High-performance computing for systems of spiking neuronsCitation formats

Standard

High-performance computing for systems of spiking neurons. / Furber, S.B; Temple, Steve; Brown, Andrew.

Proceedings of AISB'06: Adaptation in Artificial and Biological Systems. Vol. 2 2006. p. 29-36.

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Harvard

Furber, SB, Temple, S & Brown, A 2006, High-performance computing for systems of spiking neurons. in Proceedings of AISB'06: Adaptation in Artificial and Biological Systems. vol. 2, pp. 29-36, AISB'06: Adaptation in Artificial and Biological Systems, Bristol, United Kingdom, 3/04/06.

APA

Furber, S. B., Temple, S., & Brown, A. (2006). High-performance computing for systems of spiking neurons. In Proceedings of AISB'06: Adaptation in Artificial and Biological Systems (Vol. 2, pp. 29-36)

Vancouver

Furber SB, Temple S, Brown A. High-performance computing for systems of spiking neurons. In Proceedings of AISB'06: Adaptation in Artificial and Biological Systems. Vol. 2. 2006. p. 29-36

Author

Furber, S.B ; Temple, Steve ; Brown, Andrew. / High-performance computing for systems of spiking neurons. Proceedings of AISB'06: Adaptation in Artificial and Biological Systems. Vol. 2 2006. pp. 29-36

Bibtex

@inproceedings{3f3be17549704d09af3602b87a703b35,
title = "High-performance computing for systems of spiking neurons",
abstract = "We propose a bottom-up computer engineering approach to the Grand Challenge of understanding the Architecture of Brain and Mind as a viable complement to top-down modelling and alternative approaches informed by the skills and philosophies of other disciplines. Our approach starts from the observation that brains are built from spiking neurons and then progresses by looking for a systematic way to deploy spiking neurons as components from which useful information processing functions can be constructed, at all stages being informed (but not constrained) by the neural structures and microarchitectures observed by neuroscientists as playing a role in biological systems. In order to explore the behaviours of large-scale complex systems of spiking neuron components we require high-performance computing equipment, and we propose the construction of a machine specifically for this task - a massively parallel computer designed to be a universal spiking neural network simulation engine.",
author = "S.B Furber and Steve Temple and Andrew Brown",
year = "2006",
language = "English",
volume = "2",
pages = "29--36",
booktitle = "Proceedings of AISB'06: Adaptation in Artificial and Biological Systems",
note = "AISB'06: Adaptation in Artificial and Biological Systems ; Conference date: 03-04-2006 Through 06-04-2006",

}

RIS

TY - GEN

T1 - High-performance computing for systems of spiking neurons

AU - Furber, S.B

AU - Temple, Steve

AU - Brown, Andrew

PY - 2006

Y1 - 2006

N2 - We propose a bottom-up computer engineering approach to the Grand Challenge of understanding the Architecture of Brain and Mind as a viable complement to top-down modelling and alternative approaches informed by the skills and philosophies of other disciplines. Our approach starts from the observation that brains are built from spiking neurons and then progresses by looking for a systematic way to deploy spiking neurons as components from which useful information processing functions can be constructed, at all stages being informed (but not constrained) by the neural structures and microarchitectures observed by neuroscientists as playing a role in biological systems. In order to explore the behaviours of large-scale complex systems of spiking neuron components we require high-performance computing equipment, and we propose the construction of a machine specifically for this task - a massively parallel computer designed to be a universal spiking neural network simulation engine.

AB - We propose a bottom-up computer engineering approach to the Grand Challenge of understanding the Architecture of Brain and Mind as a viable complement to top-down modelling and alternative approaches informed by the skills and philosophies of other disciplines. Our approach starts from the observation that brains are built from spiking neurons and then progresses by looking for a systematic way to deploy spiking neurons as components from which useful information processing functions can be constructed, at all stages being informed (but not constrained) by the neural structures and microarchitectures observed by neuroscientists as playing a role in biological systems. In order to explore the behaviours of large-scale complex systems of spiking neuron components we require high-performance computing equipment, and we propose the construction of a machine specifically for this task - a massively parallel computer designed to be a universal spiking neural network simulation engine.

UR - http://www.scopus.com/inward/record.url?scp=34047160903&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:34047160903

VL - 2

SP - 29

EP - 36

BT - Proceedings of AISB'06: Adaptation in Artificial and Biological Systems

T2 - AISB'06: Adaptation in Artificial and Biological Systems

Y2 - 3 April 2006 through 6 April 2006

ER -