Neuronal population dynamics within and across cortical areas

皮质区域内和皮质区域之间的神经元群体动态

• 批准号: 9789875
• 负责人: Brent D. Doiron
• 金额: $ 34.38万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789875
• 关键词: Address; Area; Attention; Behavior; Biological; Biology; Brain; Brain region; Charge; Communities; Complex; Data; Diagnosis; Dimensions; Discrimination; Environment; Equilibrium; Exercise; Exhibits; Failure; Macaca; Mechanics; Methods; Modeling; Nervous system structure; Neurons; Patients; Population; Population Analysis; Population Dynamics; Prefrontal Cortex; Process; Prosthesis; Recurrence; Research; Shapes; Short-Term Memory; Stimulus; Structure; Techniques; Work; area V4; brain repair; experimental study; extrastriate visual cortex; innovation; insight; neglect; nervous system disorder; network models; neural circuit; neuronal circuitry; novel strategies; relating to nervous system; response; stem; support network; theories; tool

Project Summary: The cortex must both track and process dynamically changing environments as well as store and combine diverse inputs to generate complex behavior. Further, the neuronal circuits that accomplish this must be malleable to changing contexts, such as during attention related tasks. Charged with these tasks it is perhaps unsurprising that the response dynamics of populations of cortical neurons is then dauntingly complex. Currently, we lack a deep understanding of the circuit mechanics that underlie the rich dynamics exhibited in the nervous system. This omission is particularly serious given the ever increasing breadth of data showing that neuronal dynamics, and its variability, is context- dependent and shared across large regions of the brain. Our proposal seeks to address several fundamental issues facing current network models. Namely, spiking network models with balanced excitation and inhibition are not currently capable of generating realistic transient activity, steady state activity, and neural variability within a single model. To address these shortcomings, we will develop an automated method for optimizing the parameters of network models. We will then validate the optimization method and resulting network models by comparing the population activity generated by the network models with that recorded in macaque visual area V4 and prefrontal cortex during discrimination and working memory tasks. To perform this comparison, it is a fruitless exercise to attempt to correspond each recorded neuron to a neuron in the network model. Instead, a key innovation of our proposal is that we will compare the low-dimensional representations of the population activity in the network model and the real data. The network models and optimization method that we build will be will be widely shared with the research community. If successful, the work proposed here will lead to a vastly deeper understanding of how neural circuits give rise to transient activity, steady-state activity, and neural variability, and equip the research community with the tools to make further discoveries in this direction.

项目摘要：皮层还必须跟踪和处理动态变化的环境 存储并组合不同的输入以生成复杂的行为。此外，神经元回路 实现这一目标必须能够适应不断变化的环境，例如在与注意力相关的任务期间。带电 通过这些任务，皮层神经元群体的反应动力学可能并不令人惊讶 然后是极其复杂的。目前，我们对电路机理缺乏深入的了解， 这是神经系统中表现出的丰富动态的基础。这一遗漏尤其严重 不断增加的数据表明神经元动力学及其变异性与环境有关 大脑的大部分区域相互依赖和共享。我们的建议旨在解决几个问题 当前网络模型面临的基本问题。即，具有平衡的尖峰网络模型 激发和抑制目前无法产生真实的瞬态活动、稳态 单个模型内的活动和神经变异性。为了解决这些缺点，我们将开发一个 优化网络模型参数的自动化方法。然后我们将验证 通过比较生成的人口活动来优化方法和生成的网络模型 猕猴视觉区域 V4 和前额叶皮层记录的网络模型 歧视和工作记忆任务。要进行这种比较，这是徒劳的： 尝试将每个记录的神经元与网络模型中的神经元相对应。取而代之的是一把钥匙 我们提案的创新之处在于我们将比较 网络模型和真实数据中的人口活动。网络模型和优化 我们建立的方法将与研究界广泛分享。如果成功的话，工作 这里提出的将导致对神经回路如何产生的更深入的理解 瞬态活动、稳态活动和神经变异性，并为研究界提供 在这个方向上做出进一步发现的工具。