Artificial Neural Networks Solve Musical Problems With Fourier Phase Spaces.

ABSTRACT: How does the brain represent musical properties? Even with our growing understanding of the cognitive neuroscience of music, the answer to this question remains unclear. One method for conceiving possible representations is to use artificial neural networks, which can provide biologically plausible models of cognition. One could train networks to solve musical problems, and then study how these networks encode musical properties. However, researchers rarely examine network structure in detail because networks are difficult to interpret, and because many assume that networks capture informal or subsymbolic properties. Here we report very high correlations between network connection weights and discrete Fourier phase spaces used to represent musical sets. This is remarkable because there is

SUBMITTER: Dawson MRW 

PROVIDER: S-EPMC7188679 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature