# Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons Source: [Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:23:24.960Z Authors: Rasmus H{\\o}ier, D. Staudt, Christopher Zach Published: 2023-02-02T17:07:36.000Z ## Abstract Activity difference based learning algorithms-such as contrastive Hebbian learning and equilibrium propagation-have been proposed as biologically plausible alternatives to error back-propagation. However, on traditional digital chips these algorithms suffer from having to solve a costly inference problem twice, making these approaches more than two orders of magnitude slower than back-propagation. In the analog realm equilibrium propagation may be promising for fast and energy efficient learning, but states still need to be inferred and stored twice. Inspired by lifted neural networks and compartmental neuron models we propose a simple energy based compartmental neuron model, termed dual propagation, in which each neuron is a dyad with two intrinsic states. At inference time these intrinsic states encode the error/activity duality through their difference and their mean respectively. The advantage of this method is that only a single inference phase is needed and that inference can be solved in layerwise closed-form. Experimentally we show on common computer vision datasets, including Imagenet32x32, that dual propagation performs equivalently to back-propagation both in terms of accuracy and runtime. ## 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 ImageNet 32x32 1.28M 1000 (test)](): Image Classification; ImageNet 32x32 1.28M 1000 (test); Top-1 Accuracy: 58.52. ## Original sources and implementation [Original paper]() [PDF]()