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
References
References
- 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.