Classification of musical intervals by spiking neural networks: Perfect student in solfége classes

We investigate a spike activity of a network of excitable FitzHugh–Nagumo neurons, which is under constant two-frequency auditory signals. The neurons are supplemented with linear frequency filters and nonlinear input signal converters. We show that it is possible to configure the network to recogni...

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Veröffentlicht in:Chaos (Woodbury, N.Y.) N.Y.), 2024-06, Vol.34 (6)
Hauptverfasser: Bukh, A. V., Rybalova, E. V., Shepelev, I. A., Vadivasova, T. E.
Format: Artikel
Sprache:eng
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Zusammenfassung:We investigate a spike activity of a network of excitable FitzHugh–Nagumo neurons, which is under constant two-frequency auditory signals. The neurons are supplemented with linear frequency filters and nonlinear input signal converters. We show that it is possible to configure the network to recognize a specific frequency ratio (musical interval) by selecting the parameters of the neurons, input filters, and coupling between neurons. A set of appropriately configured subnetworks with different topologies and coupling strengths can serve as a classifier for musical intervals. We have found that the selective properties of the classifier are due to the presence of a specific topology of coupling between the neurons of the network.
ISSN:1054-1500
1089-7682
DOI:10.1063/5.0210790