Circuit-based models of neuronal variability in mouse V1

项目摘要 了解神经元群体的协调活动是神经科学的中心目标。在任何赛道上 我们是否比初级视觉皮质(V1)更接近于理解这一点？然而，尽管经过几十年的研究，我们的 对V1的理解通常仅限于对特殊类别的刺激的反应，我们缺乏一种理论来 对刺激进行概括，以包括对自然图像和电影的反应。我们团队的提案提出了一个 该计划极大地拓宽了我们对V1电路的理解范围，并深入关注 抑制性神经元阵列。这些数据将推动协调一致的建模工作，这将是一个良好的后盾- 第四，实验项目，所有项目都有共同的目标，即建立一个新的基于电路的理论 视觉处理。 大脑皮层内部产生的变异性是其循环回路的一种反映。然而，大多数过去的现代- 埃林的工作集中在贫困的电路上，这些电路将所有抑制源分解为一个来源 抑制性神经元的中央池。该项目将扩展经典的递归皮质网络理论，以包括 在小鼠V1中发现的抑制神经元的丰富多样性。这将包括连接ProfiLES，它限制了- 真正的空间和基于特征的连接，在兴奋性神经元和不同的抑制性神经元之间。我们的新理论 建议捕捉内部产生的全人群共享变异性如何依赖于电路结构， 并且可以通过与自然投入相关联的丰富的空间和动态刺激统计来操纵。在……里面 特别是，我们将讨论变异性的有效维度，这是当代理论的研究方向。 这是一个需要解释的损失。高分辨率钙成像数据和光发生微扰是 我们的实验项目提供了一个独特的机会，在我们的理论出现时对其进行测试和扩展。最后， 我们的建模工作将集中在fl功能如何塑造神经元的转移属性。这将作为 在我们的配对理论项目中，为fi环速率模型提供了一个平台。总之，我们的项目提出了一个 建立神经元可变性理论的雄心勃勃的计划-这是建立基于电路的fi的关键第一步 啮齿动物V1的视觉加工理论。

Project Summary Understanding the coordinated activity of populations of neurons is a central goal in neuroscience. In no circuit are we closer to understanding this than in primary visual cortex (V1). However, despite decades of study our understanding of V1 is often restricted to responses to only special classes of stimuli, and we lack a theory that generalizes over stimuli to include the responses to natural images and movies. Our team proposal puts forth a program to dramatically broaden the scope of our understanding of V1 circuitry, with a deep focus on the vast array of inhibitory neurons. These data will drive a concerted modeling effort that engages in a virtuous back- and-forth with experimental projects where all projects share the goal of building a new circuit-based theory of visual processing. Internally generated variability in the cortex is a reflection of its recurrent circuitry. However, most past mod- eling work has focused on impoverished circuits that collapse all sources of inhibition into stemming from one central pool of inhibitory neurons. This project will extend classic theories of recurrent cortical networks to include the rich diversity of inhibitory neurons that are found in mouse V1. This will include connectivity profiles that cap- ture spatial and featured-based wiring, both between excitatory and diverse inhibitory neurons. Our new theory proposes to capture how internally generated population-wide shared variability depends upon circuit structure, and can be manipulated by the rich spatial and dynamic stimulus statistics associated with natural inputs. In particular, we will discuss the effective dimensionality of variability, something that contemporary theories are at a loss to explain. The high resolution calcium imaging data and optogenetic perturbations that are the focus of our experimental projects offer a unique opportunity to test and expand on our theories as they emerge. Finally, our modeling efforts will focus on how fluctuations shape the transfer properties of neurons. This will serve as a platform to ground the firing rate models in our companion theory project. In sum, our project puts forth an ambitious program to build a theory of neuronal variability — this is a critical first step in building a circuit-based theory of visual processing in rodent V1.