Beyond Markov Chains, Towards Adaptive Memristor Network-based Music Generation
Ella Gale, Oliver Matthews, Ben De Lacy Costello and Andrew Adamatzky

We undertook a study of the use of a memristor network for music generation, making use of the memristor’s memory to go beyond the Markov hypothesis. Seed transition matrices are created and populated using memristor equations, and which are shown to generate musical melodies and change in style over time as a result of feedback into the transition matrix. The spiking properties of simple memristor networks are demonstrated and discussed with reference to applications of music making. The limitations of simulating composing memristor networks in von Neumann hardware is discussed and a hardware solution based on physical memristor properties is presented.

Keywords: Memristor, Markov chain, music generation, neuromorphic computing, memristor networks, computer music