Understanding V1 circuit dynamics and computations

了解大脑皮层需要基于数据的理论模型，可以产生- 观察皮层计算的电路机制，并在刺激中再现详细的皮层动力学 和大脑状态初级视皮层（V1）是理论家和实验者研究得最好的皮层区域。 然而，目前的模型--无论是基于统计的还是基于电路的--只能很好地捕捉V1神经元的反应 复杂的刺激，如自然场景。本团队项目的最终目标是获得必要的 实验数据，并建立详细的电路为基础的模型，解释如何V1电路编码自然的视觉 刺激。在这样做的过程中，我们的目标不仅是提供一个机械的理解，V1动力学如何形成 视觉的基础，而且还建立了一个更普遍的范式来理解任何皮层区域。我们 假设目前的模型有两个原因：在实验方面，我们仍然缺少大多数 有关V1细胞类型的突触连接和生理反应的基本细节; 在理论方面，流行的基于电路的模型将V1简化为几种细胞类型， V1神经元对简单刺激的静态反应，而不是它们对尝试波动或捕获波动的尝试， 而不是它们丰富的非线性响应特性，这些特性是视觉计算的核心。我们的假设是 我们可以实现一个基于电路的模型，解释皮质对自然刺激的反应和动力学， 实施以下三个步骤：1）鉴定并整合所有可分化的V1神经元细胞类型 2）测量并结合这些细胞类型的突触连接性和内在特性; 3)测量并准确预测每种细胞类型对不同视觉刺激的视觉反应， 处于多种大脑状态我们专注于基于电路的V1模型，而不是统计模型，原因有两个： 提供洞察视觉计算的神经机制和皮质操作的制度，因为 它们将允许我们通过验证它们对V1如何响应定义的实验的预测来测试它们的准确性- 说话时的不安为了实现这些微扰，我们将采用多光子全息光遗传学， 使我们能够操纵V1电路，其精确程度以前只有在理论领域才有可能。这里 我们汇集了一个由理论家、实验家和数据科学家组成的优秀团队， 我们将使用新的大脑映射技术来建立和验证显着改进的视觉模型， 皮质功能和动力学

Understanding the cerebral cortex requires data-based theoretical models that can yield in- sight into the circuit mechanisms of cortical computation, and reproduce detailed cortical dynamics across stimuli and brain states. The primary visual cortex (V1) is the best-studied cortical area by both theorists and experimen- talists, yet current models - whether statistical or circuit based – only poorly capture how V1 neurons respond to complex stimuli, such as natural scenes. The ultimate goal of this team project is to obtain the necessary experimental data and build the detailed circuit-based models that explain how V1 circuits encode natural visual stimuli. In so doing, we aim not only to provide a mechanistic understanding for how V1 dynamics forms the basis of vision, but also to establish a more generalizable paradigm for understanding any cortical area. Our assumption is that current models fall short for two reasons: on the experimental side, we are still missing most of the fundamental details about the synaptic connectivity and physiological responses of V1 cell types; while on the theory side, prevailing circuit-based models reduce V1 to just a few cell types, and either capture the static responses of V1 neurons to simple stimuli but not their trial to trial fluctuations, or capture fluctuations, but not their rich array of non-linear responses properties that are central to visual computation. Our hypothesis is that we can achieve a circuit-based model that explains cortical responses and dynamics to natural stimuli by implementing the following three steps: 1) identify and incorporate all the differentiable V1 neuronal cell types into our model; 2) measure and incorporate the synaptic connectivity and intrinsic properties of these cell types; 3) measure and accurately predict the visual responses of each of these cell types to diverse visual stimuli and in multiple brain states. We focus on circuit-based rather than statistical models of V1 for two reasons: they can provide insight into neural mechanisms of visual computation and the regimes of cortical operation, and because they will permit us to test their accuracy by validating their predictions for how V1 responds to defined experimen- tal perturbations. To implement these perturbations, we will employ multiphoton holographic optogenetics, which allows us to manipulate V1 circuits with the level of precision formerly only possible in the realm of theory. Here we bring together an outstanding team of theorists, experimentalists, and data scientists to leverage cutting edge new brain mapping technologies that we will use to build and validate dramatically improved models of visual cortical function and dynamics.