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Hebbian Learning


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...


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

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Correspondence to Yoonsuck Choe .

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Choe, Y. (2014). Hebbian Learning. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY.

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