Self-Adaptation Spiking Neural Membrane Systems with Neuromodulators

Spiking neural P systems (SN PS) exemplify the third-generation of spiking neural networks (SNNs), which perform distributed and concurrent computations. However, SN PS rely solely on the spike as the singular signaling entity, ignoring the influence of other substances in the biological nervous system. Neuromodulators have the ability to exert their effects on the postsynaptic membrane, influencing synaptic plasticity and modifying the intensity of interneuronal connections. Motivated by this biological observation, we introduce a self-adaptation spiking neural P system with neuromodulators (SSNN PS). Specifically, neuromodulators generated by neurons are designed as resources consumed by rules in the postsynaptic membrane. The postsynaptic membrane, functioning as a new computational unit with three novel rules, possesses self-adapting weights regulated by neuromodulators and reflects the intensity of connections between neurons. Therefore, the SSNN PS enhance the control of the system over the computing process. In this work, we demonstrate the Turing universality of SSNN PS as both a number-generating and a number-accepting device. In addition, to verify the application capability, an SSNN PS for gender recognition of face images was constructed. The postsynaptic membrane self-adaptively updates its weights as it receives the feedback neuromodulators, which makes the recognition result more accurate. It achieves an accuracy of 91.71% on the UTKFace dataset and 87.83% on the FairFace dataset, and outperforms the other five comparative methods.