Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control

DOI10.1016/j.ins.2016.08.055zbMath1428.68155OpenAlexW2509386333WikidataQ114951094 ScholiaQ114951094MaRDI QIDQ2282261

M. L. Dennis Wong, Tao Song, Pan Zheng, Xun Wang

Publication date: 7 January 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2016.08.055

zbMATH Keywords

Boolean circuitsspiking neural P systemsbio-inspired computinglogic gatesastrocytes-like control

Mathematics Subject Classification ID

Modes of computation (nondeterministic, parallel, interactive, probabilistic, etc.) (68Q10) Switching theory, applications of Boolean algebras to circuits and networks (94C11) Biologically inspired models of computation (DNA computing, membrane computing, etc.) (68Q07)

Related Items (12)

Spiking neural P systems with a flat maximally parallel use of rules ⋮ Asynchronous homogenous spiking neural P systems with local rule synchronization ⋮ Small universal spiking neural P systems with dendritic/axonal delays and dendritic trunk/feedback ⋮ Turing universality of sequential spiking neural P systems with polarizations as number accepting devices ⋮ Numerical spiking neural P systems with weights ⋮ Asynchronous numerical spiking neural P systems ⋮ Novel systematic mathematical computation based on the spiking frequency gate (SFG): innovative organization of spiking computer ⋮ Spiking neural P systems with polarizations and rules on synapses ⋮ Numerical spiking neural P systems with production functions on synapses ⋮ On solutions and representations of spiking neural P systems with rules on synapses ⋮ An improved universal spiking neural P system with generalized use of rules ⋮ Tissue-like P systems with evolutional symport/antiport rules

Cites Work

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• Cell-like spiking neural P systems
• Double-fold localized multiple matrixized learning machine
• Evolutionary sampling: a novel way of machine learning within a probabilistic framework
• Asynchronous spiking neural P systems
• Multi-document summarization via group sparse learning
• Efficient parallel Boolean matrix based algorithms for computing composite rough set approximations
• Membrane computing. An introduction.
• Sequential SNP systems based on min/max spike number
• Machine learning models, epistemic set-valued data and generalized loss functions: an encompassing approach
• Neuro-adaptive fault-tolerant control of high speed trains under traction-braking failures using self-structuring neural networks
• Spiking neural P systems with rules on synapses
• Fuzzy reasoning spiking neural P system for fault diagnosis
• Asynchronous spiking neural P systems with local synchronization
• Adaptive fuzzy spiking neural P systems for fuzzy inference and learning
• Spiking Neural P Systems with Astrocytes
• Time-Free Spiking Neural P Systems
• ON STRING LANGUAGES GENERATED BY SPIKING NEURAL P SYSTEMS WITH ANTI-SPIKES
• On Some Classes of Sequential Spiking Neural P Systems
• Membrane Computing
• Membrane Computing

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