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

总结，总体（中间的丘脑：多区域神经网络中的计算 电路） 这个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 穿过丘脑额叶皮层显示出丰富的神经活动模式，这些模式可以分解为 与行为的特定方面相对应的“活动模式”。示例包括持久活动 与短期记忆和运动规划相关，以及在自愿期间的快速振荡活动 动作在神经计算的动力系统视角中，复杂行为对应于 不同的皮层活动模式序列然而，大脑皮层并不产生这些活动模式， 相反，它与丘脑（前脑的中枢）强烈且双向地耦合。 丘脑的大部分是非感觉的（“高级”），接受来自小脑、中脑和小脑的皮质下输入。 海马体。我们的中心理论是这些皮层下信号流经高级丘脑到达 额叶皮层，在那里他们启用活动模式，更新活动模式，并引起之间的切换 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，我们对丘脑的大部分了解来自感觉系统，我们对丘脑的了解来自感觉系统。 皮层下→丘脑→额叶皮层的回路是新生的。我们仍然只有一个初步的了解， 高级丘脑的输入和输出回路，丘脑神经元的形态和分子特性， 将皮层下输入与皮层活动联系起来的电路基序，以及这些网络在大脑中的参与， 额叶皮层我们汇集了一支在现代高通量解剖学方面具有专业知识的团队（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）和理论和计算（项目5，数据科学核心）。我们将合作揭露 信息从皮层下区域通过丘脑流动，以控制皮层活动模式，从而形成 行为个别项目由多区域神经计算的概念框架指导， 位于多区域神经网络中间的丘脑。我们的工作将对以下方面产生广泛影响： 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- 认知功能的引导多区域电路模型。我们还将创造一个范式转变的社区， 资源，包括定量解剖学，新的遗传试剂，神经生理学数据，以及丰富的建模 框架，丘脑电路的未来研究将建立在此基础上。

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.