A Hybrid Learning Algorithm Fusing STDP with GA based Explicit Delay Learning for Spiking Neurons

SP Johnston; G Prasad; Liam Maguire; TM McGinnity

doi:10.1109/IS.2006.348493

A Hybrid Learning Algorithm Fusing STDP with GA based Explicit Delay Learning for Spiking Neurons

SP Johnston, G Prasad, Liam Maguire, TM McGinnity

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

6 Citations (Scopus)

Abstract

This paper presents a hybrid learning algorithmfor spiking neural networks (SNNs), referred to as an evolvablespiking neural network (ESNN) paradigm. The algorithmintegrates a supervised and unsupervised learning approach.The unsupervised approach exploits a Spike Timing DependentPlasticity (STDP) mechanism with explicit delay learningfor multiple connections between neurons. Supervision of thesynaptic delays and the excitatory/inhibitory connections isgoverned by a genetic algorithm (GA), while the STDP rule isfree to operate in its normal unsupervised manner. A spike trainencoding/decoding scheme is developed for the algorithm. Theapproach is validated by application to the Iris classificationproblem.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	632-637
Number of pages	6
DOIs	https://doi.org/10.1109/IS.2006.348493
Publication status	Published (in print/issue) - Sept 2006
Event	3rd IEEE International conference on Intelligent Systems, Westminster, London, September 06 - Duration: 1 Sept 2006 → …

Conference

Conference	3rd IEEE International conference on Intelligent Systems, Westminster, London, September 06
Period	1/09/06 → …

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10.1109/IS.2006.348493

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@inproceedings{4c120b047ab04de6909f992f6c709f60,

title = "A Hybrid Learning Algorithm Fusing STDP with GA based Explicit Delay Learning for Spiking Neurons",

abstract = "This paper presents a hybrid learning algorithmfor spiking neural networks (SNNs), referred to as an evolvablespiking neural network (ESNN) paradigm. The algorithmintegrates a supervised and unsupervised learning approach.The unsupervised approach exploits a Spike Timing DependentPlasticity (STDP) mechanism with explicit delay learningfor multiple connections between neurons. Supervision of thesynaptic delays and the excitatory/inhibitory connections isgoverned by a genetic algorithm (GA), while the STDP rule isfree to operate in its normal unsupervised manner. A spike trainencoding/decoding scheme is developed for the algorithm. Theapproach is validated by application to the Iris classificationproblem.",

author = "SP Johnston and G Prasad and Liam Maguire and TM McGinnity",

year = "2006",

month = sep,

doi = "10.1109/IS.2006.348493",

language = "English",

pages = "632--637",

booktitle = "Unknown Host Publication",

publisher = "IEEE",

address = "United States",

note = "3rd IEEE International conference on Intelligent Systems, Westminster, London, September 06 ; Conference date: 01-09-2006",

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T1 - A Hybrid Learning Algorithm Fusing STDP with GA based Explicit Delay Learning for Spiking Neurons

AU - Johnston, SP

AU - Prasad, G

AU - Maguire, Liam

AU - McGinnity, TM

PY - 2006/9

Y1 - 2006/9

N2 - This paper presents a hybrid learning algorithmfor spiking neural networks (SNNs), referred to as an evolvablespiking neural network (ESNN) paradigm. The algorithmintegrates a supervised and unsupervised learning approach.The unsupervised approach exploits a Spike Timing DependentPlasticity (STDP) mechanism with explicit delay learningfor multiple connections between neurons. Supervision of thesynaptic delays and the excitatory/inhibitory connections isgoverned by a genetic algorithm (GA), while the STDP rule isfree to operate in its normal unsupervised manner. A spike trainencoding/decoding scheme is developed for the algorithm. Theapproach is validated by application to the Iris classificationproblem.

AB - This paper presents a hybrid learning algorithmfor spiking neural networks (SNNs), referred to as an evolvablespiking neural network (ESNN) paradigm. The algorithmintegrates a supervised and unsupervised learning approach.The unsupervised approach exploits a Spike Timing DependentPlasticity (STDP) mechanism with explicit delay learningfor multiple connections between neurons. Supervision of thesynaptic delays and the excitatory/inhibitory connections isgoverned by a genetic algorithm (GA), while the STDP rule isfree to operate in its normal unsupervised manner. A spike trainencoding/decoding scheme is developed for the algorithm. Theapproach is validated by application to the Iris classificationproblem.

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U2 - 10.1109/IS.2006.348493

DO - 10.1109/IS.2006.348493

M3 - Conference contribution

SP - 632

EP - 637

BT - Unknown Host Publication

PB - IEEE

T2 - 3rd IEEE International conference on Intelligent Systems, Westminster, London, September 06

Y2 - 1 September 2006

ER -

A Hybrid Learning Algorithm Fusing STDP with GA based Explicit Delay Learning for Spiking Neurons

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