# Biologically-plausible learning algorithms can scale to large datasets

arXiv: https://arxiv.org/abs/1811.03567 · HTML: https://mlsanity.com/paper/1811.03567/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2018.

## Built on by (most cited first, 30 of 37)

- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
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- [Brain-inspired learning in artificial neural networks: a review](https://mlsanity.com/paper/2305.11252/) (2023)
- [Reverse Differentiation via Predictive Coding](https://mlsanity.com/paper/2103.04689/) (2021)
- [A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks](https://mlsanity.com/paper/2212.00720/) (2022)
- [The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks](https://mlsanity.com/paper/2210.02157/) (2022)
- [A Theoretical Framework for Inference Learning](https://mlsanity.com/paper/2206.00164/) (2022)
- [Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment](https://mlsanity.com/paper/2312.09257/) (2023)
- [How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective](https://mlsanity.com/paper/2106.08453/) (2021)
- [Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization](https://mlsanity.com/paper/2302.05440/) (2023)
- [Meta-Learning Bidirectional Update Rules](https://mlsanity.com/paper/2104.04657/) (2021)
- [Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms](https://mlsanity.com/paper/2108.13446/) (2021)
- [Is Bio-Inspired Learning Better than Backprop? Benchmarking Bio Learning vs. Backprop](https://mlsanity.com/paper/2212.04614/) (2022)
- [Improving equilibrium propagation without weight symmetry through Jacobian homeostasis](https://mlsanity.com/paper/2309.02214/) (2023)
- [Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks](https://mlsanity.com/paper/2402.11984/) (2024)
- [Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning](https://mlsanity.com/paper/2106.06401/) (2021)
- [Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods](https://mlsanity.com/paper/2101.03419/) (2021)
- [TinyProp -- Adaptive Sparse Backpropagation for Efficient TinyML On-device Learning](https://mlsanity.com/paper/2308.09201/) (2023)
- [Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation](https://mlsanity.com/paper/2406.05222/) (2024)
- [Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks](https://mlsanity.com/paper/2407.12516/) (2024)
- [Hyperspherical Forward-Forward with Prototypical Representations](https://mlsanity.com/paper/2605.00082/) (2026)
- [A Robust Backpropagation-Free Framework for Images](https://mlsanity.com/paper/2206.01820/) (2022)
- [Overcoming Rank Collapse in Feedback Alignment](https://mlsanity.com/paper/2606.11123/) (2026)
- [A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning](https://mlsanity.com/paper/2304.06738/) (2023)
- [Deep Learning without Weight Symmetry](https://mlsanity.com/paper/2405.20594/) (2024)
- [Artificial Human Intelligence: The role of Humans in the Development of Next Generation AI](https://mlsanity.com/paper/2409.16001/) (2024)
- [Spike-based alignment learning solves the weight transport problem](https://mlsanity.com/paper/2503.02642/) (2025)
- [One-Time Soft Alignment Enables Resilient Learning without Weight Transport](https://mlsanity.com/paper/2505.20892/) (2025)
- [Credit Assignment via Neural Manifold Noise Correlation](https://mlsanity.com/paper/2601.02636/) (2026)

## Abstract (arXiv)

The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this "weight transport problem" (Grossberg, 1987), two more biologically plausible algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP's weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study by Bartunov et al. (2018) evaluate variants of target-propagation (TP) and feedback alignment (FA) on MINIST, CIFAR, and ImageNet datasets, and find that although many of the proposed algorithms perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry algorithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights share signs but not magnitudes. We examine the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet, RetinaNet for MS COCO). Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks. These results complement the study by Bartunov et al. (2018), and establish a new benchmark for future biologically plausible learning algorithms on more difficult datasets and more complex architectures.