On the Biological Plausibility of Artificial Metaplasticity

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)


The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausibility, a Multilayer Perceptron has been used. During the training phase, the artificial metaplasticity multilayer perceptron could be considered a new probabilistic version of the presynaptic rule, as during the training phase the algorithm assigns higher values for updating the weights in the less probable activations than in the ones with higher probability.


Synaptic Plasticity Training Phase Frequent Pattern Synaptic Weight Biological Plausibility 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Group for Automation in Signal and CommunicationsTechnical University of MadridSpain
  2. 2.Federal University of A.B.CBrazil

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