20.500.12556/DKUM-91196 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 topology methods to the matrices obtained by rank-ordering the entries of the distance matrices, specifically calculating the persistence barcodes and Betti curves. By comparing the features of dierent types of output patterns, we uncover the complex relations between low-dimensional target signals and the underlying multidimensional spike trains. topological features neural networks spatiotemporal patterns nonlinear dynamics topološke značilnosti nevronske mreže prostorsko-časovni vzorci nelinearna dinamika true false true Frontiers Media S.A. Angleški jezik Slovenski jezik Znanstveno delo 2024-11-27 12:30:40 2024-11-27 12:30:40 2025-10-08 14:29:52 0000-00-00 00:00:00 2024 0 0 13 str. št. članka 1363514 Letn. 18 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-12-30 2024-02-06 2024-02-23 53 187350019 10.3389/fncom.2024.1363514 1662-5188 RAZ_Maslennikov_Oleg_2024.pdf RAZ_Maslennikov_Oleg_2024.pdf 1 17D68A14C066E811ED561C5195963CC2 c94409fead458ad13cc519cb3aa15cdb09c030185f98aba78e92d5974d974373 3d53d5ef-acb4-11ef-82e2-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=186965 https://doi.org/10.3389/fncom.2024.1363514 0 fc04e622-acb4-11ef-82e2-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=186968 Fakulteta za naravoslovje in matematiko 0 0 0