Cognitive Neuromorphic Engineering Workshop

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  • 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:1804.07858, 2018. frenkel_arxiv18.pdf
  • 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
  • P. U. Diehl and M. Cook, “Unsupervised learning of digit recognition using spike-timing-dependent plasticity,” Frontiers in Computational Neuroscience, vol. 9, p. 99, 2015. link.

Useful links

Shrinked MNIST datasets from 8×8 to 16×16 pixels are available here.

Demo slide

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