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The Role of Membrane Threshold and Rate in STDP Silicon Neuron Circuit Simulation

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Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

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Abstract

Spike-timing dependent synaptic plasticity (STDP) circuitry is designed in 0.35μm CMOS VLSI. By setting different circuit parameters and generating diverse spike inputs, we got different steady weight distributions. Through analysing these simulation results, we show the effect of membrane threshold and input rate in STDP adaptation.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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References

  1. van Rossum, G.G.M.C.W., Bi Turrigiano, G.Q.: Stable hebbian learning from spike timing-dependent plasticity. J. Neurosci. 20, 8812–8821 (2000)

    Google Scholar 

  2. Morris, R.G.M., Martin, S.J., Grimwood, P.D.: Synaptic plasticity and memory: An evaluation of the hypothesis. Annual Review of Neuroscience 23, 649–711 (2000)

    Article  Google Scholar 

  3. Miller, K.D., Song, S., Abbott, L.F.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neurosci. 3, 919–926 (2000)

    Article  Google Scholar 

  4. Di Paolo, E.: Spike-timing dependent plasticity for evolved robots. Adaptive Behavior 10, 243–263 (2002)

    Article  Google Scholar 

  5. Bofill-I-Petit, A., Murray, A.F.: Synchrony detection and amplification by silicon neurons with stdp synapses. IEEE Transactions on Neural Networks 15, 1296–1304 (2004)

    Article  Google Scholar 

  6. Tsodyks, M.: Spike-timing-dependent synaptic plasticity - the long road towards understanding neuronal mechanisms of learning and memory. Trends in Neurosciences 25(12), 599–600 (2002)

    Article  Google Scholar 

  7. Koch, C.: Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, New York (1999)

    Google Scholar 

  8. Aertsen, A., Kuhn, A., Rotter, S.: Correlated input spike trains and their effects on the response of the leaky integrate-and-fire neuron. Neurocomputing 44-46, 121–126 (2002)

    Article  Google Scholar 

  9. Sjostrom, G.G., Turrigiano, P.J., Nelson, S.B.: Rate, timing, and cooperativity jointly determine cortical synaptic plasticity. Neuron 32, 1149–1164 (2001)

    Article  Google Scholar 

  10. Ferster, D., Carandini, M.: Membrane potential and firing rate in cat primary visual cortex. Neuroscience 20, 470–484 (2000)

    Google Scholar 

  11. Curtin, J.: Determinants of Spike Timing-Dependent Synaptic Plasticity. Neuron. 32(6), 966–968 (2001)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electronics and Engineering, The University of Edinburgh, Edinburgh, EH9 3JL, UK

    Juan Huo & Alan Murray

Authors
  1. Juan Huo
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  2. Alan Murray
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Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

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© 2005 Springer-Verlag Berlin Heidelberg

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Huo, J., Murray, A. (2005). The Role of Membrane Threshold and Rate in STDP Silicon Neuron Circuit Simulation. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_159

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  • DOI: https://doi.org/10.1007/11550907_159

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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