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：皮层也必须跟踪和过程动态变化的环境 作为存储并结合了各种输入以产生复杂的行为。此外，神经元电路 实现这一目标必须具有不断变化的上下文，例如在与注意力相关的任务期间。带电 通过这些任务，皮质神经元种群的反应动态可能不足为奇 然后是艰巨的复杂。目前，我们对电路机制缺乏深刻的了解 在神经系统中表现出的丰富动态。这个遗漏特别严重 数据的越来越多的数据表明，神经元动态及其可变性是上下文 - 依赖和共享大脑的大区域。我们的建议旨在解决一些 当前网络模型面临的基本问题。也就是说，具有平衡的尖峰网络模型 激发和抑制目前无法产生逼真的瞬态活性，稳态 单个模型中的活动和神经变异性。为了解决这些缺点，我们将发展一个 自动化方法来优化网络模型的参数。然后，我们将验证 优化方法和由此产生的网络模型通过比较由 在猕猴视觉区域V4和前额叶皮层中记录的网络模型 歧视和工作记忆任务。为了进行此比较，这是一项毫无结果的练习 试图将每个记录的神经元与网络模型中的神经元相关。而是一个钥匙 我们提案的创新是，我们将比较 网络模型和实际数据中的人口活动。网络模型和优化 我们构建的方法将与研究界广泛共享。如果成功，工作 这里提出的将导致对神经回路的产生方式有更深入的了解 瞬态活动，稳态活动和神经变异性，并为研究社区配备 朝这个方向进一步发现的工具。