Klindt, D, Gaukstad, S, Vaupel, M, Hermansen, E, Dunn, B (January 2023) Topological Ensemble Detection with Differentiable Yoking. In: Proceedings of Machine Learning Research.
Abstract
Modern neural recordings comprise thousands of neurons recorded at millisecond precision. An important step in analyzing these recordings is to identify neural ensembles — subsets of neurons that represent a subsystem of specific functionality. A famous example in the mammalian brain is that of the grid cells, which separate into ensembles of different spatial resolution. Recent work demonstrated that recordings from individual ensembles exhibit the topological signature of a torus. This is obscured, however, in combined recordings from multiple ensembles. Inspired by this observation, we introduce a topological ensemble detection algorithm that is capable of unsupervised identification of neural ensembles based on their topological signatures. This identification is achieved by optimizing a loss function that captures the assumed topological signature of the ensemble and opens up exciting possibilities, e.g., searching for cell ensembles in prefrontal cortex, which may represent cognitive maps on more conceptual spaces than grid cells.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| CSHL Authors: | |
| Communities: | CSHL labs > Klindt lab |
| SWORD Depositor: | CSHL Elements |
| Depositing User: | CSHL Elements |
| Date: | 1 January 2023 |
| Date Deposited: | 11 Apr 2024 15:57 |
| Last Modified: | 11 Apr 2024 15:57 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/41504 |
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