Understanding V1 circuit dynamics and computations

理解大脑皮层需要基于数据的理论模型，这些模型可以在- 深入了解大脑皮质计算的电路机制，并通过刺激再现详细的大脑皮质动力学 以及大脑的状态。初级视觉皮质(V1)是理论界和实践界研究最多的皮质区域。 独领风骚，然而目前的模型--无论是基于统计的还是基于电路的--只能很好地捕捉到V1神经元的反应 对复杂的刺激，如自然场景。这个团队项目的最终目标是获得必要的 实验数据并构建详细的基于电路的模型，解释V1电路如何编码自然视觉 刺激物。在这样做时，我们的目标不仅是为V1动力学如何形成 视觉的基础，但也建立一个更普遍的范例，以理解任何皮质区域。我们的 假设目前的模型有两个不足之处：在实验方面，我们仍然缺少最多 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减少到仅几种单元类型，并且要么捕获 V1神经元对简单刺激的静态反应，但不是尝试fl功能或捕获fl功能的试验，但 而不是其对视觉计算至关重要的丰富的非线性响应特性。我们的假设是 我们可以实现一个基于电路的模型，通过以下方式解释大脑对自然刺激的反应和动力学 实施以下三个步骤：1)识别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通性和固有属性； 3)测量和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。我们关注V1的基于电路的模型而不是统计模型有两个原因：它们可以 洞察视觉计算的神经机制和皮质操作的机制，因为 他们将允许我们通过验证他们对V1如何响应DefiNed实验的预测来测试他们的准确性- 塔尔摄动。为了实现这些微扰，我们将使用多光子全息光遗传学，它 使我们能够以以前仅在理论领域才能达到的精度水平来操纵V1电路。这里 我们汇聚了一支由理论家、实验学家和数据科学家组成的杰出团队，以利用尖端技术 新的脑图技术，我们将使用这些技术来构建和验证显著改进的视觉模型 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.