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Spiking neural P systems with structural plasticity

Authors:

Francis George Cabarle,

Henry N. Adorna,

Mario J. Pérez-Jiménez,

Tao SongAuthors Info & Claims

Neural Computing and Applications, Volume 26, Issue 8

Pages 1905 - 1917

https://doi.org/10.1007/s00521-015-1857-4

Published: 01 November 2015 Publication History

Abstract

Spiking neural P (SNP) systems are a class of parallel, distributed, and nondeterministic computing models inspired by the spiking of biological neurons. In this work, the biological feature known as structural plasticity is introduced in the framework of SNP systems. Structural plasticity refers to synapse creation and deletion, thus changing the synapse graph. The "programming" therefore of a brain-like model, the SNP system with structural plasticity (SNPSP system), is based on how neurons connect to each other. SNPSP systems are also a partial answer to an open question on SNP systems with dynamism only for synapses. For both the accepting and generative modes, we prove that SNPSP systems are universal. Modifying SNPSP systems semantics, we introduce the spike saving mode and prove that universality is maintained. In saving mode, however, a deadlock state can arise, and we prove that reaching such a state is undecidable. Lastly, we provide one technique in order to use structural plasticity to solve a hard problem: a constant time, nondeterministic, and semi-uniform solution to the NP-complete problem Subset Sum.

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Cited By

Wu RZhao Y(2024)Spiking neural P systems with structural plasticity and mute rulesTheoretical Computer Science10.1016/j.tcs.2024.1145541000:COnline publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1016/j.tcs.2024.114554
de la Cruz RCabarle FAdorna H(2024)Steps toward a homogenization procedure for spiking neural P systemsTheoretical Computer Science10.1016/j.tcs.2023.114250981:COnline publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1016/j.tcs.2023.114250
Zhang XLiu XRen QSun MZhao Y(2024)A general neural membrane computing modelInformation Sciences: an International Journal10.1016/j.ins.2024.120686672:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120686
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Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 26, Issue 8

November 2015

250 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

Copyright © Copyright © 2015 The Natural Computing Applications Forum.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2015

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Cited By

Wu RZhao Y(2024)Spiking neural P systems with structural plasticity and mute rulesTheoretical Computer Science10.1016/j.tcs.2024.1145541000:COnline publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1016/j.tcs.2024.114554
de la Cruz RCabarle FAdorna H(2024)Steps toward a homogenization procedure for spiking neural P systemsTheoretical Computer Science10.1016/j.tcs.2023.114250981:COnline publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1016/j.tcs.2023.114250
Zhang XLiu XRen QSun MZhao Y(2024)A general neural membrane computing modelInformation Sciences: an International Journal10.1016/j.ins.2024.120686672:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120686
Wu TValencia-Cabrera LPérez-Jiménez MPan L(2024)Spiking neural P systems with mute rulesInformation and Computation10.1016/j.ic.2024.105179299:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.ic.2024.105179
Xue JLi QLiu XGuo YLu JSong BHuang PAn QGong GLi D(2023)Hybrid neural-like P systems with evolutionary channels for multiple brain metastases segmentationPattern Recognition10.1016/j.patcog.2023.109651142:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.patcog.2023.109651
Yang SLi DPeng HZhou WLuo XYang QWang JSong X(2022)Dynamic threshold P systems with delay on synapses for shortest path problemsTheoretical Computer Science10.1016/j.tcs.2022.06.008926:C(62-70)Online publication date: 18-Aug-2022
https://dl.acm.org/doi/10.1016/j.tcs.2022.06.008
Zhang LXu F(2022)Asynchronous homogenous spiking neural P systems with local rule synchronizationTheoretical Computer Science10.1016/j.tcs.2022.05.023926:C(51-61)Online publication date: 18-Aug-2022
https://dl.acm.org/doi/10.1016/j.tcs.2022.05.023
Zhou NPeng HWang JYang QLuo X(2022)Computational completeness of spiking neural P systems with inhibitory rules for generating string languagesTheoretical Computer Science10.1016/j.tcs.2022.02.025920:C(64-75)Online publication date: 12-Jun-2022
https://dl.acm.org/doi/10.1016/j.tcs.2022.02.025
Garcia LVazquez ESanchez GAvalos JSanchez G(2022)An ultra-compact and high-speed FFT-based large-integer multiplier for fully homomorphic encryption using a dual spike-based arithmetic circuit over GF(p)Neurocomputing10.1016/j.neucom.2022.08.020507:C(54-66)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.neucom.2022.08.020
Olvera-Martinez LJimenez-Borgonio TFrias-Carmona TAbarca-Rodriguez MDiaz-Rodriguez CCedillo-Hernandez MNakano-Miyatake MPerez-Meana H(2022)First SN P visual cryptographic circuit with astrocyte control of structural plasticity for security applicationsNeurocomputing10.1016/j.neucom.2021.05.057457:C(67-73)Online publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.neucom.2021.05.057
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