# Random feedback weights support learning in deep neural networks

arXiv: https://arxiv.org/abs/1411.0247 · HTML: https://mlsanity.com/paper/1411.0247/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2014.

## Built on by (most cited first, 30 of 47)

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- [Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms](https://mlsanity.com/paper/2108.13446/) (2021)
- [Learning efficient backprojections across cortical hierarchies in real time](https://mlsanity.com/paper/2212.10249/) (2022)
- [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16235/) (2023)
- [End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training](https://mlsanity.com/paper/2402.09050/) (2024)
- [Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning](https://mlsanity.com/paper/2106.06401/) (2021)
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- [MLAAN: Scaling Supervised Local Learning with Multilaminar Leap Augmented Auxiliary Network](https://mlsanity.com/paper/2406.16633/) (2024)
- [HPFF: Hierarchical Locally Supervised Learning with Patch Feature Fusion](https://mlsanity.com/paper/2407.05638/) (2024)
- [Biologically-inspired neuronal adaptation improves learning in neural networks](https://mlsanity.com/paper/2204.14008/) (2022)
- [LANCE: Low Rank Activation Compression for Efficient On-Device Continual Learning](https://mlsanity.com/paper/2509.21617/) (2025)
- [Hyperspherical Forward-Forward with Prototypical Representations](https://mlsanity.com/paper/2605.00082/) (2026)
- [ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients](https://mlsanity.com/paper/2108.04217/) (2021)
- [Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework](https://mlsanity.com/paper/2310.09952/) (2023)
- [Two Tales of Single-Phase Contrastive Hebbian Learning](https://mlsanity.com/paper/2402.08573/) (2024)
- [Employing Layerwised Unsupervised Learning to Lessen Data and Loss Requirements in Forward-Forward Algorithms](https://mlsanity.com/paper/2404.14664/) (2024)
- [Accelerated Predictive Coding Networks via Direct Kolen-Pollack Feedback Alignment](https://mlsanity.com/paper/2602.15571/) (2026)

## Abstract (arXiv)

The brain processes information through many layers of neurons. This deep architecture is representationally powerful, but it complicates learning by making it hard to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame to a neuron by computing exactly how it contributed to an error. To do this, it multiplies error signals by matrices consisting of all the synaptic weights on the neuron's axon and farther downstream. This operation requires a precisely choreographed transport of synaptic weight information, which is thought to be impossible in the brain. Here we present a surprisingly simple algorithm for deep learning, which assigns blame by multiplying error signals by random synaptic weights. We show that a network can learn to extract useful information from signals sent through these random feedback connections. In essence, the network learns to learn. We demonstrate that this new mechanism performs as quickly and accurately as backpropagation on a variety of problems and describe the principles which underlie its function. Our demonstration provides a plausible basis for how a neuron can be adapted using error signals generated at distal locations in the brain, and thus dispels long-held assumptions about the algorithmic constraints on learning in neural circuits.