# Feedback alignment in deep convolutional networks

arXiv: https://arxiv.org/abs/1812.06488 · HTML: https://mlsanity.com/paper/1812.06488/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 30 of 31)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Brain-inspired Computational Intelligence via Predictive Coding](https://mlsanity.com/paper/2308.07870/) (2023)
- [Credit Assignment in Neural Networks through Deep Feedback Control](https://mlsanity.com/paper/2106.07887/) (2021)
- [The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks](https://mlsanity.com/paper/2210.02157/) (2022)
- [Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment](https://mlsanity.com/paper/2312.09257/) (2023)
- [Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons](https://mlsanity.com/paper/2302.01228/) (2023)
- [Towards Biologically Plausible Convolutional Networks](https://mlsanity.com/paper/2106.13031/) (2021)
- [Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization](https://mlsanity.com/paper/2302.05440/) (2023)
- [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)
- [Fixed-Weight Difference Target Propagation](https://mlsanity.com/paper/2212.10352/) (2022)
- [A Review of Neuroscience-Inspired Machine Learning](https://mlsanity.com/paper/2403.18929/) (2024)
- [Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods](https://mlsanity.com/paper/2101.03419/) (2021)
- [How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization](https://mlsanity.com/paper/2111.08706/) (2021)
- [Representation Learning in a Decomposed Encoder Design for Bio-inspired Hebbian Learning](https://mlsanity.com/paper/2401.08603/) (2023)
- [Curl Descent: Non-Gradient Learning Dynamics with Sign-Diverse Plasticity](https://mlsanity.com/paper/2510.02765/) (2025)
- [Overcoming Rank Collapse in Feedback Alignment](https://mlsanity.com/paper/2606.11123/) (2026)
- [Tourbillon: a Physically Plausible Neural Architecture](https://mlsanity.com/paper/2107.06424/) (2021)
- [Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks](https://mlsanity.com/paper/2110.10815/) (2021)
- [A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation](https://mlsanity.com/paper/2205.07292/) (2022)
- [A Goal-Driven Approach to Systems Neuroscience](https://mlsanity.com/paper/2311.02704/) (2023)
- [Deep Learning without Weight Symmetry](https://mlsanity.com/paper/2405.20594/) (2024)
- [Spike-based alignment learning solves the weight transport problem](https://mlsanity.com/paper/2503.02642/) (2025)
- [Self-Evidencing Through Hierarchical Gradient Decomposition: A Dissipative System That Maintains Non-Equilibrium Steady-State by Minimizing Variational Free Energy](https://mlsanity.com/paper/2510.17916/) (2025)
- [Conditioned Direct Feedback Alignment via Activity and Error Geometry](https://mlsanity.com/paper/2607.18574/) (2026)
- [A comparative study of back propagation and its alternatives on multilayer perceptrons](https://mlsanity.com/paper/2206.06098/) (2022)
- [Learning with augmented target information: An alternative theory of Feedback Alignment](https://mlsanity.com/paper/2304.01406/) (2023)
- [Zenkai -- Framework For Exploring Beyond Backpropagation](https://mlsanity.com/paper/2311.09663/) (2023)
- [Frozen Backpropagation: Relaxing Weight Symmetry in Deep Spiking Neural Networks](https://mlsanity.com/paper/2505.13741/) (2025)
- [Biological Plausibility and Representational Alignment of Feedback Alignment in Convolutional Networks](https://mlsanity.com/paper/2605.08564/) (2026)

## Abstract (arXiv)

Ongoing studies have identified similarities between neural representations in biological networks and in deep artificial neural networks. This has led to renewed interest in developing analogies between the backpropagation learning algorithm used to train artificial networks and the synaptic plasticity rules operative in the brain. These efforts are challenged by biologically implausible features of backpropagation, one of which is a reliance on symmetric forward and backward synaptic weights. A number of methods have been proposed that do not rely on weight symmetry but, thus far, these have failed to scale to deep convolutional networks and complex data. We identify principal obstacles to the scalability of such algorithms and introduce several techniques to mitigate them. We demonstrate that a modification of the feedback alignment method that enforces a weaker form of weight symmetry, one that requires agreement of weight sign but not magnitude, can achieve performance competitive with backpropagation. Our results complement those of Bartunov et al. (2018) and Xiao et al. (2018b) and suggest that mechanisms that promote alignment of feedforward and feedback weights are critical for learning in deep networks.