New approaches to understand neuronal microcircuit dynamics for working memory

理解工作记忆神经元微电路动力学的新方法

• 批准号: 8955230
• 负责人: Christopher D Harvey
• 金额: $ 53.39万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-11 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8955230
• 关键词: Area; Attention deficit hyperactivity disorder; Autistic Disorder; Behavior; Behavioral; Biological; Biological Neural Networks; Bipolar Disorder; Brain region; Chronic; Computer Simulation; Cues; Data; Data Set; Disease; Future; Health; Image; Individual; Information Storage; Learning; Measurement; Measures; Mediating; Memory; Mental disorders; Methods; Modeling; Mus; Neurons; Parietal Lobe; Pattern; Performance; Phase; Population; Property; Recurrence; Rotation; Sampling; Schizophrenia; Short-Term Memory; Stable Populations; Testing; Theoretical model; Thinking; Time; Visual; Work; base; cognitive function; cognitive process; flexibility; insight; neural circuit; neuromechanism; novel; novel strategies; public health relevance; research study; theories; tool; two-photon; virtual; virtual reality

 DESCRIPTION (provided by applicant): Working memory, the temporary storage of information for future manipulation to guide actions, is an essential component of nearly all cognitive processes. Deficits in working memory contribute to a variety of mental health disorders, including schizophrenia, autism, bipolar disorder, and attention-deficit hyperactivity disorder. Working memory is thought to be an emergent property of neurons acting in groups to form microcircuits. However, due in large part to technical limitations, working memory has been studied nearly exclusively at the scales of entire brain regions or individual neurons, both of which fail to reveal interactions in neuronal populations. A major challenge in understanding the neural mechanisms for working memory is to develop tools to measure, analyze, and model working memory at the scale of the neuronal microcircuit. In this application, we present approaches to solve this challenge using two-photon imaging of activity in neuronal populations in mice performing working memory tasks in virtual reality. We will combine our large imaging data sets with theoretical modeling approaches to develop new computational models of how working memory is generated in microcircuits. First, we will examine how working memory representations are encoded in neuronal populations by examining the stability of representations over long timescales, using chronic two- photon imaging of the same neurons over weeks during working memory behaviors. Second, we will perform imaging experiments during novel working memory tasks to test competing, long-standing theoretical models of working memory, including models based on attractor dynamics and sequence dynamics. Third, we will develop a new theoretical modeling framework in conjunction with rapid experiment-model iterations to generate and test new microcircuit-scale hypotheses for the neural mechanisms underlying working memory. Together the proposed work is expected to establish new approaches to measure, analyze, and model microcircuit function during working memory in the mouse, leading toward mechanistic studies of how working memory and its underlying microcircuits are disrupted in mental illness.

 描述（由申请人提供）：工作记忆，为未来操纵指导行动的信息的临时存储，是几乎所有认知过程的重要组成部分。工作记忆的缺陷会导致各种精神疾病，包括精神分裂症、自闭症、双相情感障碍和注意力缺陷多动障碍。工作记忆被认为是神经元在群体中形成微回路的一种涌现特性。然而，在很大程度上由于技术限制，工作记忆几乎只在整个大脑区域或单个神经元的尺度上进行研究，这两者都未能揭示神经元群体中的相互作用。理解工作记忆的神经机制的一个主要挑战是开发工具来测量，分析和建模工作记忆的神经元微电路的规模。在这个应用程序中，我们提出了解决这一挑战的方法，使用双光子成像的神经元群体的活动在虚拟现实中执行工作记忆任务的小鼠。我们将联合收割机结合我们的大型成像数据集与理论建模方法，以开发新的计算模型，工作记忆是如何产生的微电路。首先，我们将通过在长时间尺度上检查表征的稳定性来检查工作记忆表征是如何在神经元群体中编码的，在工作记忆行为期间使用相同神经元数周的慢性双光子成像。其次，我们将在新的工作记忆任务中进行成像实验，以测试竞争，长期存在的工作记忆理论模型，包括基于吸引子动力学和序列动力学的模型。第三，我们将开发一个新的理论建模框架，结合快速的实验模型迭代，以生成和测试新的微电路规模的工作记忆神经机制的假设。这项工作有望建立新的方法来测量、分析和模拟小鼠工作记忆过程中的微电路功能，从而研究工作记忆及其底层微电路在精神疾病中是如何被破坏的。