Coordinating Structure and Function for Neuronal Computations Mediating Context-Dependent Behavior

项目摘要 这项建议探索了一种用于理解神经种群编码的紧急计算框架 支持灵活的、上下文相关的行为。该领域的当前状态是基于两个竞争对手 视图。根据电路观点，固定的行为产生于特定的解剖学或基因定义的细胞 服务于特定功能的种群。或者，网络计算视图持有该神经 活动提供了任务变量的混合表示形式，并且只能基于 激活许多神经元。目前，这些相互矛盾的观点被不同的社区所追求， 不同的工具、不同的行为范式和不同的模式生物。这导致了一种脱节 神经计算和潜在的生物回路机制之间的关系。在这里，我们提出一个统一的 框架中，生物识别的神经元群体的组合活动塑造了 通过低维动力学进行计算。一个新的跨学科调查团队-佩萨兰(纽约大学 -灵长类实验者)，Johansen(RIKEN-啮齿动物实验者)和Ostojic(Ecole Norman Superieure -计算理论)将发展计算理论，并将其应用于灵活的输入-输出任务 物种--老鼠和非人类灵长类动物。为了实现这些目标，我们将分析递归神经网络模型 接受训练以执行感觉-运动上下文相关的决策任务，并将低维模型适合于 实验数据。我们将在啮齿动物和非人灵长类动物身上进行实验，以验证模型预测 组合编码可以支持上下文相关的行为，并且在行为上具有重要意义(目标1)。我们会 分析在上下文期间生物定义的细胞类型是否映射到计算定义的细胞类别- 通过确定遗传和解剖识别的PFC组合中的活动如何实现依赖行为 细胞类型对应于低维动力学(目标2)。同时，我们将确定如何在生物学上- 定义的单元格类型通过以下方式控制显式提示和隐式信号通知的上下文期间的上下文相关行为 对解剖定义的PFC细胞类型进行光遗传扰动并测试行为表现 (目标3)。这些目标的成功完成将把神经计算与跨越多个物种的生物学联系起来 提供一个计算框架，解释生物回路机制如何产生神经元 调解上下文相关行为的计算。

Project Summary This proposal explores an emergent computational framework for understanding the neural population codes that support flexible, context-dependent behavior. The current state of the field is based on two competing views. According to the circuits view, fixed behaviors arise from specific anatomically or genetically defined cell populations that serve specific functions. Alternatively, the network computation view instead holds that neural activity provides mixed representations of task variables and can be understood only based on the joint activation of many neurons. Currently, these competing views are pursued by different communities with different tools, different behavioral paradigms and different model organisms. This has led to a disconnect between neural computation and the underlying biological circuit mechanisms. Here we propose a unified framework, in which the combinatorial activity of biologically-identified populations of neurons shapes the computations through low dimensional dynamics. A new interdisciplinary team of investigators - Pesaran (NYU - primate experimentalist), Johansen (RIKEN - rodent experimentalist) and Ostojic (Ecole Normale Superieure - computational theory) will develop the computational theory and apply it to flexible input-output tasks in multiple species - rats and non-human primates. To achieve these goals, we will analyze recurrent neural network models trained to perform a sensory-motor context-dependent decision-making task and fit low-dimensional models to experimental data. We will perform experiments in rodents and non-human primates to validate model predictions that combinatorial coding can support context-dependent behavior and is behaviorally-significant (Aim 1). We will analyze whether biologically-defined cell types map onto computationally-defined cell classes during context- dependent behavior by determining how activity in combinations of genetically and anatomically identified PFC cell types corresponds to low-dimensional dynamics (Aim 2). In parallel, we will determine how biologically- defined cell types control context-dependent behavior during explicitly-cued and implicitly-signaled contexts by performing optogenetic perturbations of anatomically-defined PFC cell types and testing behavioral performance (Aim 3). Successful completion of these aims will link neural computation to biology across multiple species to deliver a computational framework explaining how biological circuit mechanisms give rise to neuronal computations that mediate context-dependent behavior.