Online learning in Bayesian Spiking Neurons

Levin Kuhlmann; Michael Hauser-Raspe; Jonathan Manton; David B. Grayden; Jonathan Tapson; André Van Schaik

doi:10.1109/IJCNN.2012.6252370

Online learning in Bayesian Spiking Neurons

Levin Kuhlmann
, Michael Hauser-Raspe
, Jonathan Manton
, David B. Grayden
, Jonathan Tapson
, André Van Schaik

Department of Data Science & AI

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

1 Citation (Scopus)

Abstract

Bayesian Spiking Neurons (BSNs) provide a probabilistic interpretation of how neurons can perform inference and learning. Learning in a single BSN can be formulated as an online maximum-likelihood expectation-maximisation (ML-EM) algorithm. This form of learning is quite slow. Here, an alternative to this learning algorithm, called Fast Learning (FL), is presented. The FL algorithm is shown to have acceptable convergence performance when compared to the ML-EM algorithm. Moreover, for our implementations the FL algorithm is approximately 25 times faster than the ML-EM algorithm. Although only approximate, the FL algorithm therefore makes learning in hierarchical BSN networks much more tractable.

Original language	English
Title of host publication	2012 International Joint Conference on Neural Networks, IJCNN 2012
Publisher	IEEE, Institute of Electrical and Electronics Engineers
ISBN (Print)	9781467314909
DOIs	https://doi.org/10.1109/IJCNN.2012.6252370
Publication status	Published - 22 Aug 2012
Event	IEEE International Joint Conference on Neural Networks 2012 - Brisbane Convention & Exhibition Centre, Brisbane, Australia Duration: 10 Jun 2012 → 15 Jun 2012 https://ieeexplore.ieee.org/xpl/conhome/6241467/proceeding (Proceedings)

Conference

Conference	IEEE International Joint Conference on Neural Networks 2012
Abbreviated title	IJCNN 2012
Country/Territory	Australia
City	Brisbane
Period	10/06/12 → 15/06/12
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6241467/proceeding (Proceedings)

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Cite this

@inproceedings{56884ee9716a49f0b013aa72047a3f86,

title = "Online learning in Bayesian Spiking Neurons",

abstract = "Bayesian Spiking Neurons (BSNs) provide a probabilistic interpretation of how neurons can perform inference and learning. Learning in a single BSN can be formulated as an online maximum-likelihood expectation-maximisation (ML-EM) algorithm. This form of learning is quite slow. Here, an alternative to this learning algorithm, called Fast Learning (FL), is presented. The FL algorithm is shown to have acceptable convergence performance when compared to the ML-EM algorithm. Moreover, for our implementations the FL algorithm is approximately 25 times faster than the ML-EM algorithm. Although only approximate, the FL algorithm therefore makes learning in hierarchical BSN networks much more tractable.",

author = "Levin Kuhlmann and Michael Hauser-Raspe and Jonathan Manton and Grayden, \{David B.\} and Jonathan Tapson and \{Van Schaik\}, Andr{\'e}",

year = "2012",

month = aug,

day = "22",

doi = "10.1109/IJCNN.2012.6252370",

language = "English",

isbn = "9781467314909",

booktitle = "2012 International Joint Conference on Neural Networks, IJCNN 2012",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "IEEE International Joint Conference on Neural Networks 2012, IJCNN 2012 ; Conference date: 10-06-2012 Through 15-06-2012",

url = "https://ieeexplore.ieee.org/xpl/conhome/6241467/proceeding",

}

Kuhlmann, L, Hauser-Raspe, M, Manton, J, Grayden, DB, Tapson, J & Van Schaik, A 2012, Online learning in Bayesian Spiking Neurons. in 2012 International Joint Conference on Neural Networks, IJCNN 2012. IEEE, Institute of Electrical and Electronics Engineers, IEEE International Joint Conference on Neural Networks 2012, Brisbane, Queensland, Australia, 10/06/12. https://doi.org/10.1109/IJCNN.2012.6252370

TY - GEN

T1 - Online learning in Bayesian Spiking Neurons

AU - Kuhlmann, Levin

AU - Hauser-Raspe, Michael

AU - Manton, Jonathan

AU - Grayden, David B.

AU - Tapson, Jonathan

AU - Van Schaik, André

PY - 2012/8/22

Y1 - 2012/8/22

N2 - Bayesian Spiking Neurons (BSNs) provide a probabilistic interpretation of how neurons can perform inference and learning. Learning in a single BSN can be formulated as an online maximum-likelihood expectation-maximisation (ML-EM) algorithm. This form of learning is quite slow. Here, an alternative to this learning algorithm, called Fast Learning (FL), is presented. The FL algorithm is shown to have acceptable convergence performance when compared to the ML-EM algorithm. Moreover, for our implementations the FL algorithm is approximately 25 times faster than the ML-EM algorithm. Although only approximate, the FL algorithm therefore makes learning in hierarchical BSN networks much more tractable.

AB - Bayesian Spiking Neurons (BSNs) provide a probabilistic interpretation of how neurons can perform inference and learning. Learning in a single BSN can be formulated as an online maximum-likelihood expectation-maximisation (ML-EM) algorithm. This form of learning is quite slow. Here, an alternative to this learning algorithm, called Fast Learning (FL), is presented. The FL algorithm is shown to have acceptable convergence performance when compared to the ML-EM algorithm. Moreover, for our implementations the FL algorithm is approximately 25 times faster than the ML-EM algorithm. Although only approximate, the FL algorithm therefore makes learning in hierarchical BSN networks much more tractable.

UR - https://www.scopus.com/pages/publications/84865085717

U2 - 10.1109/IJCNN.2012.6252370

DO - 10.1109/IJCNN.2012.6252370

M3 - Conference Paper

AN - SCOPUS:84865085717

SN - 9781467314909

BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - IEEE International Joint Conference on Neural Networks 2012

Y2 - 10 June 2012 through 15 June 2012

ER -

Online learning in Bayesian Spiking Neurons

Abstract

Conference

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