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（如脉冲神经网络）与钢琴曲结构之间有趣的相似性。尽管两者均涉及节点或音符的序列或并行激活，但后者得益于丰富的音乐理论体系，可指导有意义的组合。我们提出了一种新颖方法，利用音乐语法来调节脉冲神经网络中的激活过程，从而将符号表示为吸引子。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遵循其他激活，类似于吸引子概念。此外，我们引入了调式转换的概念，以在神经网络的不同吸引域中导航。最终，我们证明了模型中的概念映射由音乐五度圈结构化，凸显了在深度学习算法中借鉴音乐理论原理的潜力。