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Why Spiking Neural Networks Are Efficient: A Theorem
Current artificial neural networks are very successful in many machine learning applications, but in some cases they still lag behind human abilities. To improve their performance, a natural idea is to simulate features of biological neurons which are not yet implemented in machine learning. One of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274333/ http://dx.doi.org/10.1007/978-3-030-50146-4_5 |
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author | Beer, Michael Urenda, Julio Kosheleva, Olga Kreinovich, Vladik |
author_facet | Beer, Michael Urenda, Julio Kosheleva, Olga Kreinovich, Vladik |
author_sort | Beer, Michael |
collection | PubMed |
description | Current artificial neural networks are very successful in many machine learning applications, but in some cases they still lag behind human abilities. To improve their performance, a natural idea is to simulate features of biological neurons which are not yet implemented in machine learning. One of such features is the fact that in biological neural networks, signals are represented by a train of spikes. Researchers have tried adding this spikiness to machine learning and indeed got very good results, especially when processing time series (and, more generally, spatio-temporal data). In this paper, we provide a possible theoretical explanation for this empirical success. |
format | Online Article Text |
id | pubmed-7274333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72743332020-06-05 Why Spiking Neural Networks Are Efficient: A Theorem Beer, Michael Urenda, Julio Kosheleva, Olga Kreinovich, Vladik Information Processing and Management of Uncertainty in Knowledge-Based Systems Article Current artificial neural networks are very successful in many machine learning applications, but in some cases they still lag behind human abilities. To improve their performance, a natural idea is to simulate features of biological neurons which are not yet implemented in machine learning. One of such features is the fact that in biological neural networks, signals are represented by a train of spikes. Researchers have tried adding this spikiness to machine learning and indeed got very good results, especially when processing time series (and, more generally, spatio-temporal data). In this paper, we provide a possible theoretical explanation for this empirical success. 2020-05-18 /pmc/articles/PMC7274333/ http://dx.doi.org/10.1007/978-3-030-50146-4_5 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Beer, Michael Urenda, Julio Kosheleva, Olga Kreinovich, Vladik Why Spiking Neural Networks Are Efficient: A Theorem |
title | Why Spiking Neural Networks Are Efficient: A Theorem |
title_full | Why Spiking Neural Networks Are Efficient: A Theorem |
title_fullStr | Why Spiking Neural Networks Are Efficient: A Theorem |
title_full_unstemmed | Why Spiking Neural Networks Are Efficient: A Theorem |
title_short | Why Spiking Neural Networks Are Efficient: A Theorem |
title_sort | why spiking neural networks are efficient: a theorem |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274333/ http://dx.doi.org/10.1007/978-3-030-50146-4_5 |
work_keys_str_mv | AT beermichael whyspikingneuralnetworksareefficientatheorem AT urendajulio whyspikingneuralnetworksareefficientatheorem AT koshelevaolga whyspikingneuralnetworksareefficientatheorem AT kreinovichvladik whyspikingneuralnetworksareefficientatheorem |