Abstract. Spiking Neural P systems, SNP systems for short, are bio-logically inspired computing devices based on how neurons perform com-putations. SNP systems use only one type of symbol, the spike, in the computations. Information is encoded in the time differences of spikes or the multiplicity of spikes produced at certain times. SNP systems with delays (associated with rules) and those without delays are two of sev-eral Turing complete SNP system variants in literature. In this work we investigate how restricted forms of SNP systems with delays can be sim-ulated by SNP systems without delays. We show the simulations for the following spike routing constructs: sequential, iteration, join, and split
The spiking neural P systems are a class of computing devices recently introduced as a bridge betwe...
Spiking neural P systems (in short, SNP systems) are parallel, distributed, and nondeterministic co...
International audienceWe propose a multi-timescale learning rule for spiking neuron networks, in the...
Due to the inevitable delay phenomenon in the process of signal conversion and transmission, time de...
AbstractSpiking neurons are models for the computational units in biological neural systems where in...
Differentbiologicalprocesses takedifferent times tobe completed,which can also be influenced by many...
AbstractSpiking neural P systems (in short, SN P systems) are computing devices based on the way the...
Spiking neurons are models for the computational units in biological neural systems where informatio...
In the 2010, matrix representation of SN P system without delay was presented while in the case of S...
Spiking neurons are models for the computational units in biological neural systems where informatio...
Spiking neural P systems with scheduled synapses are a class of distributed and parallel computation...
AbstractWe consider the properties of spiking neural P (SNP) systems that work in a sequential manne...
computing devices inspired by the structure and functioning of neural cells. The presence of unrelia...
The modeling of neural systems often involves representing the temporal structure of a dynamic stimu...
International audienceIn the present overview, our wish is to demystify some aspects of coding with ...
The spiking neural P systems are a class of computing devices recently introduced as a bridge betwe...
Spiking neural P systems (in short, SNP systems) are parallel, distributed, and nondeterministic co...
International audienceWe propose a multi-timescale learning rule for spiking neuron networks, in the...
Due to the inevitable delay phenomenon in the process of signal conversion and transmission, time de...
AbstractSpiking neurons are models for the computational units in biological neural systems where in...
Differentbiologicalprocesses takedifferent times tobe completed,which can also be influenced by many...
AbstractSpiking neural P systems (in short, SN P systems) are computing devices based on the way the...
Spiking neurons are models for the computational units in biological neural systems where informatio...
In the 2010, matrix representation of SN P system without delay was presented while in the case of S...
Spiking neurons are models for the computational units in biological neural systems where informatio...
Spiking neural P systems with scheduled synapses are a class of distributed and parallel computation...
AbstractWe consider the properties of spiking neural P (SNP) systems that work in a sequential manne...
computing devices inspired by the structure and functioning of neural cells. The presence of unrelia...
The modeling of neural systems often involves representing the temporal structure of a dynamic stimu...
International audienceIn the present overview, our wish is to demystify some aspects of coding with ...
The spiking neural P systems are a class of computing devices recently introduced as a bridge betwe...
Spiking neural P systems (in short, SNP systems) are parallel, distributed, and nondeterministic co...
International audienceWe propose a multi-timescale learning rule for spiking neuron networks, in the...