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Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities
Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs have the potential to be more energy efficient tha...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053242/ https://www.ncbi.nlm.nih.gov/pubmed/36991750 http://dx.doi.org/10.3390/s23063037 |
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author | Pietrzak, Paweł Szczęsny, Szymon Huderek, Damian Przyborowski, Łukasz |
author_facet | Pietrzak, Paweł Szczęsny, Szymon Huderek, Damian Przyborowski, Łukasz |
author_sort | Pietrzak, Paweł |
collection | PubMed |
description | Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs have the potential to be more energy efficient than ANNs on event-driven neuromorphic hardware. This can yield drastic maintenance cost reduction for neural network models, as the energy consumption would be much lower in comparison to regular deep learning models hosted in the cloud today. However, such hardware is still not yet widely available. On standard computer architectures consisting mainly of central processing units (CPUs) and graphics processing units (GPUs) ANNs, due to simpler models of neurons and simpler models of connections between neurons, have the upper hand in terms of execution speed. In general, they also win in terms of learning algorithms, as SNNs do not reach the same levels of performance as their second-generation counterparts in typical machine learning benchmark tasks, such as classification. In this paper, we review existing learning algorithms for spiking neural networks, divide them into categories by type, and assess their computational complexity. |
format | Online Article Text |
id | pubmed-10053242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100532422023-03-30 Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities Pietrzak, Paweł Szczęsny, Szymon Huderek, Damian Przyborowski, Łukasz Sensors (Basel) Review Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs have the potential to be more energy efficient than ANNs on event-driven neuromorphic hardware. This can yield drastic maintenance cost reduction for neural network models, as the energy consumption would be much lower in comparison to regular deep learning models hosted in the cloud today. However, such hardware is still not yet widely available. On standard computer architectures consisting mainly of central processing units (CPUs) and graphics processing units (GPUs) ANNs, due to simpler models of neurons and simpler models of connections between neurons, have the upper hand in terms of execution speed. In general, they also win in terms of learning algorithms, as SNNs do not reach the same levels of performance as their second-generation counterparts in typical machine learning benchmark tasks, such as classification. In this paper, we review existing learning algorithms for spiking neural networks, divide them into categories by type, and assess their computational complexity. MDPI 2023-03-11 /pmc/articles/PMC10053242/ /pubmed/36991750 http://dx.doi.org/10.3390/s23063037 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Pietrzak, Paweł Szczęsny, Szymon Huderek, Damian Przyborowski, Łukasz Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities |
title | Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities |
title_full | Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities |
title_fullStr | Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities |
title_full_unstemmed | Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities |
title_short | Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities |
title_sort | overview of spiking neural network learning approaches and their computational complexities |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053242/ https://www.ncbi.nlm.nih.gov/pubmed/36991750 http://dx.doi.org/10.3390/s23063037 |
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