Spiking Neural P Systems with Weights

Wang, Jun; Hoogeboom, Hendrik Jan; Pan, Linqiang; Păun, Gheorghe; Pérez–Jiménez, Mario J.

doi:10.1162/neco_a_00022

Cited by 141 publications

(62 citation statements)

References 13 publications

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“…The ingredient of weighted synapses has already been introduced in SNP systems literature, and we refer the reader to [16] (and references therein) for computing and biological motivations. In particular, if σ i applies a rule E/a c → a p , and the weighted synapse (i, j, r) exists (i.e.…”

Section: Resultsmentioning

confidence: 99%

“…Many neuroscience inspirations have also been included for computing use, producing several variants (to which the previous investigation lines are also applied), e.g. use of weighted synapses [16], neuron division and budding [9], the use of astrocytes [10]. Furthermore, many restrictions have been applied to SNP systems (and variants), e.g.…”

Section: Introductionmentioning

confidence: 99%

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Asynchronous Spiking Neural P Systems with Structural Plasticity

Cabarle

Adorna

Pérez–Jiménez

2015

Unconventional Computation and Natural Computation

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Summary. Spiking neural P (in short, SNP) systems are computing devices inspired by biological spiking neurons. In this work we consider SNP systems with structural plasticity (in short, SNPSP systems) working in the asynchronous (in short, asyn mode). SNPSP systems represent a class of SNP systems that have dynamic synapses, i.e. neurons can use plasticity rules to create or remove synapses. We prove that for asyn mode, bounded SNPSP systems (where any neuron produces at most one spike each step) are not universal, while unbounded SNPSP systems with weighted synapses (a weight associated with each synapse allows a neuron to produce more than one spike each step) are universal. The latter systems are similar to SNP systems with extended rules in asyn mode (known to be universal) while the former are similar to SNP systems with standard rules only in asyn mode (conjectured not to be universal). Our results thus provide support to the conjecture of the still open problem.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Asynchronous Spiking Neural P Systems with Structural Plasticity

Cabarle

Adorna

Pérez–Jiménez

2015

Unconventional Computation and Natural Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the previous obtained SN P systems in [3,5,6,7,8,12,16,20], a global clock is generally assumed marking the time for the system. The systems work in a synchronized manner in the global level (all neurons apply their rules in a parallel manner), and for each neuron, only one of the enabled rules can be used on the tick of the clock.…”

Section: Introductionmentioning

confidence: 99%

Solving Subset Sum Problems by Time-free Spiking Neural P Systems

Song¹,

Luo²,

He³

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:In membrane computing, spiking neural P systems (shortly called SN P systems) are a group of neural-like computing models inspired from the way spiking neurons communication in form of spikes. In previous works, SN P systems working in a nondeterministic manner have been used to solve numerical NP-complete problems, such as SAT, vertex cover, in feasible time. In these works, the application of any rule should complete in exactly one time unit, and the precise execution time of rules plays a crucial role on solving the problems in polynomial (or even in linear) time. However, the restriction does not coincide with the biological fact, since in biological systems, bio-chemical reactions may cost different execution time due to the external uncontrollable conditions. In this paper, we consider timed and time-free SN P systems, where the precise execution time of the rules is removed. To investigate the computational efficiency of time-free SN P systems, we solve Subset Sum problem by a family of uniform time-free SN P systems.

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“…Many variants of all the three classes of P systems, as well as their computational properties have been considered [1,3,4,5,6,7,8,15,16,17,18,19]; an overview of membrane computing or P systems can be found in [10] and [12], with up-to-date information available at the membrane computing website (http://ppage.psystems.eu).…”

Section: Introductionmentioning

confidence: 99%

Solving Vertex Cover Problem by Tissue P Systems with Cell Division

Song¹,

Zheng²,

He³

2014

Appl. Math. Inf. Sci.

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Abstract:Tissue P systems are a class of distributed and parallel computing models investigated in membrane computing, which are inspired from the structure and functioning of communication cells in tissues. Such systems with cell division (corresponding to the mitosis behavior of living cells) can theoretically generate exponential working space in linear time, therefore providing a possible way to solve computational hard problems in feasible time by a space-time trade-off. In this work, we construct a family of tissue P systems with cell division to solve the vertex cover problems, and achieve a linear time solution (with respect to the size of the problems). Furthermore, we prove that the systems are constructed in a uniform manner and work in a confluent way.

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Spiking Neural P Systems with Weights

Cited by 141 publications

References 13 publications

Asynchronous Spiking Neural P Systems with Structural Plasticity

Asynchronous Spiking Neural P Systems with Structural Plasticity

Solving Subset Sum Problems by Time-free Spiking Neural P Systems

Solving Vertex Cover Problem by Tissue P Systems with Cell Division

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