Mechanistic neural circuit models and principles

机械神经回路模型和原理

• 批准号: 10669698
• 负责人: Ila R. Fiete
• 金额: $ 50.99万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10669698
• 关键词: Accounting; Anatomy; Animals; Architecture; Attention; Bar Codes; Behavior; Behavioral; Benchmarking; Biological; Brain; Brain region; Collaborations; Communication; Coupled; Data; Data Analyses; Decision Making; Development; Dimensions; Electrophysiology (science); Environment; Experimental Designs; Experimental Models; Future; Genetic Markers; Goals; Infrastructure; International; Laboratories; Learning; Left; Link; Measurement; Methods; Modeling; Mus; Neural Network Simulation; Output; Performance; Population; Process; Regional Anatomy; Research Personnel; Rewards; Running; Sensory; Standardization; Statistical Data Interpretation; Statistical Models; Stimulus; Structure; Synapses; Task Performances; Testing; Training; Work; cell type; data sharing; design; experimental study; learning strategy; network models; neural; neural circuit; neural model; neuromechanism; novel; operation; predictive modeling; projectin; recurrent neural network; sensory input; synergism; theories; tool

Summary/Abstract, Project 3 Even in the same environment, an animal may make different decisions on different occasions, because its internal state, such as engagement in a task, interacts powerfully with external inputs to determine behavior. This proposal’s overarching goal is to understand how internal states influence decisions and to identify the underlying neural mechanisms. The team is part of the International Brain Laboratory (IBL), an established consortium that has developed a standardized mouse decision-making task and standardized methods for training, neural measurement, and data analysis, along with a working, scalable infrastructure for sharing data. The goal of Project 3 is to synthesize the findings of experimental Projects 1, 2, 4, and 5 into circuit-level mechanistic models of the IBL task. The task involves hierarchical, probabilistic decision-making through sensory evidence integration to make left-right decisions about where the stimulus is on the current trial, along with integration on a longer timescale to estimate the slowly varying left-right biases in where the stimuli are more likely to appear. Initial models not only will be trained to reproduce expert-level task performance, but also will include general biological constraints on neural dynamics and anatomical connectivity gradients. They will be analyzed for their learning dynamics, and for which parameters are the handles through which internal states exert their effects on circuit computation and dynamics. These models will yield predictions on multiple levels of abstraction: state-space predictions, network structure predictions, and anatomical predictions. The resulting models will be deployed in a tight loop with all experimental projects, to guide experimental design; serve as ground-truth testbeds for perturbative and causal connectivity analysis studies; and link statistical analysis results from data with mechanistic interpretations. The results of these experiment-model prediction comparisons will then be used to further refine and elaborate the models. Project 3 researchers will incorporate the experimentally derived neural activity data, causal connectivity by anatomical region data, and structural cell-type and connectivity data to further constrain the models. Finally, Project 3 will also generate highly simplified abstract neural circuit models, using novel methods of model compression to elucidate the general principles underlying hierarchical decision-making in the brain. All this work involves the use and de novo development of cutting-edge modeling, statistical, and data analysis tools. The work of Project 3 will thus deliver a mechanistic circuit- level understanding of this proposal’s overarching hypothesis that information flow and communication across brain regions during decision-making depends on internal state.

摘要/摘要，项目3 即使在同一环境中，动物也可能在不同的情况下做出不同的决定， 因为它的内部状态（例如参与任务）与外部输入有力互动 确定行为。该提议的总体目标是了解内部状态如何 影响决策并确定潜在的神经机制。团队是 国际脑实验室（IBL），一个已建立的财团 标准化的鼠标决策任务和训练，神经的标准化方法 测量和数据分析以及可扩展的基础架构用于共享 数据。项目3的目的是综合实验项目1、2、4和5的发现。 进入IBL任务的电路级机械模型。该任务涉及分层，概率 通过感官证据整合决策，以做出左右决策 刺激正在当前试验中，并在更长的时间范围内进行整合以估计 慢慢变化的左右偏见在刺激中更可能出现。初始型号不是 仅将接受培训以重现专家级的任务绩效，但也将包括一般 神经动力学和解剖连通性梯度的生物限制。他们会的 分析了他们的学习动态，以及哪些参数是其中的手柄 内部状态对电路计算和动态执行影响。这些模型将产生 对多个抽象级别的预测：状态空间预测，网络结构 预测和解剖学预测。最终的模型将与 所有实验项目，以指导实验设计；充当地面测试床 扰动和因果连通性分析研究；和链接统计分析结果 带有机械解释。这些实验模型预测比较的结果 然后将用于进一步完善并详细说明模型。项目3研究人员将合并 实验得出的神经元活动数据，解剖区域数据的因果连通性，以及 结构性细胞类型和连接数据，以进一步限制模型。最后，项目3将 还使用新颖的模型方法生成高度简化的抽象神经电路模型 压缩以阐明层次决策的一般原则 脑。所有这些工作都涉及从头开发尖端建模， 统计和数据分析工具。因此，项目3的工作将提供机械电路 - 对该提案的总体假设的水平理解，即信息流和 决策过程中跨大脑区域的交流取决于内部状态。