# Hebbian Learning with Global Direction

Source: [Hebbian Learning with Global Direction](<https://www.sota2.com/research/paper/697c3b00a8b01e76cc8da442>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-24T05:00:04.605Z

Authors: Wenjia Hua, Kejie Zhao, Luziwei Leng, Ran Cheng, Yuxin Ma, Qinghai Guo

Published: 2026-01-29T07:49:21.000Z

## Abstract

Backpropagation algorithm has driven the remarkable success of deep neural networks, but its lack of biological plausibility and high computational costs have motivated the ongoing search for alternative training methods. Hebbian learning has attracted considerable interest as a biologically plausible alternative to backpropagation. Nevertheless, its exclusive reliance on local information, without consideration of global task objectives, fundamentally limits its scalability. Inspired by the biological synergy between neuromodulators and local plasticity, we introduce a novel model-agnostic Global-guided Hebbian Learning (GHL) framework, which seamlessly integrates local and global information to scale up across diverse networks and tasks. In specific, the local component employs Oja's rule with competitive learning to ensure stable and effective local updates. Meanwhile, the global component introduces a sign-based signal that guides the direction of local Hebbian plasticity updates. Extensive experiments demonstrate that our method consistently outperforms existing Hebbian approaches. Notably, on large-scale network and complex datasets like ImageNet, our framework achieves the competitive results and significantly narrows the gap with standard backpropagation.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on CIFAR-10 (test) (Accuracy and Backprop Difference)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test-accuracy-and-backprop-difference.md>): Image Classification; CIFAR-10 (test); Accuracy: 86.41.

- [Image Classification on ImageNet ILSVRC-2012 (val)](<https://www.sota2.com/research/sota/image-classification-on-imagenet-ilsvrc-2012-val.md>): Image Classification; ImageNet ILSVRC-2012 (val); Top-1 Accuracy: 73.14.

- [Image Classification on CIFAR-10 (test)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test.md>): Image Classification; CIFAR-10 (test); Accuracy: 89.48.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2601.21367>)

[PDF](<https://arxiv.org/pdf/2601.21367.pdf>)