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A Gradient Rule for the Plasticity of a Neuron’s Intrinsic Excitability

  • Jochen Triesch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)

Abstract

While synaptic learning mechanisms have always been a core topic of neural computation research, there has been relatively little work on intrinsic learning processes, which change a neuron’s excitability. Here, we study a single, continuous activation model neuron and derive a gradient rule for the intrinsic plasticity based on information theory that allows the neuron to bring its firing rate distribution into an approximately exponential regime, as observed in visual cortical neurons. In simulations, we show that the rule works efficiently.

Keywords

Rate Distribution Input Distribution Intrinsic Excitability Intrinsic Plasticity Visual Cortical Neuron 
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 2005

Authors and Affiliations

  • Jochen Triesch
    • 1
    • 2
  1. 1.Department of Cognitive ScienceUC San DiegoLa JollaUSA
  2. 2.Frankfurt Institute for Advanced StudiesJohann Wolfgang Goethe UniversityFrankfurt am MainGermany

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