An adaptive spiking neural network with Hebbian learning

Lyle N. Long

doi:10.1109/EAIS.2011.5945923

An adaptive spiking neural network with Hebbian learning

Lyle N. Long

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

This paper will describe a numerical approach to simulating biologically-plausible spiking neural networks. These are time dependent neural networks with realistic models for the neurons (Hodgkin-Huxley). In addition the learning is biologically plausible as well, being a Hebbian approach based on spike timing dependent plasticity (STDP). To make the approach very general and flexible, neurogenesis and synaptogenesis have been implemented, which allows the code to automatically add or remove neurons (or synapses) as required.

Original language	English (US)
Title of host publication	IEEE SSCI 2011
Subtitle of host publication	Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems
Pages	17-23
Number of pages	7
DOIs	https://doi.org/10.1109/EAIS.2011.5945923
State	Published - 2011
Event	Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011 - Paris, France Duration: Apr 11 2011 → Apr 15 2011

Publication series

Name	IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems

Other

Other	Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011
Country/Territory	France
City	Paris
Period	4/11/11 → 4/15/11

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Information Systems
Control and Systems Engineering

Access to Document

10.1109/EAIS.2011.5945923

Cite this

Long, L. N. (2011). An adaptive spiking neural network with Hebbian learning. In IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (pp. 17-23). Article 5945923 (IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems). https://doi.org/10.1109/EAIS.2011.5945923

Long, Lyle N. / An adaptive spiking neural network with Hebbian learning. IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems. 2011. pp. 17-23 (IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems).

@inproceedings{c2fe939c252c42d69537d0bde9e0068f,

title = "An adaptive spiking neural network with Hebbian learning",

abstract = "This paper will describe a numerical approach to simulating biologically-plausible spiking neural networks. These are time dependent neural networks with realistic models for the neurons (Hodgkin-Huxley). In addition the learning is biologically plausible as well, being a Hebbian approach based on spike timing dependent plasticity (STDP). To make the approach very general and flexible, neurogenesis and synaptogenesis have been implemented, which allows the code to automatically add or remove neurons (or synapses) as required.",

author = "Long, {Lyle N.}",

year = "2011",

doi = "10.1109/EAIS.2011.5945923",

language = "English (US)",

isbn = "9781424499793",

series = "IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems",

pages = "17--23",

booktitle = "IEEE SSCI 2011",

note = "Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011 ; Conference date: 11-04-2011 Through 15-04-2011",

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Long, LN 2011, An adaptive spiking neural network with Hebbian learning. in IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems., 5945923, IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems, pp. 17-23, Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011, Paris, France, 4/11/11. https://doi.org/10.1109/EAIS.2011.5945923

An adaptive spiking neural network with Hebbian learning. / Long, Lyle N.
IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems. 2011. p. 17-23 5945923 (IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - This paper will describe a numerical approach to simulating biologically-plausible spiking neural networks. These are time dependent neural networks with realistic models for the neurons (Hodgkin-Huxley). In addition the learning is biologically plausible as well, being a Hebbian approach based on spike timing dependent plasticity (STDP). To make the approach very general and flexible, neurogenesis and synaptogenesis have been implemented, which allows the code to automatically add or remove neurons (or synapses) as required.

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Long LN. An adaptive spiking neural network with Hebbian learning. In IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems. 2011. p. 17-23. 5945923. (IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems). doi: 10.1109/EAIS.2011.5945923

An adaptive spiking neural network with Hebbian learning

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