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ref: -2019 tags: optical neural networks spiking phase change material learning date: 06-01-2019 19:00 gmt revision:4 [3] [2] [1] [0] [head]

All-optical spiking neurosynaptic networks with self-learning capabilities

  • J. Feldmann, N. Youngblood, C. D. Wright, H. Bhaskaran & W. H. P. Pernice
  • Idea: use phase-change material to either block or pass the light in waveguides.
    • In this case, they used GST -- germanium-antimony-tellurium. This material is less reflective in the amorphous phase, which can be reached by heating to ~150C and rapidly quenching. It is more reflective in the crystalline phase, which occurs on annealing.
  • This is used for both plastic synapses (phase change driven by the intensity of the light) and the nonlinear output of optical neurons (via a ring resonator).
  • Uses optical resonators with very high Q factors to couple different wavelengths of light into the 'dendrite'.
  • Ring resonator on the output: to match the polarity of the phase-change material. Is this for reset? Storing light until trigger?
  • Were able to get correlative-like or hebbian learning (which I suppose is not dissimilar from really slow photographic film, just re-branded, and most importantly with nonlinear feedback.)
  • Issue: every weight needs a different source wavelength! Hence they have not demonstrated a multi-layer network.
  • Previous paper: All-optical nonlinear activation function for photonic neural networks
    • Only 3db and 7db extinction ratios for induced transparency and inverse saturation

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ref: Dethier-2011.28 tags: BMI decoder spiking neural network Kalman date: 01-06-2012 00:20 gmt revision:1 [0] [head]

IEEE-5910570 (pdf) Spiking neural network decoder for brain-machine interfaces

  • Golden standard: kalman filter.
  • Spiking neural network got within 1% of this standard.
  • THe 'neuromorphic' approach.
  • Used Nengo, freely available neural simulator.

____References____

Dethier, J. and Gilja, V. and Nuyujukian, P. and Elassaad, S.A. and Shenoy, K.V. and Boahen, K. Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on 396 -399 (2011)

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ref: Nicolelis-1998.11 tags: spatiotemporal spiking nicolelis somatosensory tactile S1 3b microwire array rate temporal coding code date: 12-28-2011 20:42 gmt revision:3 [2] [1] [0] [head]

PMID-10196571[0] Simultaneous encoding of tactile information by three primate cortical areas

  • owl monkeys.
  • used microwires arrays to decode the location of tactile stimuli; location was encoded through te population, not within single units.
  • areas 3b, S1 & S2.
  • used LVQ (learning vector quantization) backprop, LDA to predict/ classify touch trials; all yielded about the same ~60% accuracy. Chance level 33%.
  • Interesting: "the spatiotemporal character of neuronal responses in the SII cortex was shown to contain the requisite information for the encoding of stimulus location using temporally patterned spike sequences, whereas the simultaneously recorded neuronal responses in areas 3b and 2 contained the requisite information for rate coding."
    • They support this result by varying bin widths and looking at the % of correctly classivied trials. in SII, increasing bin width decreases (slightly but significantly) the prediction accuracy.

____References____

[0] Nicolelis MA, Ghazanfar AA, Stambaugh CR, Oliveira LM, Laubach M, Chapin JK, Nelson RJ, Kaas JH, Simultaneous encoding of tactile information by three primate cortical areas.Nat Neurosci 1:7, 621-30 (1998 Nov)

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ref: bookmark-0 tags: spiking neuron models learning SRM spike response model date: 0-0-2006 0:0 revision:0 [head]

http://diwww.epfl.ch/~gerstner/SPNM/SPNM.html