Contrastive Hebbian learning is a biologically plausible form of
Hebbian learning
Hebbian theory is a neuropsychological theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptat ...
.
It is based on the contrastive divergence algorithm, which has been used to train a variety of energy-based latent variable models.
In 2003, contrastive Hebbian learning was shown to be equivalent in power to the
backpropagation
In machine learning, backpropagation is a gradient computation method commonly used for training a neural network to compute its parameter updates.
It is an efficient application of the chain rule to neural networks. Backpropagation computes th ...
algorithms commonly used in
machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task ( ...
.
See also
*
Oja's rule
*
Generalized Hebbian algorithm
References
Hebbian theory
Artificial neural networks
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