; Synapses; Talents; Training; Trees; Universities; Visual Cortex; Weight; Work; biophysical model; calcium indicator; career; career development; computational neuroscience; deep neural network; design; experimental study; high school; in vivo; lectures; nervous system disorder; neural; next generation; novel; novel strategies; outreach; public health relevance; skills; statistics; synaptic function; theories; two photon microscopy; undergraduate student

Computations performed by single neurons result from integration of a large myriad of synaptic inputs distributed throughout complex dendritic topology. Synaptic inputs vary in substantial ways; sensory-driven activity patterns, probability of activation, synapse location within the dendritic topology, local dendritic organization, and ultrastructural characteristics are all thought to be critical for determining how synaptic inputs influence the spiking output of a neuron. Populations of synaptic inputs, ultimately, determine the coding capacity and computations single neurons can perform. Despite this fact, studies largely overlook the synaptic input population within a single neuron, instead focusing on the activity of cellular populations, using biophysical models or constructing models that create hypothetical weights or synapses (e.g. deep-neural networks). Thus, a critical question in neuroscience remains how ensemble synaptic activity is integrated in vivo and what are the fundamental principles of synaptic organization which describe neural computation. To address this issue, we present a tightly integrated experiment-theory approach. We propose to (1) measure the sensory-driven activity patterns of large populations of dendritic spines on layer 2/3 visual cortical neurons in ferret visual cortex in vivo, and (2) use a statistical physics approach to characterize the structure and computing of synaptic populations in multiple contexts. Thus, this project will provide fundamental knowledge about the synaptic architecture of neurons in the brain. The

由单个神经元执行的计算是整合大量突触输入的结果 分布在复杂的树枝状拓扑结构中。突触输入在很大程度上不同的；感觉驱动的 活动模式、激活概率、突触在树突拓扑中的位置、局部树突 组织和超微结构特征都被认为是决定突触如何 输入会影响神经元的尖峰输出。突触输入的群体，最终决定了 单个神经元可以完成的编码能力和计算能力。尽管如此，研究很大程度上忽略了 单个神经元内的突触输入群体，而不是关注细胞群体的活动，使用 生物物理模型或构建创建假设权重或突触的模型(例如，深层神经 网络)。因此，神经科学中的一个关键问题仍然是如何整合整体突触活动。 以及描述神经计算的突触组织的基本原则是什么。 为了解决这个问题，我们提出了一种紧密结合的实验和理论方法。我们建议(1) 测量第2/3层视觉上大量树突棘的感觉驱动的活动模式 以及(2)使用统计物理方法来表征雪貂视觉皮质的 多语境下突触群体的结构和计算。因此，该项目将提供 关于大脑中神经元突触结构的基础知识。这个