| Title: | Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns |
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| Authors: | ID Maslennikov, Oleg (Author)
ID Perc, Matjaž (Author)
ID Nekorkin, Vladimir (Author) |
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| Files: |  RAZ_Maslennikov_Oleg_2024.pdf (6,96 MB)
MD5: 17D68A14C066E811ED561C5195963CC2
 https://doi.org/10.3389/fncom.2024.1363514
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FNM - Faculty of Natural Sciences and Mathematics
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| Abstract: | 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. |
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| Keywords: | topological features, neural networks, spatiotemporal patterns, nonlinear dynamics |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 30.12.2023 |
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| Article acceptance date: | 06.02.2024 |
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| Publication date: | 23.02.2024 |
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| Publisher: | Frontiers Media S.A. |
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| Year of publishing: | 2024 |
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| Number of pages: | 13 str. |
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| Numbering: | Letn. 18, št. članka 1363514 |
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| PID: | 20.500.12556/DKUM-91196  |
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| UDC: | 53 |
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| ISSN on article: | 1662-5188 |
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| COBISS.SI-ID: | 187350019 |
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| DOI: | 10.3389/fncom.2024.1363514 |
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| Publication date in DKUM: | 27.11.2024 |
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| Views: | 135 |
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| Downloads: | 15 |
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| Metadata: |    |
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| Categories: | Misc.
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