University of Hertfordshire

By the same authors

Spiking neural networks evolved to perform multiplicative operations

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Spiking neural networks evolved to perform multiplicative operations. / Khan, Muhammad Aamir; Steuber, Volker; Davey, Neil; Wróbel, Borys.

Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Springer Verlag, 2018. p. 314-321 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11139 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Khan, MA, Steuber, V, Davey, N & Wróbel, B 2018, Spiking neural networks evolved to perform multiplicative operations. in Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11139 LNCS, Springer Verlag, pp. 314-321, 27th International Conference on Artificial Neural Networks, ICANN 2018, Rhodes, Greece, 4/10/18. https://doi.org/10.1007/978-3-030-01418-6_31

APA

Khan, M. A., Steuber, V., Davey, N., & Wróbel, B. (2018). Spiking neural networks evolved to perform multiplicative operations. In Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings (pp. 314-321). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11139 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01418-6_31

Vancouver

Khan MA, Steuber V, Davey N, Wróbel B. Spiking neural networks evolved to perform multiplicative operations. In Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Springer Verlag. 2018. p. 314-321. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-01418-6_31

Author

Khan, Muhammad Aamir ; Steuber, Volker ; Davey, Neil ; Wróbel, Borys. / Spiking neural networks evolved to perform multiplicative operations. Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Springer Verlag, 2018. pp. 314-321 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex

@inproceedings{5fe81af311844345b6ea1d11c0f486fa,
title = "Spiking neural networks evolved to perform multiplicative operations",
abstract = "Multiplicative or divisive changes in tuning curves of individual neurons to one stimulus (“input”) as another stimulus (“modulation”) is applied, called gain modulation, play an important role in perception and decision making. Since the presence of modulatory synaptic stimulation results in a multiplicative operation by proportionally changing the neuronal input-output relationship, such a change affects the sensitivity of the neuron but not its selectivity. Multiplicative gain modulation has commonly been studied at the level of single neurons. Much less is known about arithmetic operations at the network level. In this work we have evolved small networks of spiking neurons in which the output neurons respond to input with non-linear tuning curves that exhibit gain modulation—the best network showed an over 3-fold multiplicative response to modulation. Interestingly, we have also obtained a network with only 2 interneurons showing an over 2-fold response.",
keywords = "Adaptive exponential integrate and fire, Artificial evolution, Gain modulation, Multiplicative operation, Spiking neural network",
author = "Khan, {Muhammad Aamir} and Volker Steuber and Neil Davey and Borys Wr{\'o}bel",
note = "{\textcopyright} Springer Nature Switzerland AG 2018 ; 27th International Conference on Artificial Neural Networks, ICANN 2018 ; Conference date: 04-10-2018 Through 07-10-2018",
year = "2018",
month = sep,
day = "27",
doi = "10.1007/978-3-030-01418-6_31",
language = "English",
isbn = "9783030014179",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "314--321",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings",
address = "Germany",

}

RIS

TY - GEN

T1 - Spiking neural networks evolved to perform multiplicative operations

AU - Khan, Muhammad Aamir

AU - Steuber, Volker

AU - Davey, Neil

AU - Wróbel, Borys

N1 - © Springer Nature Switzerland AG 2018

PY - 2018/9/27

Y1 - 2018/9/27

N2 - Multiplicative or divisive changes in tuning curves of individual neurons to one stimulus (“input”) as another stimulus (“modulation”) is applied, called gain modulation, play an important role in perception and decision making. Since the presence of modulatory synaptic stimulation results in a multiplicative operation by proportionally changing the neuronal input-output relationship, such a change affects the sensitivity of the neuron but not its selectivity. Multiplicative gain modulation has commonly been studied at the level of single neurons. Much less is known about arithmetic operations at the network level. In this work we have evolved small networks of spiking neurons in which the output neurons respond to input with non-linear tuning curves that exhibit gain modulation—the best network showed an over 3-fold multiplicative response to modulation. Interestingly, we have also obtained a network with only 2 interneurons showing an over 2-fold response.

AB - Multiplicative or divisive changes in tuning curves of individual neurons to one stimulus (“input”) as another stimulus (“modulation”) is applied, called gain modulation, play an important role in perception and decision making. Since the presence of modulatory synaptic stimulation results in a multiplicative operation by proportionally changing the neuronal input-output relationship, such a change affects the sensitivity of the neuron but not its selectivity. Multiplicative gain modulation has commonly been studied at the level of single neurons. Much less is known about arithmetic operations at the network level. In this work we have evolved small networks of spiking neurons in which the output neurons respond to input with non-linear tuning curves that exhibit gain modulation—the best network showed an over 3-fold multiplicative response to modulation. Interestingly, we have also obtained a network with only 2 interneurons showing an over 2-fold response.

KW - Adaptive exponential integrate and fire

KW - Artificial evolution

KW - Gain modulation

KW - Multiplicative operation

KW - Spiking neural network

UR - http://www.scopus.com/inward/record.url?scp=85054794252&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-01418-6_31

DO - 10.1007/978-3-030-01418-6_31

M3 - Conference contribution

AN - SCOPUS:85054794252

SN - 9783030014179

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 314

EP - 321

BT - Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings

PB - Springer Verlag

T2 - 27th International Conference on Artificial Neural Networks, ICANN 2018

Y2 - 4 October 2018 through 7 October 2018

ER -