CARANDANG , Jym Paul

Spiking neural P systems (in short, SN P systems) are models of computation inspired by biological neurons. CuSNP is a project involving sequential (CPU) and parallel (GPU) simulators for SN P systems. In this work, we report the following results: a P-Lingua le parser is included, for ease of use when performing simulations; extension of the...

Spiking neural P systems (in short, SN P systems) are models of computation inspired by biological neurons. In this work, we report our ongoing e orts to improve simulators for SN P systems. CuSNP is a project involving sequential and parallel simulators, and in this work we include a PLingua le parser. The PLingua le parser is for ease of...

Spiking Neural P system (or SN P system) is a computing model based on the neurons in a living being. It is composed of neurons containing spikes interconnected by synapses. Each neuron contain a set of rules which will determine how the spikes are passed in the system. It is a non-deterministic and parallel system which makes GPU a...

Spiking neural P systems (in short, SN P systems) are parallel models of computations inspired by the spiking ( ring) of biological neurons. In SN P systems, neurons function as spike processors and are placed on nodes of a directed graph. Synapses, the connections between neurons, are represented by arcs or directed endges in the graph. Not...

Spiking Neural P systems (in short, SNP systems) are computing models based on living neurons. SNP systems are non-deterministic and parallel, hence making use of a parallel processor such as a graphics processing unit (in short, GPU) is a natural candidate for simulations. Matrix representations and algorithms were previously developed for...