Topological ensemble detection with differentiable yoking

David Klindt, Sigurd Gaukstad, Melvin Vaupel, Erik Hermansen, Benjamin Dunn
Proceedings of the 1st NeurIPS Workshop on Symmetry and Geometry in Neural Representations, PMLR 197:354-369, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v197-klindt23a, title = {Topological ensemble detection with differentiable yoking}, author = {Klindt, David and Gaukstad, Sigurd and Vaupel, Melvin and Hermansen, Erik and Dunn, Benjamin}, booktitle = {Proceedings of the 1st NeurIPS Workshop on Symmetry and Geometry in Neural Representations}, pages = {354--369}, year = {2023}, editor = {Sanborn, Sophia and Shewmake, Christian and Azeglio, Simone and Di Bernardo, Arianna and Miolane, Nina}, volume = {197}, series = {Proceedings of Machine Learning Research}, month = {03 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v197/klindt23a/klindt23a.pdf}, url = {https://proceedings.mlr.press/v197/klindt23a.html}, 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.} }
Endnote
%0 Conference Paper %T Topological ensemble detection with differentiable yoking %A David Klindt %A Sigurd Gaukstad %A Melvin Vaupel %A Erik Hermansen %A Benjamin Dunn %B Proceedings of the 1st NeurIPS Workshop on Symmetry and Geometry in Neural Representations %C Proceedings of Machine Learning Research %D 2023 %E Sophia Sanborn %E Christian Shewmake %E Simone Azeglio %E Arianna Di Bernardo %E Nina Miolane %F pmlr-v197-klindt23a %I PMLR %P 354--369 %U https://proceedings.mlr.press/v197/klindt23a.html %V 197 %X 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.
APA
Klindt, D., Gaukstad, S., Vaupel, M., Hermansen, E. & Dunn, B.. (2023). Topological ensemble detection with differentiable yoking. Proceedings of the 1st NeurIPS Workshop on Symmetry and Geometry in Neural Representations, in Proceedings of Machine Learning Research 197:354-369 Available from https://proceedings.mlr.press/v197/klindt23a.html.

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