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

基本信息

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
项目类别：
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

项目摘要

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

单个神经元执行的计算是大量突触输入整合的结果分布在复杂的树突拓扑结构中。突触输入有很大的不同；感官驱动活动模式、激活概率、树突拓扑内的突触位置、局部树突组织和超微结构特征都被认为对于确定突触如何发挥作用至关重要。输入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执行的编码能力和计算。尽管如此，研究在很大程度上忽视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，使用创建假设权重或突触的生物物理模型或构建模型（例如深度神经网络）网络）。因此，神经科学中的一个关键问题仍然是如何将整体突触活动整合到体内以及描述神经计算的突触组织的基本原理是什么。为了解决这个问题，我们提出了一种紧密集成的实验理论方法。我们建议（1）测量 2/3 层视觉上大量树突棘的感觉驱动活动模式体内雪貂视觉皮层的皮层神经元，以及（2）使用统计物理方法来表征多种环境下突触群的结构和计算。因此，该项目将提供有关大脑神经元突触结构的基础知识。这