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) |
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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. |
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ISSN: | 1054-1500 1089-7682 |
DOI: | 10.1063/5.0210790 |