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.

总结神经科学的一个基本问题是了解脑皮质回路的运行。 鉴于所有皮质电路的基本相似性都拼命了各种物种和地区的许多差异 - 理解 - 任何特定的皮质电路将是朝着该目标迈出的重要一步。在这里，我们提议带来极端的 强大的理论家团队在一起，以无与伦比的深度对小鼠初级视觉皮层（V1）的电路进行建模， 与实验者紧密互动，他们将产生变革性数据以告知和测试我们的模型。 我们最初将专注于理解上下文调制及其通过运行和唤醒的调制 第2/3层处理，编码抑制性神经元的三种最佳研究亚型，白蛋白（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。我们还将开发可拖动的树突状抑制单室模型，这将是 关键的进步允许网络模型解决针对不同的不同中间神经元类型的功能 神经元室，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 SOM抑制作用在树突中与PV抑制SOMA和突触的短期可塑性的行为 在系统中。然后，我们将前进以合并进一步的亚型，以解决更广泛的动态范围 响应属性和建模第4层以及第2至4层的完整系统，建立在广泛的基础上 该提案中由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 平均刺激响应和相关爆发的模型，并解决对天然刺激的V1响应。 要了解皮质专业的功能，例如细胞亚型和层，我们不仅必须 数据中揭示了系统融合的结构，但使用旨在获得见识的建模方法， 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 从靶向特定组合的特定细胞类型。为了实现这一目标，我们将逐步逐步添加 对我们的模型的复杂性，在每个步骤中理解新的行为的引入，哪些更大的结构 或在先前理解的机制中发生了改变，以及哪些新机制变得可见。 该提案最具创新性的方面是，我们将使用旨在提供旨在进行的理论方法 视力探究机制，以应对小鼠V1的复杂特定细节。现有方法通常 研究更多的抽象模型（例如，通用兴奋性和抑制性细胞），或者放置所有已知细节（沿着 一定要在计算机中进入计算机中，这将使大脑重现 活动，一种方法不太可能产生功能响应或可测试的预测。我们承诺的方法 逐渐地加深了我们对皮质加工机制，尤其是在小鼠V1中的洞察力。