Emergence of the consonance pattern within synaptic weights of a neural network featuring Hebbian neuroplasticity

Citation data:

Biologically Inspired Cognitive Architectures, ISSN: 2212-683X, Vol: 22, Page: 82-94

Publication Year:
2017
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DOI:
10.1016/j.bica.2017.09.001
Author(s):
Toso Pankovski, Eva Pankovska
Publisher(s):
Elsevier BV
Tags:
Psychology, Neuroscience, Computer Science
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article description
Consonance is a perception phenomenon that evokes pleasant feelings when listening to complex sounds. Since Pythagoras, people have attempted to explain consonance and dissonance, using various methodological means, with limited success and without providing convincing underlying causes. We demonstrate that a specific auditory spectral distribution caused by nonlinearities, as a first phenomenon, and the Hebbian neuroplasticity as a second, are sufficient set of phenomena a system should possess so it could generate the consonance pattern - the actual two-tone interval list ordered by consonance. The emergence of this pattern is explained in a step-by-step manner, utilizing an artificial neural network model. In a reverse engineering manner, our simulations are testing all the possible spectral distributions of auditory stimuli (within particular precision scales and applying certain abstractions) and reveal those that produce a result with a pattern perfectly matching the consonance ordered two-tone interval list, the one that is widely accepted in the Western musical culture. The results of this study suggest that the consonance pattern could be an expected outcome in any system containing the asserted set of features (including the higher animals). The intent of this study is not to realistically model the human auditory system, but to demonstrate a set of features an abstract and generic system should possess so it could produce the consonance pattern.

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