Musical mode is a fundamental element of tonal music, structuring pitch organization and shaping tonal relationships. Existing artificial intelligence approaches to symbolic music generation often rely on rigid alignment strategies and simplified tonal representations, limiting their ability to capture the diversity of musical modes, in contrast to the complex perceptual and learning mechanisms observed in human listeners. In this paper, we propose a brain-inspired spiking neural network that integrates biologically grounded mechanisms with symbolic music theory to represent and learn musical modes and keys. The model comprises multiple interacting subsystems inspired by the functional organization of relevant brain regions, and incorporates neural circuit evolution and spike-timing-dependent plasticity to support mode- and key-conditioned music learning and generation. Experimental results show that the synaptic connectivity patterns emerging in the proposed network exhibit strong alignment with the Krumhansl-Schmuckler key profiles, a well-established model of tonal perception in music psychology. Additionally, quantitative evaluations show that the generated musical pieces preserve tonal characteristics while maintaining melodic diversity. By integrating insights from neuroscience, music psychology, and music theory within a spiking neural network framework, this work provides an interpretable and biologically inspired approach to symbolic music learning and generation.
Liang et al. (Tue,) studied this question.
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