Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns

In this study, we focus on training recurrent spiking neural networks to generate spatiotemporal patterns in the form of closed two-dimensional trajectories. Spike trains in the trained networks are examined in terms of their dissimilarity using the Victor–Purpura distance. We apply algebraic topolo...

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Ngā kaituhi matua:	Oleg Maslennikov, Matjaž Perc, Vladimir Nekorkin
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Frontiers Media S.A. 2024-02-01
Rangatū:	Frontiers in Computational Neuroscience
Ngā marau:	spiking neural network target spatiotemporal pattern supervised learning reservoir computing spike metrics persistent homology
Urunga tuihono:	https://www.frontiersin.org/articles/10.3389/fncom.2024.1363514/full
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns

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