PROJECT SUMMARY Neurons in the visual cortex form an intricate connectivity structure and topographic arrangement. The structural and morphological organization of the neurons is known to constrain its functional properties. To understand these constraints, it is necessary to generate large-scale anatomical and functional measurements of the brain. Ongoing efforts in electron microscopy (EM) and fluorescent microscopy promise to massively accelerate the speed of generating such data. To discover the relationship between structure and function, one approach is to use models of computation in the brain that have both structural and functional components. Convolutional neural networks (CNNs), and more broadly machine learning (ML), have shown an increasing promise in modeling the functional properties of the brain. However, these CNN-based models are often not guided by the real structure of the neurons in the brain. In this proposal, we seek to share and analyze the largest calcium imaging dataset collected to date from an entire column in mouse primary visual cortex (V1). We propose to integrate this dataset with a separate publicly available electron microscopy (EM) dataset of 1mm3 volume in mouse V1 using a novel topologically-plausible CNN-based model of neurons. We will use this new model to understand how the structural organization affords function. Our approach is to bring topological modeling to the study of the computation in neural networks. The calcium imaging dataset includes volumetric 2-photon and 3-photon imaging in four mice. The data contains recordings of visual responses from neurons within an 800um × 800um × ~800um region of the primary visual cortex, spanning all visual layers from pia to white matter. We propose to use this dataset to systematically characterize the physiological properties of spatially-selective and motion-selective neurons in an entire column in mouse V1. We will determine whether and how the organization and size of ON and OFF subfields in spatially-selective neurons reveal differences across visual layers. We will then test the hypothesis that the spatially-selective and motion- selective neurons form distinct networks within V1. We propose to incorporate these two networks into convolutional neural network model of neurons to test whether this model results in higher predictive accuracy and better estimation of neural pattern selectivity. Finally, we propose to characterize the branching motifs in mouse V1 using the EM reconstructions from 1mm3 volume in mouse visual cortex. We will then integrate these motifs with the CNN-based models of neurons to accurately predict calcium imaging responses. We will use this setup to test whether incorporating the branching motifs in a model result in more accurate prediction of neural activity and better estimation of neural pattern selectivity. Our proposal offers a systematic approach to integrate structural and functional datasets to understand how structure affor...