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Julian JN @julian-jn.bsky.social · 07/02/2025

🌟 Weekend Read: Advancing AI with Hebbian Learning! 🌟 Explore our latest research on integrating Hebbian learning into CNNs for more biologically realistic unsupervised models! 🔗 arxiv.org/abs/2501.17266 🔗 github.com/Julian-JN/Ad... 🌐 Follow @emp1.bsky.social & @citai.bsky.social for updates!

arxiv.org

Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks

The research presented in this paper advances the integration of Hebbian learning into Convolutional Neural Networks (CNNs) for image processing, systematically exploring different architectures to bu...

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Julian JN @julian-jn.bsky.social · 31/01/2025

🧠Excited to share my published paper [...] Brain on cold meds: "Look at my published paper!" Reality: "Sir, this is an arxiv preprint" Still thrilled to share our Hebbian CNN work though! 🤒✨ *Corrected version:* 🧠 Excited to share our new preprint [...]. Check it out: arxiv.org/abs/2501.17266

arxiv.org

Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks

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Julian JN @julian-jn.bsky.social · 30/01/2025

1. 🧠Excited to share my published paper "Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks" with @emp1.bsky.social. Available from: arxiv.org/abs/2501.17266

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Reposted by Julian JN

Esther Mondragón @e-mondragon.bsky.social · 06/01/2025

An advance of a paper that will soon be available in ArXiv. All merit to my wonderful student @julian-jn.bsky.social ! I hope you enjoy it. We present an optimal architecture that significantly enhances recent research aimed at integrating Hebbian learning with competition mechanisms and CNNs. ➡️

$Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks by Julian Jimenez-Nimo and Esther Mondragón The research presented in this paper advances the integration of Hebbian learning into Convolutional Neural Networks (CNNs) for image processing, systematically exploring different architectures to build an optimal configuration, adhering to biological tenability. Hebbian learning operates on local unsupervised neural information to form feature representations, providing an alternative to the popular but arguably biologically implausible and computationally intensive backpropagation learning algorithm. The suggested optimal architecture significantly enhances recent research aimed at integrating Hebbian learning with competition mechanisms and CNNs, expanding their representational capabilities by incorporating hard Winner-Takes-All (WTA) competition, Gaussian lateral inhibition mechanisms and Bienenstock–Cooper–Munro (BCM) learning rule in a single model. The resulting model achieved 76\% classification accuracy on CIFAR-10, rivalling its end-to-end backpropagation variant (77\%) and critically surpassing the state-of-the-art hard-WTA performance in CNNs of the same network depth (64.6\%) by 11.4\%. Moreover, results showed clear indications of sparse hierarchical learning through increasingly complex and abstract receptive fields. In summary, our implementation enhances both the performance and the generalisability of the learnt representations and constitutes a crucial step towards more biologically realistic artificial neural networks.$

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