A survey on learning models of spiking neural membrane systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F25%3AA2603B1L" target="_blank" >RIV/61988987:17610/25:A2603B1L - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/47813059:19240/25:A0001537

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11047-025-10026-9" target="_blank" >https://link.springer.com/article/10.1007/s11047-025-10026-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11047-025-10026-9" target="_blank" >10.1007/s11047-025-10026-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A survey on learning models of spiking neural membrane systems

Popis výsledku v původním jazyce

Spiking neural P systems (SN P systems) are a mathematical model of neural networks, abstracting the way biological neurons communicate with spikes, developed within the framework of the membrane computing theory. Recently, driven by the boom of learning neural models, SN P systems have become a rapidly emerging research front. Consequently, many different variants of the learning models of SN P system prevail among the new research results. Although large proprietary deep learning models are still based on the continuous neural network paradigm, spiking neurons are attractive because of their low-energy demands. The purpose of this paper is to provide an up-to-date overview of learning paradigms and techniques for SN P systems. After a brief introduction of the structure and function of SN P systems, we summarise recent approaches to learning and adaptation in SN P systems, including Hebbian learning, Widrow-Hoff algorithm, fuzzy approaches, nonlinear SN P systems, gated and long short-term memory inspired SN P systems, convolutional SN P systems, and more.

Název v anglickém jazyce

A survey on learning models of spiking neural membrane systems

Popis výsledku anglicky

Spiking neural P systems (SN P systems) are a mathematical model of neural networks, abstracting the way biological neurons communicate with spikes, developed within the framework of the membrane computing theory. Recently, driven by the boom of learning neural models, SN P systems have become a rapidly emerging research front. Consequently, many different variants of the learning models of SN P system prevail among the new research results. Although large proprietary deep learning models are still based on the continuous neural network paradigm, spiking neurons are attractive because of their low-energy demands. The purpose of this paper is to provide an up-to-date overview of learning paradigms and techniques for SN P systems. After a brief introduction of the structure and function of SN P systems, we summarise recent approaches to learning and adaptation in SN P systems, including Hebbian learning, Widrow-Hoff algorithm, fuzzy approaches, nonlinear SN P systems, gated and long short-term memory inspired SN P systems, convolutional SN P systems, and more.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EH23_025%2F0008724" target="_blank" >EH23_025/0008724: Biografie dezinformace s přívlastkem AI: Rizikový fenomén prizmatem moderních věd o člověku</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Natural Computing

ISSN

1567-7818

e-ISSN

1572-9796

Svazek periodika

—

Číslo periodika v rámci svazku

July 2025

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

665-677

Kód UT WoS článku

001524962100001

EID výsledku v databázi Scopus

2-s2.0-105010109673