Dynamic network computations for foraging in an uncertain environment

不确定环境中觅食的动态网络计算

• 批准号: 9012468
• 负责人: Dora Angelaki
• 金额: $ 134.64万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9012468
• 关键词: Accounting; Animal Behavior; Animals; Area; Behavior; Behavioral; Behavioral Model; Belief; Biological Neural Networks; Brain; Code; Color; Complex; Cues; Data; Data Analyses; Decision Making; Dimensions; Discrimination; Electric Stimulation; Electrophysiology (science); Environment; Equilibrium; Exhibits; Food; Hippocampus (Brain); Learning; Location; Macaca; Measures; Medial; Memory; Mental Health; Modeling; Monkeys; Motor; Network-based; Neurons; Neurosciences; Parahippocampal Gyrus; Population; Prefrontal Cortex; Process; Property; Psyche structure; Recurrence; Relative (related person); Research; Rewards; Rice; Sensory; Series; Signal Transduction; Solutions; Specific qualifier value; Statistical Data Interpretation; Synaptic Transmission; System; Techniques; Testing; Theoretical model; Time; Training; Uncertainty; Visual; Visual Cortex; Visual Perception; Wireless Technology; area V4; cost; entorhinal cortex; flexibility; improved; insight; neural patterning; neuronal patterning; novel; public health relevance; relating to nervous system; research study; response; sensorimotor system; sensory integration; spatial memory; theories; tool; visual stimulus; way finding

 DESCRIPTION (provided by applicant): The brain evolved complex recurrent networks to enable flexible behavior in a dynamic and uncertain world, but its computational strategies and underlying mechanisms remain poorly understood. We propose to uncover the network basis of neural computations in foraging, an ethologically relevant behavioral task that involves sensory integration, spatial navigation, memory, and complex decision-making. We will use large-scale electrical recordings from six relevant interconnected areas (visual cortical area V4, Area 7A, Entorhinal Cortex, Hippocampus, Parahippocampal gyrus, and Prefrontal Cortex) of freely behaving macaques. To track the neural network computations used in these ethologically relevant, natural tasks, we will exploit recent advances in both statistical data analysis and theories of neural computation. First, to characterize behavior, we will model relationships between task-relevant sensory, motor, and internal variables using graphical modeling. Animal behavior will be modeled in the framework of Partially Observable Markov Decision Processes (POMDP) and these models will provide predictions about which variables the animals use and how they interact. Second, once we have modeled the behaviorally relevant variables, we will use modern data analysis techniques to identify these variables from the patterns of neuronal responses, extracting the low- dimensional, task-relevant signals from the high-dimensional population activity. The time series of these low- dimensional neural representations will be used to analyze the transformation and flow of signals between different brain areas, using such measures as Directed Information. Finally, we will compare these neural analyses to predictions from the normative models of the foraging task. We hypothesize that neural representations of sensory and internal variables will exhibit the same causal and temporal relationships manifested in the behavioral model. By combining - for the first time - normative modeling, selective dimensionality reduction of neural population signals, and quantification of directed information flow, we will be able to identify the transformations within and between key brain areas that enact neural computations on complex natural tasks. The team project aims to produce a transformative view of distributed neural population coding, unifying ethologically crucial computations across multiple neural systems.

 描述（由适用提供）：大脑进化出复杂的复发网络以在动态和不确定的世界中实现灵活的行为，但其计算策略和基本机制仍然很少了解。我们建议在觅食中揭示神经调查的网络基础，这是一项涉及感觉整合，空间导航，记忆和复杂决策的伦理学相关行为任务。我们将使用来自六个相关互连区域的大规模电记录（视觉皮层区域V4，区域7A，内嗅皮层，海马，海马，parahampocampal Gyrus和前额叶皮层）。为了跟踪这些与伦理学相关的自然任务中使用的神经元网络计算，我们将探讨统计数据分析和神经元计算理论的最新进展。首先，为了表征行为，我们将使用图形建模对与任务相关的感觉，电动机和内部变量进行建模。动物行为将在可观察到的马尔可夫决策过程（POMDP）的框架中建模，这些模型将提供有关动物使用哪些变量及其相互作用的预测。其次，一旦我们对行为相关的变量进行了建模，我们将使用现代数据分析技术来从神经元反应的模式中识别这些变量，从而从高维人群活动中提取低维，与任务相关的信号。这些低维神经表示的时间序列将使用指示信息，以分析不同大脑区域之间信号的转换和流动。最后，我们将将这些神经元分析与觅食任务的正常模型的预测进行比较。我们假设感觉和内部变量的神经元表示将存在在行为模型中表现出相同的因果关系和临时关系。通过首次合并 - 正常建模，选择性维度的降低神经元人口信号以及有向信息流的数量，我们将能够确定对复杂自然任务进行神经元计算的关键大脑区域内和之间的转换。该团队项目旨在对分布式神经元人群编码进行变革性观点，从而统一多个神经系统的伦理上至关重要的计算。