Neuroevolution of Spiking Neural P Systems

Membrane computing is a discipline that aims to perform computation by mimicking nature at the cellular level. Spiking Neural P (in short, SN P) systems are a subset of membrane computing methodologies that combine spiking neurons with membrane computing techniques, where “P” means that the system is intrinsically parallel. While these methodologies are very powerful, being able to simulate a Turing machine with only few neurons, their design is time-consuming and it can only be handled by experts in the field, that have an in-depth knowledge of such systems. In this work, we use the Neuroevolution of Augmenting Topologies (NEAT) algorithm, usually employed to evolve multi-layer perceptrons and recurrent neural networks, to evolve SN P systems. Unlike existing approaches for the automatic design of SN P systems, NEAT provides high flexibility in the type of SN P systems, removing the need to specify a great part of the system. To test the proposed method, we evolve Spiking Neural P systems as policies for two classic control tasks from OpenAI Gym. The experimental results show that our method is able to generate efficient (yet extremely simple) Spiking Neural P systems that can solve the two tasks. A further analysis shows that the evolved systems act on the environment by performing a kind of “if-then-else” reasoning.