Efficient Connectome Analyses for Identifying Influential Neurons in Behaviorally Relevant Pathways

Ishani Ganguly, Columbia University

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Current efforts to analyze large-scale connectome datasets extracted from EM data have largely focused on identifying global properties of neural connectivity such as common motifs and degree distribution. However, scalable methods to efficiently solve specific problems relevant to circuit neuroscience such as isolating behaviorally relevant neurons in circuit pathways have not been fully explored. Here, we adapt existing frameworks such as pathway weight computation and exploit powerful computing resources to rank influential neurons in all pathways between two subsets of neurons. We deploy these methods on the Drosophila hemibrain connectome to recover several well-studied neural pathways from sensory input to descending motor output and compare our results with existing methods for tracing pathways. Applying these methods to less-understood brain circuits could elucidate which neurons are best suited for experimental manipulation and shed light on organizational principles for neural circuits.

Abstract Author(s): Ishani Ganguly, Rudy Behnia, Ashok Litwin-Kumar