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.

翻译：本文探讨了离散神经网络（如脉冲网络）与钢琴曲结构之间引人入胜的相似性。两者均涉及按顺序或并行激活的节点或音符，而后者得益于丰富的音乐理论体系来引导有意义的组合。我们提出一种新颖方法，利用音乐语法调控脉冲神经网络中的激活，使符号能够以吸引子的形式表示。通过应用音乐理论中的和弦进行规则，我们展示了某些激活如何自然地跟随其他激活，类似于吸引的概念。此外，我们引入调性转换的概念，以在网络中导航不同的吸引域。最终，我们证明模型中的概念映射由音乐五度循环所构建，这凸显了在深度学习算法中利用音乐理论原理的潜力。