Asynchronous Spiking Neural P Systems with Structural Plasticity

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

doi:10.1007/978-3-319-21819-9_9

Cited by 14 publications

(3 citation statements)

References 11 publications

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“…We also note that at present, the matrix representation seems to be applicable to asynchronous version of SNPSP systems, see e.g. [3]. In asynchronous mode of rule application, as opposed to the synchronous mode in this work, at each step a neuron can nondeterministically choose not to apply a rule even if a rule can be applied.…”

Section: Closing Remarksmentioning

confidence: 98%

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Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity

et al. 2019

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In this paper, we create a matrix representation for spiking neural P systems with structural plasticity (SNPSP, for short), taking inspiration from existing algorithms and representations for related variants. Using our matrix representation, we provide a simulation algorithm for SNPSP systems. We prove that the algorithm correctly simulates an SNPSP system: our representation and algorithm are able to capture the syntax and semantics of SNPSP systems, e.g. plasticity rules, dynamism in the synapse set. Analyses of the time and space complexity of our algorithm show that its implementation can benefit using parallel computers. Our representation and simulation algorithm can be useful when implementing SNPSP systems and related variants with a dynamic topology, in software or hardware.

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Section: Closing Remarksmentioning

confidence: 98%

“…[4], we have ex computing the set {1, 4, 7, 10, …} = {3m + 1|m ≥ 0} . In Table 1, the output of ex is t 2 − t 1 = 1 if neuron 1 creates synapse (1,3), where (!) means that the output neuron 3 fires a spike to the environment, and t 2 and t 1 are the second and first time 3 fires, respectively.…”

Section: Preliminariesmentioning

confidence: 99%

Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity

et al. 2019

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“…SN P systems and their variants mentioned above can work in synchronous mode, where a global clock is assumed to synchronize all neurons; thus, all neurons work in parallel. Some works have made an effort to discuss SN P systems working in asynchronous mode and investigate their computational completeness [41][42][43]. From the biological perspective, the assumption that a global clock is used to synchronize all neurons is rather non-realistic for a biological nervous system.…”

Section: Introductionmentioning

confidence: 99%

Sequential dynamic threshold neural P systems

Bao

Zhou

et al. 2020

J Membr Comput

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Dynamic threshold neural P systems (DTNP systems, for short) are a kind of distributed parallel computing systems abstracted from the spiking and dynamic threshold mechanisms of neurons. A DTNP system consists of several dynamic threshold neurons, and each neuron has a data unit and a threshold unit. The computational completeness of DTNP systems has been investigated. DTNP systems are synchronous systems, and a global clock is assumed to synchronize all threshold neurons. However, the assumption is biologically non-realistic. In this paper, we discuss DTNP systems working in sequential mode, i.e., sequential DTNP systems (SDTNP systems, in short). Based on the number of spikes of active neurons and the ruleapplication strategy, four sequentiality strategies are considered. It is proven that SDTP systems working in four sequentiality strategies are Turing universal number generating/accepting devices.

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