Details
| Original language | English |
|---|---|
| Title of host publication | Information Processing and Management of Uncertainty in Knowledge-Based Systems |
| Subtitle of host publication | 18th International Conference, IPMU 2020, Proceedings |
| Editors | Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager |
| Place of Publication | Cham |
| Pages | 59-69 |
| Number of pages | 11 |
| Volume | 1 |
| ISBN (electronic) | 9783030501464 |
| Publication status | Published - 2020 |
| Event | 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems - Lisbon, Portugal, Lissabon, Portugal Duration: 15 Jun 2020 → 19 Jun 2020 Conference number: 18 https://ipmu2020.inesc-id.pt/ |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Volume | 1237 |
| ISSN (Print) | 1865-0929 |
| ISSN (electronic) | 1865-0937 |
Abstract
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.
Keywords
- Scale-invariance, Shift-invariance, Spiking neural networks
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Mathematics(all)
- General Mathematics
Cite this
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Information Processing and Management of Uncertainty in Knowledge-Based Systems: 18th International Conference, IPMU 2020, Proceedings. ed. / Marie-Jeanne Lesot; Susana Vieira; Marek Z. Reformat; João Paulo Carvalho; Anna Wilbik; Bernadette Bouchon-Meunier; Ronald R. Yager. Vol. 1 Cham, 2020. p. 59-69 (Communications in Computer and Information Science; Vol. 1237).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Why Spiking Neural Networks Are Efficient
T2 - 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems
AU - Beer, Michael
AU - Urenda, Julio
AU - Kosheleva, Olga
AU - Kreinovich, Vladik
N1 - Conference code: 18
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Scale-invariance
KW - Shift-invariance
KW - Spiking neural networks
UR - http://www.scopus.com/inward/record.url?scp=85086234246&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-50146-4_5
DO - 10.1007/978-3-030-50146-4_5
M3 - Conference contribution
AN - SCOPUS:85086234246
SN - 9783030501457
VL - 1
T3 - Communications in Computer and Information Science
SP - 59
EP - 69
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems
A2 - Lesot, Marie-Jeanne
A2 - Vieira, Susana
A2 - Reformat, Marek Z.
A2 - Carvalho, João Paulo
A2 - Wilbik, Anna
A2 - Bouchon-Meunier, Bernadette
A2 - Yager, Ronald R.
CY - Cham
Y2 - 15 June 2020 through 19 June 2020
ER -