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Abstract

Understanding the functional connectivity between different cortical cell types and the resulting population dynamics is a challenging and important problem. Progress with in-vivo 2-photon population calcium imaging has made it possible to densely sample neural activity in superficial layers of a local patch of cortex. In principle, such data can be used to infer the functional (statistical) connectivity between different classes of cortical neurons by fitting models such as generalized linear models or latent dynamical systems (LDS). However, this approach faces 3 major challenges which we address: 1) only small populations of neurons (~200) can currently be simultaneously imaged at any given time; 2) the cell types of individual neurons are often unknown; and 3) it is unclear how to pool data across different animals to derive an average model. First, while it is not possible to simultaneously image all neurons in a cortical column, it is currently possible to image the activity of ~200 neurons at a time and to repeat this procedure at multiple cortical depths (down to layer 3). We present a computational method ("Stitching LDS") which allows us to "stitch" such non-simultaneously imaged populations of neurons into one large virtual population spanning different depths of cortex. Importantly - and surprisingly - this approach allows us to predict couplings and noise correlations even for pairs of neurons that were never imaged simultaneously. Second, we automatically cluster neurons based on similarities in their functional connectivity (“Clustering LDS”). Under the assumption that such functionally defined clusters can correspond to cell types, this enables us to infer both the cell types and their functional connectivity. Third, while connection profiles of individual cells in one class can be variable, we expect the ‘average’ influence of one cell class on another to be fairly consistent across animals. We show how our approach can be used to pool measurements across different animals in a principled manner (“Pooling LDS”). The result is a highly accurate average model of the interactions between different cell classes. We demonstrate the utility of our computational tools by applying them to model the superficial layers of barrel cortex based on in-vivo 2-photon imaging data in awake mice.