Modeling V1 circuit dynamics

V1 电路动力学建模

• 批准号: 10231004
• 负责人: KENNETH D MILLER
• 金额: $ 49.65万
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10231004
• 关键词: Address; Animals; Area; Arousal; Behavior; Belief; Brain; Cells; Cerebrum; Collaborations; Companions; Complex; Computers; D Cells; Data; Dendrites; Elements; Environment; Eye; Future; Goals; Interneurons; Laboratories; Locomotion; Modeling; Mus; Nature; Neurons; Neurosciences; Outcome; Output; PF4 Gene; Parvalbumins; Pattern; Physiology; Property; Response to stimulus physiology; Role; Running; Somatostatin; Stimulus; Structure; Synapses; System; Testing; Vasoactive Intestinal Peptide; Vision; Visual; area striata; base; cell type; design; experimental study; improved; inhibitory neuron; innovation; insight; member; movie; network models; neuronal cell body; operation; predicting response; receptive field; response; synaptic depression; theories; visual process; visual stimulus

Summary A fundamental problem of neuroscience is understanding the operation of cerebral cortical circuits. Given the basic similarity of all cortical circuitry despite many differences across species and areas, understand- ing of any particular cortical circuit will be a major step toward that goal. Here we propose to bring an extremely strong team of theorists together to model the circuitry of mouse primary visual cortex (V1) in unparalleled depth, in tight interaction with experimentalists who will produce transformative data to inform and test our models. We will initially focus on understanding contextual modulation and its modulation by running and arousal in layer 2/3 processing, incorporating the three best-studied subtypes of inhibitory neurons, parvalbumin- (PV), somatostatin- (SOM), or vasoactive-intestinal-peptide-expressing (VIP) interneurons, and possible subtypes of SOM neurons. We will also develop tractable single-compartment models of dendritic inhibition, which will be a critical advance allowing network models to address the function of different interneuron types targeting different neuronal compartments while remaining simple enough to yield insight. We will study the impacts on network behavior of SOM inhibition at dendrites vs. PV inhibition on soma and of the short-term plasticity of synapses in the system. We will then advance to incorporating further subtypes, addressing a wider range of dynamic response properties, and modeling layer 4 and the full system of layers 2 through 4, building on the extensive data gathered by experimental projects in this proposal. Finally, working with Project 1, we will develop a unified model of mean stimulus responses and correlated fluctuations, and address V1 responses to natural stimuli. To understand the functions of cortical specializations such as cell subtypes and layers, we must not only systematically incorporate structure revealed in the data, but use modeling approaches aimed at gaining insight, e.g. understanding mechanisms that produce specific activities, or the forms of circuit modulation that can result from targeting particular cell types in particular combinations. To achieve this, we will gradually, step-by-step, add complexity to our models, understanding at each step what new behaviors are introduced, what greater structure or alterations occur in previously understood mechanisms, and what new mechanisms become visible. The most innovative aspect of this proposal is that we will use theoretical approaches designed to give in- sight into mechanisms to grapple with the complex specific details of mouse V1. Existing approaches typically either study more abstract models (e.g., generic excitatory and inhibitory cells) or put all known details (along with, necessarily, a great many unknown ones) into the computer with the belief that this will reproduce brain activity, an approach unlikely to generate functional responses or testable predictions. Our approach promises to dramatically deepen our insight into the mechanisms of processing in cortex and in mouse V1 in particular.

神经科学的一个基本问题是理解大脑皮层回路的运作。 鉴于所有皮层回路的基本相似性，尽管物种和地区之间存在许多差异，请理解- 任何特定的皮层回路的分离都将是实现这一目标的重要一步。在这里，我们提出了一个非常 强大的理论家团队一起以无与伦比的深度模拟小鼠初级视觉皮层（V1）的电路， 与实验者紧密互动，他们将产生变革性的数据来告知和测试我们的模型。 我们将首先集中在理解语境调制和它的调制运行和唤醒， 层2/3处理，结合了三种研究最好的抑制性神经元亚型，小清蛋白-（PV）， 生长抑素（SOM）或血管活性肠肽表达（VIP）中间神经元，以及可能的亚型 SOM神经元我们还将开发易于处理的树突状抑制单室模型，这将是一个 关键的进步，允许网络模型解决不同的中间神经元类型的功能， 神经元区室，同时保持足够简单以产生洞察力。我们将研究对网络的影响 树突上SOM抑制与索马上PV抑制的行为以及突触的短期可塑性 系统中然后，我们将进一步整合子类型，解决更广泛的动态 响应特性，以及建模层4和层2到4的完整系统，建立在广泛的 本提案中的实验项目收集的数据。最后，与项目1合作，我们将开发一个统一的艾德 模型的平均刺激反应和相关的波动，并解决V1反应的自然刺激。 为了了解皮质特化（如细胞亚型和层）的功能，我们不仅要 系统地结合数据中揭示的结构，但使用旨在获得洞察力的建模方法， 例如了解产生特定活动的机制，或可能导致的电路调制形式 以特定的组合来瞄准特定的细胞类型。为了实现这一目标，我们将逐步，一步一步， 我们的模型的复杂性，理解在每一步引入了什么新的行为，什么更大的结构， 或改变发生在以前理解的机制，什么新的机制变得可见。 这一建议最具创新性的方面是，我们将使用理论方法，旨在让- 深入了解机制，以解决鼠标V1的复杂具体细节。现有方法通常 或者研究更抽象的模型（例如，一般兴奋性和抑制性细胞）或将所有已知的细节（沿着 当然，还有很多未知的）输入计算机，相信这将重现大脑 活动，一种不太可能产生功能反应或可测试预测的方法。我们的方法承诺， 大大加深了我们对大脑皮层，特别是小鼠V1区的加工机制的了解。