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- W2945748086 abstract "Learning often involves adapting behavior in response to the inferred causes of success and failure. At the neural level, this can be the result of repeating activity patterns of neurons that lead to favorable outcomes. However, it is not clear how the contributions of individual cells to an ongoing behavior is assessed. Using a calcium imaging based closed loop Brain-Machine Interface (CaBMI), we trained mice to perform a neuroprosthetic task using the coordinated activity of a small ensemble of neurons in layer 2/3 of sensorimotor cortex. We find that that after an initial period of exploration, neurons that do not directly drive the effector decrease in both variance and event frequency over the course of learning. However, a large fraction of these ‘indirect’ cells demonstrate robust spatiotemporal dynamics both before and after an animal achieves reward. Throughout a single 30 minute session, these spatiotemporal sequences increase in frequency and become more consistent. Our findings suggest that neuroprosthetic control is the result of an emergent, spatially organized network level solution, rather than the direct modulation of a few chosen output neurons." @default.
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- W2945748086 date "2019-03-01" @default.
- W2945748086 modified "2023-09-27" @default.
- W2945748086 title "Local network coordination supports neuroprosthetic control" @default.
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- W2945748086 doi "https://doi.org/10.1109/ner.2019.8717107" @default.
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