Multi-regional neural circuit dynamics underlying short-term memory

短期记忆的多区域神经回路动力学

• 批准号: 9449037
• 负责人: Shaul Druckmann
• 金额: $ 25.13万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-25 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9449037
• 关键词: Algorithms; Anatomy; Animals; Anterior; Area; Automobile Driving; Behavior; Behavioral; Behavioral Paradigm; Biological Neural Networks; Brain; Brain Stem; Brain region; Cognitive; Data; Data Analyses; Data Set; Decision Making; Disease; Event; Exhibits; Explosion; Functional disorder; Future; Goals; Investigation; Knowledge; Maps; Memory; Modeling; Monitor; Motor; Movement; Multiregional Analyses; Mus; Neurons; Outcome; Performance; Population; Population Dynamics; Property; Psyche structure; Reading; Recovery; Recurrence; Rest; Running; Schizophrenia; Sensory; Short-Term Memory; Side; Signal Transduction; Silicon; Structure; Techniques; Technology; Time; Training; Work; base; behavioral response; brain size; cognitive function; combinatorial; data modeling; driving behavior; dynamic system; experience; experimental study; flexibility; follow-up; frontal lobe; improved; interest; neural circuit; relating to nervous system; response; sensory discrimination; stem; theories

Abstract Short-term memory (STM) is a core cognitive function, critical for reasoning, decision-making, and flexible behavior. Though it has been recognized as a key function of interest for many decades, the critical neural substrate of STM is little understood. Recording experiments have found distributed persistent activity related to STM across multiple brain areas, yet it is unclear what the critical circuit nodes are, whether STM is supported by a single distributed circuit or involves many distinct parallel representations at the same time, and what if any are the causal relations between STM related activity in different brain areas. This gap in knowledge stems from lacks of a holistic view of the neural dynamics giving rise to STM. Comprehensive studies that examine multiple brain regions within a single STM behavior are severely lacking. Moreover, recordings alone cannot assess how signals across multiple brain regions are related to each other and to behavior. Transient perturbation is a powerful approach to probe recurrent neural networks. The response of neural dynamics following a transient perturbation are related to the network structure. The objective of this proposal is to use region-specific and temporally-precise perturbations in multiple brain regions to probe critical circuit nodes for STM. This will demonstrate the potential of our approach for identifying multi-regional circuits driving behavior and generate hypotheses regarding their network structure. We focus on a frontal cortical region (ALM) that is critical for a STM behavior we developed. Mice make a sensory discrimination, followed by a delay epoch in which they maintain a STM of their upcoming motor response. ALM neurons exhibit persistent activity that are causally related to the upcoming movement. STM neural dynamics are funneled through ALM projections to the brainstem to trigger a behavioral response. These properties make ALM an ideal entry point to read out the dynamics arising from the interactions of different circuit nodes and relate the dynamics to behavior. We will first obtain a comprehensive dataset where we transiently perturb all of the candidate brain regions for STM (identified using recordings and anatomy) while monitoring their consequences on STM dynamics in ALM with population recordings using the latest silicon probe technology (Aim1). We will then perform theoretical analysis of the perturbed dynamics leading to model classes that relate specific dynamics to behavior and serve as hypotheses for the underlying circuit structure (Aim2). If successful, this approach will identify the critical anatomical substrate and the relevant STM neural dynamics in a single behavior. The model outcome will subsequently guide a truly targeted yet comprehensive investigation of the multi-regional STM circuit.

抽象的 短期记忆 (STM) 是一种核心认知功能，对于推理、决策和灵活性至关重要 行为。尽管几十年来它一直被认为是一个令人感兴趣的关键功能，但关键的神经网络 STM 的底物知之甚少。记录实验发现分布式持续活动相关 STM 跨越多个大脑区域，但尚不清楚关键的电路节点是什么，STM 是否是 由单个分布式电路支持或同时涉及许多不同的并行表示，并且 不同大脑区域的 STM 相关活动之间是否存在因果关系？这个差距在 知识源于缺乏对引起 STM 的神经动力学的整体看法。综合的 严重缺乏在单一 STM 行为中检查多个大脑区域的研究。而且， 仅靠录音无法评估多个大脑区域的信号如何相互关联以及 行为。瞬态扰动是探测循环神经网络的有效方法。的回应 瞬态扰动后的神经动力学与网络结构有关。此举的目的 提议是在多个大脑区域中使用特定区域和时间精确的扰动来探测关键的 STM 的电路节点。这将证明我们的方法识别多区域电路的潜力 驱动行为并生成有关其网络结构的假设。我们关注额叶皮质 对于我们开发的 STM 行为至关重要的区域（ALM）。小鼠进行感官辨别，然后 一个延迟时期，他们在其中维持即将到来的运动反应的 STM。 ALM神经元表现出持续性 与即将到来的运动有因果关系的活动。 STM 神经动力学通过 ALM 汇集 投射到脑干以触发行为反应。这些特性使 ALM 成为理想的切入点 读出不同电路节点相互作用产生的动态，并将动态与 行为。我们将首先获得一个全面的数据集，在其中我们短暂地扰动所有候选大脑 STM 区域（使用录音和解剖学识别），同时监测其对 STM 的影响 使用最新的硅探针技术 (Aim1) 进行群体记录的 ALM 动态。我们随后将 对扰动动力学进行理论分析，生成与特定动力学相关的模型类 行为并作为底层电路结构的假设（目标2）。如果成功的话，这种方法将 识别单一行为中的关键解剖基质和相关 STM 神经动力学。型号 结果将随后指导对多区域 STM 进行真正有针对性且全面的调查 电路。