# Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks

arXiv: https://arxiv.org/abs/1411.6191 · HTML: https://mlsanity.com/paper/1411.6191/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2014.

## Built on by (most cited first, 11 of 11)

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- [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16235/) (2023)
- [NoProp: Training Neural Networks without Full Back-propagation or Full Forward-propagation](https://mlsanity.com/paper/2503.24322/) (2025)
- [Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods](https://mlsanity.com/paper/2101.03419/) (2021)
- [Gradient Forward-Propagation for Large-Scale Temporal Video Modelling](https://mlsanity.com/paper/2106.08318/) (2021)
- [Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework](https://mlsanity.com/paper/2310.09952/) (2023)
- [Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates](https://mlsanity.com/paper/2111.13187/) (2021)
- [ADA-GP: Accelerating DNN Training By Adaptive Gradient Prediction](https://mlsanity.com/paper/2305.13236/) (2023)
- [Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations](https://mlsanity.com/paper/2603.03234/) (2026)

## Abstract (arXiv)

Error backpropagation is an extremely effective algorithm for assigning credit in artificial neural networks. However, weight updates under Backprop depend on lengthy recursive computations and require separate output and error messages -- features not shared by biological neurons, that are perhaps unnecessary. In this paper, we revisit Backprop and the credit assignment problem. We first decompose Backprop into a collection of interacting learning algorithms; provide regret bounds on the performance of these sub-algorithms; and factorize Backprop's error signals. Using these results, we derive a new credit assignment algorithm for nonparametric regression, Kickback, that is significantly simpler than Backprop. Finally, we provide a sufficient condition for Kickback to follow error gradients, and show that Kickback matches Backprop's performance on real-world regression benchmarks.