Dynamic network computations for foraging in an uncertain environment

 描述(申请人提供)：大脑进化出复杂的递归网络，以在动态和不确定的世界中实现灵活的行为，但其计算策略和潜在机制仍然知之甚少。我们建议在觅食中揭示神经计算的网络基础，这是一种与行为学相关的行为任务，涉及感觉整合、空间导航、记忆和复杂的决策。我们将使用六个相关的相互关联的区域(视觉皮质V4区、7A区、内嗅皮层、海马体、海马旁回和前额叶皮质)的大规模电记录。为了跟踪在这些与行为学相关的自然任务中使用的神经网络计算，我们将利用统计数据分析和神经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来建模与任务相关的感觉、运动和内部变量之间的关系。动物行为将在部分可观测马尔可夫决策过程(POMDP)的框架内建模，这些模型将提供关于动物使用哪些变量以及它们如何相互作用的预测。其次，一旦我们建立了行为相关变量的模型，我们将使用现代数据分析技术从神经元反应的模式中识别这些变量，从高维群体活动中提取低维的、与任务相关的信号。这些低维神经表示的时间序列将被用来分析不同大脑区域之间的信号转换和流动，使用诸如定向信息之类的测量方法。最后，我们将把这些神经分析与来自觅食任务的标准模型的预测进行比较。我们假设，感觉和内部变量的神经表示将表现出与行为模型中相同的因果关系和时间关系。通过首次将标准化建模、神经种群信号的选择性降维和定向信息流的量化相结合，我们将能够识别在复杂自然任务中执行神经计算的关键大脑区域内和关键区域之间的转换。该团队项目旨在产生分布式神经种群编码的变革性观点，统一多个神经系统中在行为学上至关重要的计算。

 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.