Mechanistic neural circuit models and principles

机械神经回路模型和原理

• 批准号: 10461999
• 负责人: Ila R. Fiete
• 金额: $ 43.39万
• 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/10461999
• 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; Future; Genetic Markers; Goals; Infrastructure; International; Laboratories; Learning; Left; Link; Measurement; Methods; Modeling; Mus; Neural Network Simulation; Output; Performance; Population; Process; Research Personnel; Rewards; Running; Sensory; Standardization; Statistical Data Interpretation; Statistical Models; Stimulus; Structure; Synapses; Task Performances; Testing; Training; Work; base; cell type; data sharing; design; experimental study; learning strategy; network models; neural circuit; neural model; neuromechanism; novel; operation; predictive modeling; recurrent neural network; relating to nervous system; sensory input; 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 即使在相同的环境中，动物在不同的场合也会做出不同的决定， 因为它的内部状态，比如参与一项任务，与外部输入有着强有力的相互作用 来决定行为。该提案的首要目标是了解内部状态如何 影响决策并识别潜在的神经机制。该团队是 国际脑实验室（IBL），一个已经建立的联盟，开发了一个 标准化的小鼠决策任务和标准化的训练方法，神经 沿着一个有效的、可扩展的共享基础架构 数据项目3的目标是综合实验项目1、2、4和5的结果 到IBL任务的电路级机械模型。这项任务涉及到层次，概率 通过感官证据整合做出左右决策， 刺激是在目前的审判，沿着与集成在一个较长的时间尺度，以估计 在刺激更可能出现的地方缓慢变化的左右偏置。初始型号 将只接受培训，以重现专家级的任务性能，但也将包括一般 对神经动力学和解剖学连接梯度的生物学约束。他们将 分析他们的学习动力学，以及哪些参数是通过哪些参数来控制的。 内部状态对电路计算和动力学产生影响。这些模型将产生 多个抽象层次的预测：状态空间预测，网络结构 预测和解剖学预测。由此产生的模型将被部署在一个紧密的循环中， 所有实验项目，指导实验设计;作为地面实况测试平台， 扰动和因果连通性分析研究;以及从数据中链接统计分析结果 机械的解释。这些实验模型预测比较的结果 然后将用于进一步完善和阐述模型。项目3研究人员将结合 实验导出的神经活动数据，解剖区域数据的因果连接，以及 结构单元类型和连接数据，以进一步约束模型。最后，项目3将 还产生高度简化的抽象神经电路模型，使用新的建模方法， 压缩，以阐明分层决策的一般原则， 个脑袋所有这些工作都涉及到尖端建模的使用和重新开发， 统计和数据分析工具因此，项目3的工作将提供一个机械电路- 水平的理解，这一建议的总体假设，信息流和 决策过程中大脑区域之间的交流取决于内部状态。