Theoretical Analysis of Spike-Timing-Dependent Plasticity Learning with Memristive Devices

  • Damien Querlioz
  • Olivier Bichler
  • Adrien F. Vincent
  • Christian Gamrat
Part of the Cognitive Systems Monographs book series (COSMOS, volume 31)


Several recent works, described in chapters of the present series, have shown that memristive devices can naturally emulate variations of the biological spike-timing-dependent plasticity (STDP) learning rule and can allow the design of learning systems. Such systems can be built with memristive devices of extremely diverse physics and behaviors and are particularly robust to device variations and imperfections. The present work investigates the theoretical roots of their STDP learning. It is suggested, by revisiting works developed in the field of computational neuroscience, that STDP learning can approximate the machine learning algorithm of Expectation-Maximization, the neural network operation implementing “Expectation” steps, while STDP itself implementing “Maximization” steps. This process allows a system to perform Bayesian inference among the values of a latent variable present in the input. This theoretical analysis allows interpreting how STDP differs for several device physics and why it is robust to device mismatch. It can also provide guidelines for designing STDP-based learning systems.


Output Neuron Synaptic Weight Inference Engine Phase Change Memory Memristive Device 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank C. Bennett, P. Bessière, L. Calvet, D. Chabi, D. Colliaux, B. De Salvo, J. Droulez, J. S. Friedman, J. Grollier, J.-O. Klein, N. Locatelli, E. Mazer, A. Mizrahi, M. Suri, S. Tiwari, D. Vodenicarevic, and W. S. Zhao. The works presented in this chapter were funded by the ANR COGNISPIN (ANR-13-JS03-0004-01) and the FP7 ICT BAMBI (FP7-ICT-2013-C) projects and by a public grant overseen by the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (Labex NanoSaclay, reference: ANR-10-LABX-0035).


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Copyright information

© Springer (India) Pvt. Ltd. 2017

Authors and Affiliations

  • Damien Querlioz
    • 1
  • Olivier Bichler
    • 2
  • Adrien F. Vincent
    • 1
  • Christian Gamrat
    • 2
  1. 1.CNRS, Institut d’Electronique Fondamentale, University of Paris-SudOrsayFrance
  2. 2.CEA, LISTSaclayFrance

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