A forecast-based biologically-plausible STDP learning rule

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Authors:
  • Sergio Davies
  • Alexander Rast
  • Francesco Galluppi
  • Steve Furber


Spike Timing Dependent Plasticity (STDP) is a well known paradigm for learning in neural networks. In this paper we propose a new approach to this problem based on the standard STDP algorithm, with modifications and approximations, that relate the membrane potential with the LTP (Long Term Potentiation) part of the basic STDP rule. On the other side we use the standard STDP rule for the LTD (Long Term Depression) part of the algorithm. We show that on the basis of the membrane potential [5] it is possible to make a statistical prediction of the time needed by the neuron to reach the threshold, and therefore the LTP part of the STDP algorithm can be triggered when the neuron receives a spike.We present results that show the efficacy of this algorithm using one or more input patterns repeated over the whole time of the simulation. Through the approximations we suggest in this paper we introduce a learning rule that is easy to implement in simulators and reduces the execution time if compared with the standard STDP rule. © 2011 IEEE.

Bibliographical metadata

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks|Proc Int Jt Conf Neural Networks
Place of PublicationUSA
Number of pages7
ISBN (Print)9781457710865
Publication statusPublished - 2011
Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA
Event duration: 1 Jul 2011 → …


Conference2011 International Joint Conference on Neural Network, IJCNN 2011
CitySan Jose, CA
Period1/07/11 → …
Internet address