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Bottleneck of using a single memristive device as a synapse

Merrikh Bayat, F ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1016/j.neucom.2012.12.027
  3. Publisher: 2013
  4. Abstract:
  5. In this study we will show that the variation rate of the memristance of the memristive device depends completely on its current memristance which means that it can change significantly with time during the learning phase. This phenomenon can degrade the performance of learning methods like Spike Timing-Dependent Plasticity (STDP) and cause the corresponding neuromorphic systems to become unstable
  6. Keywords:
  7. Synapse Hebbian learning ; Hebbian learning ; Learning methods ; Learning phase ; Memristance ; Memristive device ; Neuromorphic systems ; Computer applications ; Neural networks ; Memristors ; Artificial neural network ; Computer memory ; Devices ; Learning algorithm ; Mental performance ; Priority journal ; Spike timing dependent plasticity ; Synaptic potential
  8. Source: Neurocomputing ; Volume 115 , September , 2013 , Pages 166-168 ; 09252312 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0925231213000957