Mechanistic neural circuit models and principles

摘要/摘要，项目3 即使在相同的环境中，动物也可能在不同的场合做出不同的决定， 因为它的内部状态，如参与任务，与外部输入有很强的交互作用 以确定其行为。该提案的首要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。该团队是 国际脑实验室(IBL)，一个老牌财团已经开发出一种 标准化的老鼠决策任务和标准化的训练方法，神经 测量和数据分析，以及用于共享的工作的、可扩展的基础设施 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中。这项任务涉及到层次化、概率性 通过感官证据整合做出决策，以做出关于位置的左右决策 目前正在进行刺激措施，并在更长的时间范围内进行整合，以估计 在刺激更有可能出现的地方，慢慢地改变左右的偏见。初始型号备注 只会训练再现专家级的任务表现，还会包括一般 神经动力学和解剖连通性梯度的生物学限制。他们将会是 分析它们的学习动态，以及哪些参数是通过哪些参数 内部状态对电路计算和动力学产生影响。这些模型将产生 对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。生成的模型将部署在一个紧密的循环中， 所有实验项目，以指导实验设计；作为地面真理试验台 扰动和因果连通性分析研究；以及来自数据的链接统计分析结果 机械的解释。这些实验模型预测比较的结果 然后将被用来进一步提炼和细化模型。项目3的研究人员将把 实验得出的神经活动数据、解剖区域数据的因果连通性，以及 结构单元格类型和连接数据，以进一步约束模型。最后，项目3将 还使用新的建模方法生成高度简化的抽象神经电路模型 压缩以阐明分层决策背后的一般原则 大脑。所有这些工作都涉及到尖端建模的使用和新的开发， 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- 对这一提议的总体假设的水平理解是信息流动和 在决策过程中，跨大脑区域的交流取决于大脑的内部状态。

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.