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Volumn , Issue , 2008, Pages

Learning to classify complex patterns using a VLSI network of spiking neurons

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; LOW POWER ELECTRONICS; NEURAL NETWORKS; NEURONS; TEACHING; VLSI CIRCUITS;

EID: 85162070591     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (9)

References (15)
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  • 2
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    • DOI 10.1162/0899766054796888
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  • 4
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    • Learning real world stimuli in a neural network with spike-driven synaptic dynamics
    • In press
    • J. Brader,W. Senn, and S. Fusi. Learning real world stimuli in a neural network with spike-driven synaptic dynamics. Neural Computation, 2007. (In press).
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    • Brader, J.1    Senn, W.2    Fusi, S.3
  • 5
    • 33244465845 scopus 로고    scopus 로고
    • A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity
    • DOI 10.1109/TNN.2005.860850
    • G. Indiveri, E. Chicca, and R. Douglas. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Transactions on Neural Networks, 17(1):211-221, Jan 2006. (Pubitemid 43272460)
    • (2006) IEEE Transactions on Neural Networks , vol.17 , Issue.1 , pp. 211-221
    • Indiveri, G.1    Chicca, E.2    Douglas, R.3
  • 6
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    • Learning in silicon: Timing is everything
    • Y. Weiss, B. Schölkopf, and J. Platt, editors. MIT Press, Cambridge, MA
    • J. Arthur and K. Boahen. Learning in silicon: Timing is everything. In Y. Weiss, B. Schölkopf, and J. Platt, editors, Advances in Neural Information Processing Systems 18. MIT Press, Cambridge, MA, 2006.
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    • Arthur, J.1    Boahen, K.2
  • 7
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    • An aVLSI recurrent network of spiking neurons with reconfigurable and plastic synapses
    • 1692813, ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Proceedings
    • D. Badoni, M. Giulioni, V. Dante, and P. Del Giudice. An aVLSI recurrent network of spiking neurons with reconfigurable and plastic synapses. In Proceedings of the IEEE International Symposium on Circuits and Systems, pages 1227-1230. IEEE, IEEE, May 2006. (Pubitemid 47131741)
    • (2006) Proceedings - IEEE International Symposium on Circuits and Systems , pp. 1227-1230
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  • 8
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    • Spike-based learning in VLSI networks of integrate-and-fire neurons
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    • G. Indiveri and S. Fusi. Spike-based learning in VLSI networks of integrate-and-fire neurons. In Proc. IEEE International Symposium on Circuits and Systems, ISCAS 2007, pages 3371-3374, 2007. (Pubitemid 47449272)
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  • 9
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    • Limits on the memory storage capacity of bounded synapses
    • DOI 10.1038/nn1859, PII NN1859
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.