Track: long paper (up to 10 pages)
Domain: neuroscience
Abstract: To obtain deeper understandings of the brain, aligning similarly functioning neurons, or matching neurons, in different neural systems is becoming an important problem in neuroscience. A major approach for neuronal matching is stimulus- based approach, where matching is performed through similarity of neuronal activity when exerted the same stimulation. This approach, however, is experimentally time-consuming and laborious, and we are in want for a more widely applicable matching approach that possibly uses more accessible data, such as spontaneous neural activity. Here we propose a neuronal matching framework that uses the spontaneous activity. The proposed method is based on an extension of Gromov-Wasserstein optimal transport (GWOT) (Memoli, 2011), which we named Gromov-Wasserstein optimal transport with multiple distance matrices (GWOT-MD). As a test of efficacy of the proposed approach, we applied the proposed framework to calcium imaging time series of spontaneous neuronal activities of \textit{Caenorhabditis elegans} (\textit{C.elegans}). Ratios of matching with pre-identified labels between individual pairs turned out much better than chance level matching ratios. We also performed neuron label identification using the matching results and revealed that the top 5 identification accuracy turned out as good as an identification method using neuronal locations (Sprague et al, 2024).
Submission Number: 64
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