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cc18:exploring-unsupervised-online-learning-in-digital-spiking-neural-networks:overview

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Papers

  • C. Frenkel, J.-D. Legat and D. Bol, “A 0.086-mm² 9.8-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28nm CMOS”, arXiv preprint arXiv:xxxx:xxxxx, 2018 (available on Monday April 23rd).
  • C. Frenkel et al., “A fully-synthesized 20-gate digital spike-based synapse with embedded online learning, ” Proc. of IEEE International Symposium on Circuits and Systems (ISCAS), pp. 17-20, 2017. frenkel_iscas17.pdf
  • C. Frenkel, J.-D. Legat and D. Bol, “A Compact Phenomenological Digital Neuron Implementing the 20 Izhikevich Behaviors, ” Proc. of IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 677-680, 2017. frenkel_biocas17.pdf
  • J. M. Brader, W. Senn and S. Fusi, “Learning real-world stimuli in a neural network with spike-driven synaptic dynamics, ” Neural Computation, vol. 19, no. 11, pp. 2881-2912, 2007. brader_neuralcomputation07.pdf
  • R. Kreiser et al., “On-chip unsupervised learning in winner-take-all networks of spiking neurons,” Proc. of Biomedical Circuits and Systems Conference (BioCAS), 2017. kreiser_biocas17.pdf

Useful links

Shrinked MNIST datasets from 6×6 to 16×16 pixels are available here.

cc18/exploring-unsupervised-online-learning-in-digital-spiking-neural-networks/overview.1524303407.txt.gz · Last modified: 2019/05/16 20:14 (external edit)