From diverse dynamics to diverse computation via neural cell types

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Project Summary A prominent feature of biological neuronal networks is the astonishing diversity of their cell types. Major, nationally coordinated experimental efforts, including the BRAIN Initiative’s Cell Census Network (BICCN) and the Allen Institute Cell Types programs, are currently revealing this cellular diversity at new and very high levels of resolution. For example, just across two areas of mouse cortex 133 cell types have been characterized, with many types shared across areas! Similar cell classes have been observed across species. These types show marked differences not only gene expression and connectivity, but also membrane, spiking, and synaptic dynamics. This is in sharp contrast to most computational and theoretical models of learning in neural networks, which generally make use of only one or a small number of cell types. The goal of this project is to produce new computational and theoretical tools to help close this gap. This will enable us, and the broader community, to test a hypothesis for the functional role of cell-type specific, heterogeneous cellular and synaptic dynamics: that they can be harnessed to generate complex network dynamics which allows faster or more accurate learning of tasks which themselves have inputs or objectives which have complex dynamics. Such tasks abound in natural environments. Testing this hypothesis requires new high-throughput computational tools to train neural networks with biologically realistic dynamics and connectivity to solve tasks, new theoretical tools to understand how diverse cellular dynamics contribute to network computation, and new application to large-scale, cellular data-driven models. First, with the expertise of a grant-supported scientific software engineer, will build, test, and disseminate a software package that flexibly implements heterogeneous dynamics of single cells and short-term synaptic dynamics. We plan to use a very popular, freely available and open source software framework for machine learning (Pytorch). Next, we will establish metrics of network dynamics that help to mechanistically explain what does -- and does not -- matter about cellular and synaptic heterogeneity in impacting learning performance. Finally, we will integrate these tools with a prior cell-type specific computational model of the mouse primary visual cortex, based on large scale Allen Institute databases, to test the hypothesis stated above. Specifically, we will newly determine whether experimentally observed levels of heterogeneity in cellular and synaptic dynamics contribute to the ability of visual microcircuits to perform visual computation.