Real-time event-driven spiking neural network object recognition on the SpiNNaker platformCitation formats

  • External authors:
  • Garrick Orchard
  • Xavier Lagorce
  • Christoph Posch
  • Ryad Benosman
  • Francesco Galluppi

Standard

Real-time event-driven spiking neural network object recognition on the SpiNNaker platform. / Orchard, Garrick; Lagorce, Xavier; Posch, Christoph; Furber, Stephen; Benosman, Ryad; Galluppi, Francesco.

Proceedings - IEEE International Symposium on Circuits and Systems. Vol. 2015-July IEEE, 2015. p. 2413-2416 7169171.

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

Harvard

Orchard, G, Lagorce, X, Posch, C, Furber, S, Benosman, R & Galluppi, F 2015, Real-time event-driven spiking neural network object recognition on the SpiNNaker platform. in Proceedings - IEEE International Symposium on Circuits and Systems. vol. 2015-July, 7169171, IEEE, pp. 2413-2416, IEEE International Symposium on Circuits and Systems, ISCAS 2015, Lisbon, Portugal, 24/05/15. https://doi.org/10.1109/ISCAS.2015.7169171

APA

Orchard, G., Lagorce, X., Posch, C., Furber, S., Benosman, R., & Galluppi, F. (2015). Real-time event-driven spiking neural network object recognition on the SpiNNaker platform. In Proceedings - IEEE International Symposium on Circuits and Systems (Vol. 2015-July, pp. 2413-2416). [7169171] IEEE. https://doi.org/10.1109/ISCAS.2015.7169171

Vancouver

Orchard G, Lagorce X, Posch C, Furber S, Benosman R, Galluppi F. Real-time event-driven spiking neural network object recognition on the SpiNNaker platform. In Proceedings - IEEE International Symposium on Circuits and Systems. Vol. 2015-July. IEEE. 2015. p. 2413-2416. 7169171 https://doi.org/10.1109/ISCAS.2015.7169171

Author

Orchard, Garrick ; Lagorce, Xavier ; Posch, Christoph ; Furber, Stephen ; Benosman, Ryad ; Galluppi, Francesco. / Real-time event-driven spiking neural network object recognition on the SpiNNaker platform. Proceedings - IEEE International Symposium on Circuits and Systems. Vol. 2015-July IEEE, 2015. pp. 2413-2416

Bibtex

@inproceedings{2787a546f07e4f128806bed14b1a1bce,
title = "Real-time event-driven spiking neural network object recognition on the SpiNNaker platform",
abstract = "This paper presents a real-time spiking neural network adaptation of the HMAX object recognition model on an event-driven platform. Visual input is provided by a spiking silicon retina, while the SpiNNaker system is used as a computational hardware platform for implementation. We show the implementation of a simple Leaky Integrate-and-Fire (LIF) neuron model on SpiNNaker to create an event driven network, where a neuron only updates when it receives an interrupt indicating that a new input spike has been received. The model output consists of view tuned neurons which respond selectively to a particular view of an object. The network can be used to discriminate between objects, or between the same object at different views. On a 26 class character recognition task, the correct class is always assigned the highest probability (69.42% on average).",
author = "Garrick Orchard and Xavier Lagorce and Christoph Posch and Stephen Furber and Ryad Benosman and Francesco Galluppi",
year = "2015",
month = jul,
day = "27",
doi = "10.1109/ISCAS.2015.7169171",
language = "English",
isbn = "9781479983919",
volume = "2015-July",
pages = "2413--2416",
booktitle = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "IEEE",
address = "United States",
note = "IEEE International Symposium on Circuits and Systems, ISCAS 2015 ; Conference date: 24-05-2015 Through 27-05-2015",
url = "http://www.scopus.com/inward/record.url?eid=2-s2.0-84946225388&partnerID=40&md5=58467212fe9944b83fe45c3ea4058d6b",

}

RIS

TY - GEN

T1 - Real-time event-driven spiking neural network object recognition on the SpiNNaker platform

AU - Orchard, Garrick

AU - Lagorce, Xavier

AU - Posch, Christoph

AU - Furber, Stephen

AU - Benosman, Ryad

AU - Galluppi, Francesco

PY - 2015/7/27

Y1 - 2015/7/27

N2 - This paper presents a real-time spiking neural network adaptation of the HMAX object recognition model on an event-driven platform. Visual input is provided by a spiking silicon retina, while the SpiNNaker system is used as a computational hardware platform for implementation. We show the implementation of a simple Leaky Integrate-and-Fire (LIF) neuron model on SpiNNaker to create an event driven network, where a neuron only updates when it receives an interrupt indicating that a new input spike has been received. The model output consists of view tuned neurons which respond selectively to a particular view of an object. The network can be used to discriminate between objects, or between the same object at different views. On a 26 class character recognition task, the correct class is always assigned the highest probability (69.42% on average).

AB - This paper presents a real-time spiking neural network adaptation of the HMAX object recognition model on an event-driven platform. Visual input is provided by a spiking silicon retina, while the SpiNNaker system is used as a computational hardware platform for implementation. We show the implementation of a simple Leaky Integrate-and-Fire (LIF) neuron model on SpiNNaker to create an event driven network, where a neuron only updates when it receives an interrupt indicating that a new input spike has been received. The model output consists of view tuned neurons which respond selectively to a particular view of an object. The network can be used to discriminate between objects, or between the same object at different views. On a 26 class character recognition task, the correct class is always assigned the highest probability (69.42% on average).

UR - http://www.scopus.com/inward/record.url?scp=84946220655&partnerID=8YFLogxK

U2 - 10.1109/ISCAS.2015.7169171

DO - 10.1109/ISCAS.2015.7169171

M3 - Conference contribution

AN - SCOPUS:84946220655

SN - 9781479983919

VL - 2015-July

SP - 2413

EP - 2416

BT - Proceedings - IEEE International Symposium on Circuits and Systems

PB - IEEE

T2 - IEEE International Symposium on Circuits and Systems, ISCAS 2015

Y2 - 24 May 2015 through 27 May 2015

ER -