Modeling the structure-function relation in a reconstructed cortical tissue

对重建皮质组织中的结构-功能关系进行建模

• 批准号: 10005712
• 负责人: ANTON ARKHIPOV
• 金额: $ 134.98万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-14 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10005712
• 关键词: Artificial Intelligence; Biological; Brain; Case Study; Cells; Code; Cognition; Communities; Complement; Coupled; Data; Data Set; Databases; Dependence; Disease; Electron Microscopy; Exhibits; Future; Individual; Institutes; Learning; Literature; Measurement; Modeling; Morphology; Mus; Neurons; Neurosciences; Pattern; Perception; Physiology; Property; Recording of previous events; Resources; Science; Scientist; Structural Models; Structure; Structure-Activity Relationship; Synapses; Testing; Theoretical model; Time; Tissues; Visual Cortex; Work; area V1; area striata; base; cell type; connectome; design; experimental study; improved; in vivo; models and simulation; network models; reconstruction; relating to nervous system; simulation; statistics; theories; tool

Abstract How is connectivity between neurons related to patterns of activity exhibited by these neurons in vivo? This question of structure-function relations in brain circuits is of fundamental importance. Answering it in a quantitative manner would have far-reaching consequences both for our theories of how brain works and for applications ranging from better disease treatments to new tools for artificial intelligence. However, our current understanding of structure-function relations is relatively poor, in large part because the fine structure of neuronal connectivity has remained largely unknown. In turn, this severely limits connecting modeling to theoretical efforts. This problem is particularly challenging in the case of studying the highly heterogeneous cortical circuits, which are involved in important functions like perception, cognition, and learning. Fortunately, recent experimental work by our collaborators at the Allen Institute for Brain Science is now resulting in transformational new datasets that characterize connectivity in the mouse cortical area V1 at the level of Cell Types using multi-patch synaptic physiology and at the level of individual neurons using electron microscopy (EM). For the first time in history of neuroscience, we will have connectome of individual neurons coupled with dense recordings of activity in ~1 mm3 of V1, plus systematic characterization of synaptic properties. We will leverage these unique datasets to build and share with the community new models of V1 and use them to study the relationships between cortical connectivity and in vivo activity and computations. We will analyze how multiple features of neuronal code depend on individual cell properties and on higher-order connectivity motifs, which are present in the EM connectome, but not in the statistics-based connectivity inferred from sparse measurements at the Cell Types level or from existing literature. We also will evaluate the consistency of the new models of V1 with predictions made by current theories of structure-function relations. These models and simulations will be freely shared with the community as a resource that scientists will use to guide future experiment designs, improve biological realism in models, and assist in generating and testing theories. By providing a rich and biologically realistic framework for new theoretical, modeling, and experimental studies, this resource will fuel new discoveries regarding relations between the structure and function of cortical circuits.

摘要