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.
To improve our understanding of the healthy brain as well as what breaks down in disease, it is essential to develop a quantitative, mechanistic description of how properties of neurons and their connections determine the properties of neural activity in vivo. This project will take advantage of unprecedented experimental datasets from the Allen Institute for Brain Science that contain a systematic characterization of synaptic connections between the cell types present and a connectome of individual neurons coupled with dense recordings of activity in the mouse visual cortex. We propose to integrate these data into new, highly realistic network models of cortical circuits, use the models to explore theoretical predictions, and provide them as a resource to fuel future theoretical, modeling, and experimental work in the community.
