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Spiking Neural P Systems with Rules on Synapses Working in Sum Spikes Consumption Strategy

Su Y., Wu T., Xu F., Păun A.

Fundamenta Informaticae

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2017

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Vol. 156, nr 2

187--208

EN

Spiking neural P systems with rules on synapses (RSSN P systems, for short) are a class of distributed and parallel computation models inspired by the way in which neurons process and communicate information with each other by means of spikes, where neurons only contain spikes and the evolution rules are on synapses. RSSN P systems have been proved to be Turing universal, using the strategy that restricts all the applied rules to consume the same number of spikes from the given neuron, termed as equal spikes consumption strategy. In this work, in order to avoid imposing the equal spikes consumption restriction on the application of rules, a new strategy for rule application, termed as sum spikes consumption strategy, is considered in RSSN P systems, where a maximal set of enabled rules from synapses starting from the same neuron is nondeterministically chosen to be applied, in the sense that no further synapse can use any of its rules, and the sum of these numbers of spikes that all the applied rules consume is removed from the neuron. In this way, the proposed strategy avoids checking whether all the applied rules consume the same number of spikes from the given neuron. The computation power of RSSN P systems working in the proposed strategy is investigated, and it is proved that such systems characterize the semilinear sets of natural numbers, i.e., such systems are not universal. Furthermore, RSSN P systems with weighted synapses working in the proposed strategy are proved to be Turing universal. These results show that the weight on synapses is a powerful ingredient of RSSN P systems in terms of the computation power, which makes RSSN P systems working in sum spikes consumption strategy become universal from non-universality.

2

Reversible Spiking Neural P Systems with Astrocytes

Kong Y., Shi X., Xu J., Huang X.

Fundamenta Informaticae

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2015

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Vol. 136, nr 3

219--230

EN

Spiking neural P systems with astrocytes (SNPA systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, we investigate the reversibility in SNPA systems as well as the computational power of reversible SNPA systems. It is proved that reversible SNPA systems are universal, where the forgetting rules and the feature of delay in spiking rules are not used, and each neuron contains only one spiking rule. The result suggests that the astrocytes play a key role in the functioning of reversible SNPA systems.

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On String Languages Generated by Spiking Neural P Systems with Astrocytes

Kong Y., Zhang Z., Liu Y.

Fundamenta Informaticae

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2015

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Vol. 136, nr 3

231--240

EN

Spiking neural P systems with astrocytes (SNPA systems, for short) are a class of distributed parallel computing devices inspired from the way spikes pass along the synapses between neurons. In this work, we investigate the computational power of SNPA systems as language generators. Specifically, representations of recursively enumerable languages and of regular languages are given by means of SNPA systems without forgetting rules. Furthermore, a simple finite language is produced which can be generated by SNPA systems, while it cannot be generated by usual spiking neural P systems. These results show that the astrocytes are a powerful ingredient for spiking neural P systems as language generators.

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Extending Simulation of Asynchronous Spiking Neural P Systems in P–Lingua

Macías-Ramos L. F., Pérez-Jiménez M. J., Song T., Pan L.

Fundamenta Informaticae

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2015

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Vol. 136, nr 3

253--267

EN

Spiking neural P systems (SN P systems for short) are a class of neural-like computing models in the framework of membrane computing. Inspired by the neurophysiological structure of the brain, SN P systems have been extended in various ways. P–Lingua is a standard language for the definition of P systems, where pLinguaCore library provides particular implementations of parsers and simulators for the models specified in P–Lingua. A support for simulating SN P systems in P–Lingua was introduced recently and soon expanded to cover further features of these systems. In this paper, we present an extension of P–Lingua related to asynchronous SN P systems, in order to incorporate simulation capabilities for limited asynchronous SN P systems and asynchronous SN P systems with local synchronization.