Hebbian learning

Hebbian learning

(artificial intelligence)
The most common way to train a neural network; a kind of unsupervised learning; named after canadian neuropsychologist, Donald O. Hebb.

The algorithm is based on Hebb's Postulate, which states that where one cell's firing repeatedly contributes to the firing of another cell, the magnitude of this contribution will tend to increase gradually with time. This means that what may start as little more than a coincidental relationship between the firing of two nearby neurons becomes strongly causal.

Despite limitations with Hebbian learning, e.g., the inability to learn certain patterns, variations such as Signal Hebbian Learning and Differential Hebbian Learning are still used.

http://neuron-ai.tuke.sk/NCS/VOL1/P3_html/node14.html.
This article is provided by FOLDOC - Free Online Dictionary of Computing (foldoc.org)
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[30.] Caporale N, Dan Y (2008) Spike Timing-Dependent Plasticity: A Hebbian Learning Rule.
A few examples of unsupervised machine learning techniques are Hebbian learning, expectation-maximisation algorithm and blind signal separation (Michie, Spiegelhalter, and Taylor 1994).
In this work, we provide the Hebbian learning rule for TMQHNNs and evaluate the storage capacity based on Jankowski's concept.
The competitive neural network (Figure 3) relies fundamentally on the Hebbian learning rule.
This question is relevant, because only if signals integrated into superficial cortical layers and activated similar neurons, an influence of vision of touch on S-I-mediated Hebbian learning would be expected.
Neural PCA and Maximum Likelihood Hebbian Learning (MLHL).
A number of weight-learning methods, such as Hebbian learning [26, 27], genetic algorithm (GA) [28], and swarm intelligence optimization algorithm [29], have been applied to learning weights of an FCM.
Pulvermuller, "Recruitment and consolidation of cell assemblies for words by way of hebbian learning and competition in a multi-layer neural network," Cognitive Computation, vol.
If the user responds similarly, then neurons representing BabyX's actions begin to associate with neurons responding to the user's action through a process called Hebbian learning. Neurons that fire together, wire together."
In an approach to an answer, Arabi discussed concepts such as Hebbian learning (neurons that fire together wire together, neurons that fire out of sync lose their link) and spike timing dependent plasticity.
Keysers and Perrett (2004) suggest that mirror neurons work according to a Hebbian learning model.
A growing body of evidence suggests that neural representations of movements and sounds may become linked through mechanisms such as Hebbian learning or as an emergent property of an auditory-motor loop [6].