Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patternsMaslennikov, Oleg (Avtor) Perc, Matjaž (Avtor) Nekorkin, Vladimir (Avtor) topological featuresneural networksspatiotemporal patternsnonlinear dynamicsIn 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.Frontiers Media S.A.20242024-11-27 12:30:40Znanstveno delo91196UDK: 53COBISS_ID: 187350019DOI: 10.3389/fncom.2024.1363514ISSN pri članku: 1662-5188sl