Modeling the structure-function relation in a reconstructed cortical tissue

摘要 神经元之间的连通性与这些神经元在体内表现出的活动模式有什么关系？这 脑回路中的结构-功能关系问题具有根本的重要性。把它放在一个 定量的方法将对我们关于大脑如何工作的理论和 从更好的疾病治疗到人工智能的新工具。然而，我们目前 对结构-功能关系的理解相对较差，这在很大程度上是因为 神经元的连接性在很大程度上仍然是未知的。反过来，这严重限制了将建模与 理论上的努力这个问题在研究高度异质性的情况下尤其具有挑战性。 皮层回路，参与感知、认知和学习等重要功能。 幸运的是，我们在艾伦脑科学研究所的合作者最近的实验工作， 从而产生表征小鼠皮层区V1中的连接性的转换性新数据集， 在细胞类型水平上使用多补丁突触生理学，在单个神经元水平上使用电子 显微镜（EM）。这是神经科学史上第一次，我们将有单个神经元的连接体 结合V1 ~1 mm 3的密集活动记录，加上突触的系统表征， 特性. 我们将利用这些独特的数据集来构建并与社区共享V1的新模型， 他们研究皮层连接和体内活动和计算之间的关系。我们将 分析神经元代码的多种特征如何取决于单个细胞的特性和高阶 连接基序，其存在于EM连接体中，但不存在于基于免疫学的连接体中。 从细胞类型水平的稀疏测量或从现有文献推断。我们还将评估 V1的新模型与当前结构-功能关系理论的预测一致。 这些模型和模拟将免费与社区共享，作为科学家 用于指导未来的实验设计，提高模型中的生物真实性，并协助生成和 测试理论通过提供一个丰富的和生物现实的框架，为新的理论，建模， 实验研究，这一资源将推动新的发现之间的关系的结构和 皮层回路的功能

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.