A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting

Sai Deepesh Pokala, Marie Bernert, Takuya Nanami, Takashi Kohno, Timothée Lévi and Blaise Yvert ()
Additional contact information
Sai Deepesh Pokala: Grenoble Institut Neurosciences
Marie Bernert: Grenoble Institut Neurosciences
Takuya Nanami: Meguro
Takashi Kohno: Meguro
Timothée Lévi: University of Bordeaux
Blaise Yvert: Grenoble Institut Neurosciences

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Advanced large-scale neural interfaces call for efficient algorithms to automatically process and optimally exploit the richness of their heavy continuous flow of data. In this context, we introduce here a very frugal generic single-layer Spiking Neural Network (SNN) for fully unsupervised identification and classification of multivariate temporal patterns in continuous data streams. This approach is first validated on simulated multivariate data, Mel Cepstral representations of speech sounds, and multichannel multiunit neural recordings. Then, this very simple SNN was found to be effective at classifying action potentials in a fully unsupervised and online-compatible mode on simulated and real spike sorting datasets. These results pave the way for highly frugal SNN architectures for automatic unsupervised real-time pattern recognition in high-dimensional neural data recordings, which could be suitable for future embedding into ultra-low power hardware platforms, such as active neural implants.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-64231-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64231-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-64231-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().