| Autor: |
Zhang Y; Columbia University., He T; Columbia University.; New York University., Boussard J; Columbia University., Windolf C; Columbia University., Winter O; The International Brain Laboratory., Trautmann E; Columbia University., Roth N; University of Washington., Barrell H; University of Washington., Churchland M; Columbia University., Steinmetz NA; University of Washington., Varol E; New York University., Hurwitz C; Columbia University., Paninski L; Columbia University. |
| Jazyk: |
angličtina |
| Zdroj: |
BioRxiv : the preprint server for biology [bioRxiv] 2023 Sep 22. Date of Electronic Publication: 2023 Sep 22. |
| DOI: |
10.1101/2023.09.21.558869 |
| Abstrakt: |
Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting , the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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