Spiking Neural P Systems With Rules on Synapses Working in Maximum Spikes Consumption Strategy

Spiking neural P systems (SN P systems, for short) are a class of parallel and distributed computation models inspired from the way the neurons process and communicate information by means of spikes. In this paper, we consider a new variant of SN P systems, where each synapse instead of neuron has a set of spiking rules, and the neurons contain only spikes; when the number of spikes in a given neuron is recognized by a rule on a synapse leaving from it, the rule is enabled; at a computation step, at most one enabled spiking rule is applied on a synapse, and spikes are removed from a neuron if the maximum number of spikes that the applied spiking rules on the synapses starting from this neuron consume is. The computation power of this variant of SN P systems is investigated. Specifically, we prove that such SN P systems can generate or accept any set of Turing computable natural numbers. This result gives an answer to an open problem formulated in Theor. Comput. Sci., vol. 529, pp. 82-95, 2014.