Circuit-based models of neuronal variability in mouse V1

小鼠 V1 神经元变异的电路模型

• 批准号: 10438692
• 负责人: Brent D. Doiron
• 金额: $ 49.28万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10438692
• 关键词: Area; Back; Behavior; Biophysics; Brain Stem; Calcium; Cognitive; Companions; Data; Dimensions; Equilibrium; Generations; Goals; Image; Measures; Mechanics; Modeling; Modernization; Mus; Neurons; Neurosciences; Output; Pattern; Population; Poverty; Probability; Property; Recurrence; Resolution; Rodent; Role; Shapes; Source; Stimulus; Structure; Sum; Techniques; Testing; V1 neuron; Visual; Whole-Cell Recordings; Work; area striata; base; experimental study; inhibitory neuron; movie; network models; optogenetics; programs; recruit; relating to nervous system; response; spatiotemporal; statistics; stem; theories; two-photon; visual processing

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.

项目概要 了解神经元群体的协调活动是神经科学的核心目标。 然而，尽管我们进行了数十年的研究，但我们比初级视觉皮层（V1）更接近于理解这一点。 对 V1 的理解通常仅限于对特殊类别刺激的反应，并且我们缺乏一种理论 概括了刺激，包括对自然图像和电影的反应，我们的团队提案提出了一个。 计划极大地拓宽了我们对 V1 电路的理解范围，重点关注广泛的 这些数据将推动协调一致的建模工作，从而实现良性的支持。 反复进行实验项目，所有项目的共同目标是建立一种新的基于电路的理论 视觉处理。 皮层内部产生的变异性反映了其循环电路。 埃林的工作主要集中在贫乏的回路上，这些回路将所有的抑制来源分解为源自一种 该项目将扩展循环皮质网络的经典理论，以包括抑制性神经元的中央池。 小鼠 V1 中发现的丰富多样性的抑制性神经元这将包括限制的连接配置文件。 我们的新理论在兴奋性神经元和不同的抑制性神经元之间建立了真实的空间和基于特征的连接。 提议捕捉内部产生的人群范围内的共享变异性如何取决于电路结构， 并且可以通过与自然输入相关的丰富的空间和动态刺激统计数据来操纵。 特别是，我们将讨论变异性的有效维度，这是当代理论所关注的 难以解释的焦点是高分辨率钙成像数据和光遗传学扰动。 我们的实验项目提供了一个独特的机会来测试和扩展我们的理论。 我们的建模工作将集中于通量如何塑造神经元的传输特性。 一个在我们的配套理论项目中建立发射率模型的平台总之，我们的项目提出了一个 建立神经变异理论的雄心勃勃的计划——这是构建基于电路的关键的第一步 啮齿动物视觉处理理论 V1。