We continue the investigations concerning the possibility of using spiking neural P systems as a framework for solving computationally hard problems, addressing two problems which were already recently considered in this respect: Subset Sum and SAT: For both of them we provide uniform constructions of standard spiking neural P systems (i.e., not using extended rules or parallel use of rules) which solve these problems in a constant number of steps, working in a non-deterministic way. This improves known results of this type where the construction was non-uniform, and/or was using various ingredients added to the initial definition of spiking neural P systems (the SN P systems as defined initially are called here ‘‘standard’’). Howev...
AbstractThe spiking neural P systems are a class of computing devices recently introduced as a bridg...
Spiking neural P systems are a class of P systems inspired from the way the neurons communicate wit...
Spiking neural P systems are a new candidate in spiking neural network models. By using neuron divis...
Recently we have considered the possibility of using spiking neural P systems for solving computati...
In this paper we study some computational properties of spiking neural P systems. In particular, we...
Spiking neural P systems with structural plasticity (in short, SNPSP systems) are models of computa...
Starting from an extended nondeterministic spiking neural P system that solves the Subset Sum proble...
In membrane computing, spiking neural P systems (shortly called SN P systems) are a group of neural-...
Spiking neural P systems (in short, SNP systems) are parallel, distributed models of computations ba...
Spiking neural P systems were recently introduced in and proved to be Turing complete as number com...
In order to enhance the e±ciency of spiking neural P systems, we introduce the features of neuron d...
A variant of spiking neural P systems with positive or negative weights on synapses is introduced, w...
AbstractSpiking neural P systems (in short, SN P systems) are computing devices based on the way the...
A sequence of papers have been recently published, pointing out various intractable problems which ...
We consider spiking neural P systems with spiking rules allowed to introduce zero, one, or more spi...
AbstractThe spiking neural P systems are a class of computing devices recently introduced as a bridg...
Spiking neural P systems are a class of P systems inspired from the way the neurons communicate wit...
Spiking neural P systems are a new candidate in spiking neural network models. By using neuron divis...
Recently we have considered the possibility of using spiking neural P systems for solving computati...
In this paper we study some computational properties of spiking neural P systems. In particular, we...
Spiking neural P systems with structural plasticity (in short, SNPSP systems) are models of computa...
Starting from an extended nondeterministic spiking neural P system that solves the Subset Sum proble...
In membrane computing, spiking neural P systems (shortly called SN P systems) are a group of neural-...
Spiking neural P systems (in short, SNP systems) are parallel, distributed models of computations ba...
Spiking neural P systems were recently introduced in and proved to be Turing complete as number com...
In order to enhance the e±ciency of spiking neural P systems, we introduce the features of neuron d...
A variant of spiking neural P systems with positive or negative weights on synapses is introduced, w...
AbstractSpiking neural P systems (in short, SN P systems) are computing devices based on the way the...
A sequence of papers have been recently published, pointing out various intractable problems which ...
We consider spiking neural P systems with spiking rules allowed to introduce zero, one, or more spi...
AbstractThe spiking neural P systems are a class of computing devices recently introduced as a bridg...
Spiking neural P systems are a class of P systems inspired from the way the neurons communicate wit...
Spiking neural P systems are a new candidate in spiking neural network models. By using neuron divis...