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• W2891486278 abstract "We introduce a novel data-driven approach to discover and decode features in the neural code coming from large population neural recordings with minimal assumptions, using cohomological feature extraction. We apply our approach to neural recordings of mice moving freely in a box, where we find a circular feature. We then observe that the decoded value corresponds well to the head direction of the mouse. Thus, we capture head direction cells and decode the head direction from the neural population activity without having to process the mouse's behavior. Interestingly, the decoded values convey more information about the neural activity than the tracked head direction does, with differences that have some spatial organization. Finally, we note that the residual population activity, after the head direction has been accounted for, retains some low-dimensional structure that is correlated with the speed of the mouse." @default.
• W2891486278 created "2018-09-27" @default.
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• W2891486278 creator A5014703778 @default.
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• W2891486278 date "2019-01-01" @default.
• W2891486278 modified "2023-09-27" @default.
• W2891486278 title "Decoding of Neural Data Using Cohomological Feature Extraction" @default.
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• W2891486278 doi "https://doi.org/10.1162/neco_a_01150" @default.
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• W2891486278 hasPublicationYear "2019" @default.
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