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 即使在相同的环境中，动物在不同的场合也可能做出不同的决定， 因为它的内部状态（例如参与任务）与外部输入相互作用 确定行为的总体目标是了解内部状态。 该团队是影响决策并确定潜在神经机制的一部分。 国际脑实验室（IBL）是一个成熟的联盟，开发了 标准化小鼠决策任务和标准化训练方法、神经 测量和数据分析，以及可运行的、可扩展的共享基础设施 项目 3 的目标是综合实验项目 1、2、4 和 5 的结果。 转化为 IBL 任务的电路级机械模型 该任务涉及分层、概率。 通过感官证据整合做出左右决策 刺激措施是在当前的试验中进行的，并在更长的时间尺度上进行整合以估计 缓慢变化的左右偏差更有可能出现初始模型。 不仅将接受培训以重现专家级任务表现，而且还将包括一般性任务 对神经动力学和解剖连接梯度的生物学限制。 分析他们的学习动态，以及哪些参数是控制柄 内部状态对电路计算和动力学产生影响。 多个抽象级别的预测：状态空间预测、网络结构 预测和解剖预测所产生的模型将在紧密的循环中部署。 所有实验项目，作为实验指导设计； 扰动和因果连接分析研究；并将数据的统计分析结果联系起来 这些实验模型预测比较的结果。 然后将用于进一步完善和完善项目 3 研究人员将纳入的模型。 实验得出的神经活动数据、解剖区域数据的因果连接性，以及 最后，项目 3 将提供结构细胞类型和连接数据来进一步约束模型。 还可以使用新颖的模型方法生成高度抽象的神经电路模型 压缩以阐明层次决策的一般原则 所有这些工作都涉及尖端建模的使用和从头开发， 因此，项目 3 的工作将提供一个机械电路。 对该提案的总体假设的理解程度，即信息流动和 决策过程中大脑区域之间的交流取决于内部状态。