Contrastive Hebbian learning
Contrastive Hebbian learning is a biologically plausible form of Hebbian learning.
It is based on the contrastive divergence algorithm, which has been used to train a variety of energy-based latent variable models.[1]
In 2003, contrastive Hebbian learning was shown to be equivalent in power to the backpropagation algorithms commonly used in machine learning.[2]
See also[edit]
References[edit]
- ^ Qiu, Yixuan; Zhang, Lingsong; Wang, Xiao (2019-09-25). "Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models".
{{cite journal}}: Cite journal requires|journal=(help) presented at the International Conference on Learning Representations, 2019 - ^ Xie, Xiaohui; Seung, H. Sebastian (February 2003). "Equivalence of backpropagation and contrastive Hebbian learning in a layered network". Neural Computation. 15 (2): 441–454. doi:10.1162/089976603762552988. ISSN 0899-7667. PMID 12590814. S2CID 11201868.
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