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Hebbian theory

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The theory is often summarized as "cells that fire together, wire together", although this is an oversimplification of the nervous system not to be taken literally, as well as not accurately representing Hebb's original statement on cell connectivity strength changes. The theory is commonly evoked to explain some types of associative learning in which simultaneous activation of cells leads to pronounced increases in synaptic strength. Such learning is known as Hebbian learning.

Hebbian engrams and cell assembly theoryEdit


"When one cell repeatedly assists in firing another, the axon of the first cell develops synaptic knobs (or enlarges them if they already exist) in contact with the soma of the second cell." (Hebb 1949, p. 63)








This original principle is perhaps the simplest form of weight selection. While this means it can be relatively easily coded into a computer program and used to update the weights for a network, it also prohibits the number of applications of Hebbian learning. Today, the term Hebbian learning generally refers to some form of mathematical abstraction of the original principle proposed by Hebb. In this sense, Hebbian learning involves weights between learning nodes being adjusted so that each weight better represents the relationship between the nodes. As such, many learning methods can be considered to be somewhat Hebbian in nature.




Another formulaic description is:




Generalization and stabilityEdit

Hebb's Rule is often generalized as

\,\Delta w_i = \eta x_i y ,


\,y = \sum_j w_j x_j ,

and the previous section's simplification takes both the learning rate and the input weights to be 1. This version of the rule is clearly unstable, as in any network with a dominant signal the synaptic weights will increase or decrease exponentially. However, it can be shown that for any neuron model, Hebb's rule is unstable. Therefore, network models of neurons usually employ other learning theories such as BCM theory, Oja's rule[1], or the Generalized Hebbian Algorithm.

See alsoEdit


  1. Shouval, Harel The Physics of the Brain. The Synaptic basis for Learning and Memory: A theoretical approach. The University of Texas Health Science Center at Houston. URL accessed on 2007-11-14.

Further readingEdit

  • Hebb, D.O. (1949), The organization of behavior, New York: Wiley 
  • Hebb, D.O. (1961). "Distinctive features of learning in the higher animal" J. F. Delafresnaye (Ed.) Brain Mechanisms and Learning, London: Oxford University Press.
  • Hebb, D.O., and Penfield, W. (1940). Human behaviour after extensive bilateral removal from the frontal lobes. Archives of Neurology and Psychiatry 44: 421–436.
  • Allport, D.A. (1985). "Distributed memory, modular systems and dysphasia" Newman, S.K. and Epstein, R. (Eds.) Current Perspectives in Dysphasia, Edinburgh: Churchill Livingstone.
  • Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford: Oxford University Press.
  • Paulsen, O., Sejnowski, T. J. (2000). Natural patterns of activity and long-term synaptic plasticity. Current opinion in neurobiology 10 (2): 172–179.

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