Spiking Neural P Systems

TLDR

Time of neuron firing is a key factor in spiking neural P systems. The paper introduces spiking neural P systems and outlines future research directions. Computation results are encoded as the time interval between spikes of a designated neuron. SN P systems are computationally complete, but with bounded spikes they characterize semilinear sets.

Abstract

This paper proposes a way to incorporate the idea of spiking neurons into the area of membrane computing, and to this aim we introduce a class of neural-like P systems which we call spiking neural P systems (in short, SN P systems). In these devices, the time (when the neurons fire and/or spike) plays an essential role. For instance, the result of a computation is the time between the moments when a specified neuron spikes. Seen as number computing devices, SN P systems are shown to be computationally complete (both in the generating and accepting modes, in the latter case also when restricting to deterministic systems). If the number of spikes present in the system is bounded, then the power of SN P systems falls drastically, and we get a characterization of semilinear sets. A series of research topics and open problems are formulated.