  In this paper, we explore the intriguing similarities between the structure
of a discrete neural network, such as a spiking network, and the composition of
a piano piece. While both involve nodes or notes that are activated
sequentially or in parallel, the latter benefits from the rich body of music
theory to guide meaningful combinations. We propose a novel approach that
leverages musical grammar to regulate activations in a spiking neural network,
allowing for the representation of symbols as attractors. By applying rules for
chord progressions from music theory, we demonstrate how certain activations
naturally follow others, akin to the concept of attraction. Furthermore, we
introduce the concept of modulating keys to navigate different basins of
attraction within the network. Ultimately, we show that the map of concepts in
our model is structured by the musical circle of fifths, highlighting the
potential for leveraging music theory principles in deep learning algorithms.
