Computing with Spiking Neural P Systems: Traces and Small Universal Systems

Ionescu, Mihai; Păun, Andrei; Păun, Gheorghe; Pérez–Jiménez, Mario J.

doi:10.1007/11925903_1

Cited by 37 publications

(69 citation statements)

References 6 publications

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“…A survey of results and the biological motivations for these systems can be found in [5] and [2]. In the meantime, two main research directions were particularly active in this area of membrane computing: looking for classes of systems with tractable (for instance, decidable) properties, and looking for the possibility of using SN P systems for efficiently solving computationally hard problems.…”

Section: Spiking Neural P Systems -An Informal Presentationmentioning

confidence: 99%

Asynchronous Spiking Neural P Systems: Decidability and Undecidability

et al. 2008

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Abstract. In search for "realistic" bio-inspired computing models, we consider asynchronous spiking neural P systems, in the hope to get a class of computing devices with decidable properties. However, although the non-synchronization is known in general to decrease the computing power, in the case of using extended rules (several spikes can be produced by a rule) we obtain again the equivalence with Turing machines (interpreted as generators of sets of vectors of numbers). The problem remains open for the case of restricted spiking neural P systems, whose rules can only produce one spike. On the other hand, we prove that asynchronous spiking neural P systems, with a specific way of halting, using extended rules and where each neuron is either bounded or unbounded, are equivalent to partially blind counter machines and, therefore, have many decidable properties. Spiking Neural P Systems -An Informal PresentationIn the present paper we continue the investigation of spiking neural P systems (SN P systems, in short). A survey of results and the biological motivations for these systems can be found in [5] and [2]. In the meantime, two main research directions were particularly active in this area of membrane computing: looking for classes of systems with tractable (for instance, decidable) properties, and looking for the possibility of using SN P systems for efficiently solving computationally hard problems. Along the second research line are the investigations related to the possibility of simulating an SN P system by a Turing machine with a polynomial slowdown (preliminary results can be found in [3]) and those trying to improve the efficiency of SN P systems, e.g., by enhancing the parallelism of the system (see, for instance, [7]).In this paper we report several recent results concerning the first topic mentioned above -specifically, removing the synchronization (common in many membrane computing models), calling them asynchronous SN P systems. These

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Section: Spiking Neural P Systems -An Informal Presentationmentioning

confidence: 99%

Asynchronous Spiking Neural P Systems: Decidability and Undecidability

et al. 2008

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“…As stated in the Introduction, SN P systems have been introduced in [6], in the framework of membrane computing. They can be considered as an evolution of P systems, corresponding to a shift from cell-like to neural-like architectures.…”

Section: Sn P Systems With Neuron Division and Buddingmentioning

confidence: 99%

“…The structure of SN P systems (that is, the synapse graph) introduced in [6] is static. For both biological and mathematical motivations discussed in the Introduction, neuron division and budding are introduced into SN P systems.…”

Section: Sn P Systems With Neuron Division and Buddingmentioning

confidence: 99%

“…Spiking neural P systems (in short, SN P systems) were introduced in [6] in the framework of membrane computing [13] as a new class of computing devices which are inspired by the neurophysiological behavior of neurons sending electrical impulses (spikes) along axons to other neurons. Since then, many computational properties of SN P systems have been studied; for example, it has been proved that they are Turing-complete when considered as number computing devices [6], when used as language generators [3,1] and also when computing functions [12].…”

Section: Introductionmentioning

confidence: 99%

“…Since then, many computational properties of SN P systems have been studied; for example, it has been proved that they are Turing-complete when considered as number computing devices [6], when used as language generators [3,1] and also when computing functions [12].…”

Section: Introductionmentioning

confidence: 99%

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Spiking neural P systems with neuron division and budding

Pan

Păun

Pérez–Jiménez

2011

Sci. China Inf. Sci.

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162

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Summary. In order to enhance the efficiency of spiking neural P systems, we introduce the features of neuron division and neuron budding, which are processes inspired by neural stem cell division. As expected (as it is the case for P systems with active membranes), in this way we get the possibility to solve computationally hard problems in polynomial time. We illustrate this possibility with SAT problem.

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A Look at the Descriptional Complexity of SNQ P Systems

Păun

Bîlbîe

2018

Enjoying Natural Computing

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Computing with Spiking Neural P Systems: Traces and Small Universal Systems

Cited by 37 publications

References 6 publications

Asynchronous Spiking Neural P Systems: Decidability and Undecidability

Asynchronous Spiking Neural P Systems: Decidability and Undecidability

Spiking neural P systems with neuron division and budding

A Look at the Descriptional Complexity of SNQ P Systems

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