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

总结，总体(中间的丘脑：多区域神经的计算 电路) 这一合作项目旨在揭示从皮质下区域到额叶皮质的信号传递的逻辑 通过丘脑。额叶皮质显示出丰富的神经活动模式，可以分解为 与特定行为方面相对应的“活动模式”。例如，持久化活动 与短期记忆和运动规划相关，以及在自愿的过程中快速振荡的活动 动静。在神经计算的动力系统观点中，复杂的行为对应于 不同的大脑皮层活动模式序列。然而，大脑皮层并不产生这些活动模式。 与世隔绝，而是强烈地双向耦合到丘脑--前脑的中央中枢。 丘脑的大部分是非感觉性的(高级)，接受来自小脑、中脑和大脑皮质下的输入。 海马体。我们的中心理论是，这些皮质下信号流经更高级的丘脑，到达 额叶皮质，在那里它们启用活动模式，更新活动模式，并导致在 模式，类似于机器学习中长时间短期记忆网络中的“更新”和“重置”信号。 然而，我们对丘脑的大部分了解来自感觉系统，我们对 皮质下→丘脑→额叶皮质环路是新生的。我们对这个问题还只有一个初步的了解 高阶丘脑的输入和输出回路，丘脑神经元的形态和分子特性， 将皮质下输入与皮质活动联系起来的回路主题，以及这些网络在大脑中的作用 额叶皮质。我们汇集了一支拥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)， 需要短期记忆和决策的行为的小鼠的大规模神经生理学 (项目3、4)和理论与计算(项目5，数据科学核心)。我们将合作，以揭示 信息从皮质下区域流经丘脑，以控制皮质活动模式，从而塑造 行为。单个项目由多区域神经计算的概念框架指导，放置 位于丘脑中间的一个多区域神经网络。我们的工作将对以下方面产生广泛的影响 了解大脑皮质下→丘脑→皮质回路的神经计算，并将产生解剖- 导引的认知功能多区域回路模型。我们还将产生转变范式的社区 资源，包括数量解剖学、新型遗传试剂、神经生理学数据和丰富的模型 框架，未来丘脑回路的研究将建立在这个框架上。

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.