In neuroscience and machine learning, three-factor learning is the combinaison of
Hebbian plasticity with a third modulatory factor to stabilise and enhance synaptic learning.
This third factor can represent various signals such as reward, punishment, error, surprise, or novelty, often implemented through
neuromodulators
Neuromodulation is the physiology, physiological process by which a given neuron uses one or more chemicals to regulate diverse populations of neurons. Neuromodulators typically bind to metabotropic receptor, metabotropic, G protein-coupled rece ...
.
Description
Three-factor learning introduces the concept of
eligibility traces, which flag synapses for potential modification pending the arrival of the third factor, and helps temporal
credit assignement by bridging the gap between rapid neuronal firing and slower behavioral timescales, from which learning can be done. Biological basis for Three-factor learning rules have been supported by experimental evidence.
This approach addresses the instability of classical Hebbian learning by minimizing
autocorrelation
Autocorrelation, sometimes known as serial correlation in the discrete time case, measures the correlation of a signal with a delayed copy of itself. Essentially, it quantifies the similarity between observations of a random variable at differe ...
and maximizing
cross-correlation
In signal processing, cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other. This is also known as a ''sliding dot product'' or ''sliding inner-product''. It is commonly used f ...
between inputs.
References
Machine learning
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