convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data

Roman Koshkin, Tomoki Fukai
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:25302-25312, 2024.

Abstract

Spontaneous neural activity, crucial in memory, learning, and spatial navigation, often manifests itself as repetitive spatiotemporal patterns. Despite their importance, analyzing these patterns in large neural recordings remains challenging due to a lack of efficient and scalable detection methods. Addressing this gap, we introduce convSeq, an unsupervised method that employs backpropagation for optimizing spatiotemporal filters that effectively identify these neural patterns. Our method’s performance is validated on various synthetic data and real neural recordings, revealing spike sequences with unprecedented scalability and efficiency. Significantly surpassing existing methods in speed, convSeq sets a new standard for analyzing spontaneous neural activity, potentially advancing our understanding of information processing in neural circuits.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-koshkin24a, title = {conv{S}eq: Fast and Scalable Method for Detecting Patterns in Spike Data}, author = {Koshkin, Roman and Fukai, Tomoki}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {25302--25312}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/koshkin24a/koshkin24a.pdf}, url = {https://proceedings.mlr.press/v235/koshkin24a.html}, abstract = {Spontaneous neural activity, crucial in memory, learning, and spatial navigation, often manifests itself as repetitive spatiotemporal patterns. Despite their importance, analyzing these patterns in large neural recordings remains challenging due to a lack of efficient and scalable detection methods. Addressing this gap, we introduce convSeq, an unsupervised method that employs backpropagation for optimizing spatiotemporal filters that effectively identify these neural patterns. Our method’s performance is validated on various synthetic data and real neural recordings, revealing spike sequences with unprecedented scalability and efficiency. Significantly surpassing existing methods in speed, convSeq sets a new standard for analyzing spontaneous neural activity, potentially advancing our understanding of information processing in neural circuits.} }
Endnote
%0 Conference Paper %T convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data %A Roman Koshkin %A Tomoki Fukai %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-koshkin24a %I PMLR %P 25302--25312 %U https://proceedings.mlr.press/v235/koshkin24a.html %V 235 %X Spontaneous neural activity, crucial in memory, learning, and spatial navigation, often manifests itself as repetitive spatiotemporal patterns. Despite their importance, analyzing these patterns in large neural recordings remains challenging due to a lack of efficient and scalable detection methods. Addressing this gap, we introduce convSeq, an unsupervised method that employs backpropagation for optimizing spatiotemporal filters that effectively identify these neural patterns. Our method’s performance is validated on various synthetic data and real neural recordings, revealing spike sequences with unprecedented scalability and efficiency. Significantly surpassing existing methods in speed, convSeq sets a new standard for analyzing spontaneous neural activity, potentially advancing our understanding of information processing in neural circuits.
APA
Koshkin, R. & Fukai, T.. (2024). convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:25302-25312 Available from https://proceedings.mlr.press/v235/koshkin24a.html.

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