Thalamus in the middle: computations in multi-regional neural circuits

中间的丘脑：多区域神经回路的计算

• 批准号: 10546504
• 负责人: Adam G Carter
• 金额: $ 431.86万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10546504
• 关键词: Amygdaloid structure; Anatomy; Anterior; Architecture; Area; Basal Ganglia; Behavior; Brain; Brain region; Cells; Censuses; Cerebellum; Cognition; Cognition Disorders; Cognitive; Collaborations; Coma; Communication; Communities; Complex; Computer Models; Coupled; Data; Data Analyses; Data Science Core; Decision Making; Electrophysiology (science); Enhancers; Evolution; Future; Genetic; Hippocampus; Human; Hypersomnias; Image; In Vitro; Individual; Knowledge; Lateral; Learning; Link; Logic; Machine Learning; Maintenance; Maps; Measurement; Measures; Medial; Memory; Methods; Midbrain structure; Modeling; Modernization; Molecular; Morphology; Motor; Motor Cortex; Movement; Mus; Neocortex; Neurons; Neurosciences; Output; Physiology; Prefrontal Cortex; Property; Prosencephalon; Reagent; Resources; Route; Science; Sensory; Shapes; Short-Term Memory; Signal Transduction; Slice; Specificity; Structure; Synapses; Testing; Thalamic Nuclei; Thalamic structure; Transgenic Organisms; Update; Virus; Work; artificial neural network; cell type; cloud based; cognitive function; data sharing; dynamic system; epigenetic profiling; experimental study; flexibility; frontal lobe; in vivo; invention; long short term memory; motor disorder; neocortical; neural; neural circuit; neural model; neural network; neural patterning; neurophysiology; novel; optogenetics; response; sensory system; single-cell RNA sequencing; theories; tool; transcriptomics; virtual; voltage

Summary, Overall (Thalamus in the middle: computations in multi-regional neural circuits) This collaborative project aims to uncover the logic of signal routing from subcortical areas to the frontal cortex through the thalamus. The frontal cortex displays rich patterns of neural activity, which can be decomposed into “activity modes” that correspond to specific aspects of behavior. Examples include the persistent activity correlated with short-term memory and motor planning, and the rapidly oscillating activity during voluntary movements. In this dynamical systems perspective of neural computation, complex behaviors correspond to distinct sequences of cortical activity modes. However, the cortex does not generate these activity modes in isolation, but instead is strongly and bidirectionally coupled to the thalamus, the central hub of the forebrain. Most of thalamus is non- sensory (‘higher-order’), receiving subcortical input from the cerebellum, midbrain, and hippocampus. Our central theory is that these subcortical signals flow through higher-order thalamus to reach the frontal cortex, where they enable activity modes, update activity modes, and cause switching between modes, akin to the ‘update’ and ‘reset’ signals in Long Short-Term Memory networks in machine learning. However, most of what we know about thalamus comes from sensory systems, and our knowledge of subcortex→ thalamus→ frontal cortex circuits is nascent. We still have only a rudimentary understanding of the input and output circuits of higher-order thalamus, the morphology and molecular properties of thalamic neurons, the circuit motifs that link subcortical input to cortical activity, and the engagement of these networks across the frontal cortex. We bring together a team with expertise in modern high-throughput anatomy (Project 1, 2), molecular neuroscience (Project 2, Molecular Science Core), cellular and synaptic neurophysiology (Project 3), large-scale neurophysiology in mice performing behaviors that require short-term memory and decision-making (Project 3, 4), and theory and computation (Project 5, Data Science Core). We will collaborate to uncover how information flows from subcortical areas, through thalamus, to control cortical activity modes and thereby shape behavior. Individual projects are guided by a conceptual framework of multi-regional neural computation, placing the thalamus in the middle of a multi-regional neural network. Together, our work will have broad implications for the understanding of neural computation in subcortex→ thalamus→ cortex circuits and will produce anatomy- guided multi-regional circuit models of cognitive function. We will also produce paradigm-shifting community resources, including quantitative anatomy, novel genetic reagents, neurophysiological data, and a rich modeling framework, upon which future studies of thalamic circuits will be built.

总结，总体（丘脑在中间：多区域神经计算 （电路） 该合作项目旨在揭示从皮层下区域到额叶皮层的信号路由逻辑 通过丘脑，额叶皮层显示出丰富的神经活动模式，可以分解为 与行为的特定方面相对应的“活动模式”示例包括持续活动。 与短期记忆和运动计划以及自愿期间的快速振荡活动相关 在神经计算的动态系统视角中，复杂的行为对应于运动。 然而，皮层并不产生这些活动模式。 隔离，而是与前脑的中枢丘脑强烈地双向耦合。 丘脑的大部分是非感觉的（“高阶”），接收来自小脑、中脑和大脑的皮层下输入。 我们的中心理论是这些皮层下信号流经高阶丘脑到达 额叶皮层，它们启用活动模式、更新活动模式并导致之间的切换 模式，类似于机器学习中长短期记忆网络中的“更新”和“重置”信号。 然而，我们对丘脑的了解大部分来自感觉系统，并且我们对丘脑的了解 我们对皮层下→丘脑→额叶皮层的回路还处于初级阶段。 高阶丘脑的输入和输出电路，丘脑神经元的形态和分子特性， 将皮层下输入与皮层活动联系起来的电路图案，以及这些网络在整个大脑中的参与 我们汇集了一支具有现代高通量解剖学专业知识的团队（项目 1、2）， 分子神经科学（项目 2，分子科学核心）、细胞和突触神经生理学（项目 3）、 小鼠执行需要短期记忆和决策的行为的大规模神经生理学 （项目 3、4），以及理论和计算（项目 5，数据科学核心），我们将合作揭示如何实现。 信息从皮层下区域流经丘脑，控制皮层活动模式，从而塑造 各个项目以多区域神经计算的概念框架为指导，放置 我们的工作将共同对多区域神经网络中间的丘脑产生广泛的影响。 对皮层下→丘脑→皮层回路中神经计算的理解，并将产生解剖学- 我们还将产生范式转变的社区。 资源，包括定量解剖学、新型遗传试剂、神经生理学数据和丰富的建模 未来丘脑回路的研究将在此基础上建立。