Dissecting modular and redundant organization of cortical circuits

摘要 在许多认知过程中，信息在许多大脑区域以并行方式处理。这是 认为使我们的认知能力高度容忍干扰或神经元损失，因为中断 过程由编码相同信息的其他冗余神经元补偿。然而， 理解神经元的互连网络是如何组织成冗余表示的， 鲁棒性我们最近发现，在短期记忆中，老鼠额叶皮层的持续活动， 对扰动非常鲁棒大脑皮层的两个半球被组织成冗余的模块， 每一个都可以独立地保持持久的活性。当一个受到干扰时，另一个的信号 大脑半球帮助恢复活动。模块化和冗余可能是通用的组织原则， 大脑皮层回路中的信息处理和存储。了解这部小说的神经生物学， 潜在的基本组织原则将对神经操纵的设计产生深远的影响 策略和理解慢性神经变性的行为表现。在拟议的研究中， 我们将建立和验证一个新的框架，结合计算建模，人口记录， 和有针对性的光遗传学扰动，以识别，探测和长期跟踪皮质神经元的模块化组织。 电路.我们的目标是解剖大脑区域内和跨大脑区域的冗余模块化组织，获得更深层次的 了解多个冗余模块如何协调信息以支持健壮的行为，以及如何 这种模块化的组织是通过学习来形成的，以表现出健壮的行为。

Abstract In many cognitive processes, information is processed in a parallel manner across many brain regions. This is thought to make our cognitive abilities highly tolerant to perturbations or neuron-loss because disrupted processes are compensated by other redundant neurons coding the same information. Yet, it remains poorly understood how interconnected networks of neurons are organized into redundant representations to produce robustness. We recently discovered that persistent activity in mouse frontal cortex during short-term memory is remarkably robust to perturbations. The two hemispheres of cortex are organized into redundant modules where each one can independently maintain persistent activity. When one suffers a perturbation, signals from the other hemisphere help restore the activity. Modularity and redundancy may be a general organizing principle of information processing and storage in cortical circuits. Understanding the neurobiology of this novel and potentially fundamental organizing principle will have deep implications for design of neural manipulation strategies and understanding behavioral manifestation of chronic neurodegeneration. In the proposed research, we will establish and validate a novel framework that combines computational modeling, population recording, and targeted optogenetic perturbations to identify, probe, and chronically track modular organization of cortical circuits. Our goal is to dissect redundant modular organizations within and across brain areas, obtain a deeper understanding of how multiple redundant modules coordinate information to support robust behavior, and how such modular organization is shaped by learning to manifests in robust behavior.