Spiking neural P systems with structural plasticity

Cabarle, Francis George C.; Adorna, Henry N.; Pérez–Jiménez, Mario J.; Song, Tao

doi:10.1007/s00521-015-1857-4

Cited by 105 publications

(27 citation statements)

References 21 publications

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“…It is known from [2] that SNPSP systems as generators or acceptors are universal, operating in the ''usual way'' in membrane computing, i.e. neurons operate in parallel.…”

Section: Resultsmentioning

confidence: 99%

Sequential spiking neural P systems with structural plasticity based on max/min spike number

Cabarle

Adorna

Pérez–Jiménez

2015

Neural Comput & Applic

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Spiking neural P systems (in short, SNP systems) are parallel, distributed, and nondeterministic computing devices inspired by biological spiking neurons. Recently, a class of SNP systems known as SNP systems with structural plasticity (in short, SNPSP systems) was introduced. SNPSP systems represent a class of SNP systems that have dynamism applied to the synapses, i.e. neurons can use plasticity rules to create or remove synapses. In this work, we impose the restriction of sequentiality on SNPSP systems, using four modes: max, min, max-pseudo-, and min-pseudo-sequentiality. We also impose a normal form for SNPSP systems as number acceptors and generators. Conditions for (non)universality are then provided. Specifically, acceptors are universal in all modes, while generators need a nondeterminism source in two modes, which in this work is provided by the plasticity rules.

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“…It is known from [2] that SNPSP systems as generators or acceptors are universal, operating in the ''usual way'' in membrane computing, i.e. neurons operate in parallel.…”

Section: Resultsmentioning

confidence: 99%

Sequential spiking neural P systems with structural plasticity based on max/min spike number

Cabarle

Adorna

Pérez–Jiménez

2015

Neural Comput & Applic

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“…Spiking neural P systems with structural plasticity (or SNPSP systems) introduced in [2] are variants of classic spiking neural P systems (or SNP systems) from [8].…”

Section: Spiking Neural P Systems With Structural Plasticitymentioning

confidence: 99%

Generating context-free languages using spiking neural P systems with structural plasticity

2019

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Spiking neural P system (SNP system) is a model of computation inspired by networks of spiking neurons. An SNP system is a network of neurons that can send an object, known as a spike, to each other. Spiking neural P system with structural plasticity (SNPSP system) is a variant of the classical SNP system. SNPSP system that incorporates the ideas of synaptogenesis (creating new synapses) and synaptic pruning (deletion of existing synapses), collectively known as structural plasticity, as features of the model. This gives SNPSP systems the ability to change their own structure/topology. In this work, we use SNPSP systems to generate context-free languages. We create a procedure for constructing an SNPSP system given a context-free grammar in Greibach normal form (GNF). The resulting SNPSP system essentially simulates the way in which a context-free grammar in GNF is used to generate languages. We use modules known as arithmetic-memory modules, also created using SNPSP systems, to perform arithmetic operations which are needed for the simulation.

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“…In an SNPS, a neuron and a synapse represent a node and a connection among nodes(neurons), respectively. The SNPS has attracted the attention of many scholars since it was proposed, and many different variants have been proposed, such as adding the learning functions [9,10], considering the plasticity or timeliness of synapse [11][12][13][14][15][16], introducing neuron division and budding functions [17] and other models [18][19][20][21]. More introductions on membrane computing and SNPS can be found in [22,23].…”

Section: Introductionmentioning

confidence: 99%

Reliability evaluation of distribution network based on fuzzy spiking neural P system with self-synapse

Huang

Wang

et al. 2020

J Membr Comput

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This paper proposes a fuzzy spiking neural P system with self-synapse (in short, FSNPSS) which is applied to the reliability assessment of distribution networks. The method maps the operation or fault states of the distribution network component and the load to the excited or resting states of neurons, and converts electrical relationships among components, loads and targeted systems into a synaptic connection relationship. Then, the occurrence probabilities of the states are transmitted by spikes, and reliability indices are computed by accumulating pulse values of the spikes. Finally, the feasibility and effectiveness of solving reliability assessment of distribution networks by membrane systems are verified in case studies.

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Spiking neural P systems with structural plasticity

Cited by 105 publications

References 21 publications

Sequential spiking neural P systems with structural plasticity based on max/min spike number

Sequential spiking neural P systems with structural plasticity based on max/min spike number

Generating context-free languages using spiking neural P systems with structural plasticity

Reliability evaluation of distribution network based on fuzzy spiking neural P system with self-synapse

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