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Updated: Jun 24, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Classification of musical intervals by spiking neural networks: Perfect student in solfége classes
A V Bukh1, E V Rybalova1, I A Shepelev1,2
1Institute of Physics, Saratov State University, 83 Astrakhanskaya Street, Saratov 410012, Russia.
Abstract:
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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