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Hebbian Learning

Definition

Hebbian learning is a form of activity-dependent synaptic plasticity where correlated activation of pre- and postsynaptic neurons leads to the strengthening of the connection between the two neurons. The learning principle was first proposed by Hebb (1949), who postulated that a presynaptic neuron A, if successful in repeatedly activating a postsynaptic neuron B when itself (neuron A) is active, will gradually become more effective in activating neuron B. The concept is widely employed and investigated in both experimental and computational neuroscience.

Detailed Description

In this article, we will review computational formulations and theoretical bases of Hebbian learning and its variants. We will also briefly review neurobiological underpinnings of Hebbian learning. See Frégnac (2003), Gerstner and Kistler (2002), Shouval (2007), and Dayan and Abbott (2001) for a more in-depth review of this topic.

Basic Formulations

The formulation in this subsection closely follows that...

Keywords

  • Independent Component Analysis
  • Synaptic Weight
  • Postsynaptic Neuron
  • Hebbian Learning
  • Unbounded Growth

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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Authors and Affiliations

  1. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Yoonsuck Choe

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  1. Yoonsuck Choe
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Correspondence to Yoonsuck Choe .

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Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Choe, Y. (2014). Hebbian Learning. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_672-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_672-1

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