ArticleComputational intelligence and neuroscience2021
Universal Nonlinear Spiking Neural P Systems with Delays and Weights on Synapses.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Spiking Neural P Systems with Membrane Potentials, Inhibitory Rules, and Anti-Spikes.Entropy (Basel, Switzerland) · 2022Article
- Multiview Clustering of Adaptive Sparse Representation Based on Coupled P Systems.Entropy (Basel, Switzerland) · 2022Article
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3 authors.
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Abstract
The nonlinear spiking neural P systems (NSNP systems) are new types of computation models, in which the state of neurons is represented by real numbers, and nonlinear spiking rules handle the neuron's firing. In this work, in order to improve computing performance, the weights and delays are introduced to the NSNP system, and universal nonlinear spiking neural P systems with delays and weights on synapses (NSNP-DW) are proposed. Weights are treated as multiplicative constants by which the number of spikes is increased when transiting across synapses, and delays take into account the speed at which the synapses between neurons transmit information. As a distributed parallel computing model, the Turing universality of the NSNP-DW system as number generating and accepting devices is proven. 47 and 43 neurons are sufficient for constructing two small universal NSNP-DW systems. The NSNP-DW system solving the Subset Sum problem is also presented in this work.
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