# Meta-Learning Biologically Plausible Plasticity Rules with Random Feedback Pathways

arXiv: https://arxiv.org/abs/2210.16414 · HTML: https://mlsanity.com/paper/2210.16414/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [Direct Feedback Alignment Provides Learning in Deep Neural Networks](https://mlsanity.com/paper/1609.01596/) (2016)
- [Meta-Learning Representations for Continual Learning](https://mlsanity.com/paper/1905.12588/) (2019)
- [Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures](https://mlsanity.com/paper/1807.04587/) (2018)
- [Dendritic cortical microcircuits approximate the backpropagation algorithm](https://mlsanity.com/paper/1810.11393/) (2018)
- [Deep Learning without Weight Transport](https://mlsanity.com/paper/1904.05391/) (2019)
- [Differentiable plasticity: training plastic neural networks with backpropagation](https://mlsanity.com/paper/1804.02464/) (2018)
- [How Important is Weight Symmetry in Backpropagation?](https://mlsanity.com/paper/1510.05067/) (2015)
- [Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity](https://mlsanity.com/paper/2002.10585/) (2020)
- [Meta-Learning Update Rules for Unsupervised Representation Learning](https://mlsanity.com/paper/1804.00222/) (2018)
- [Learning to solve the credit assignment problem](https://mlsanity.com/paper/1906.00889/) (2019)
- [Two Routes to Scalable Credit Assignment without Weight Symmetry](https://mlsanity.com/paper/2003.01513/) (2020)
- [Deep learning with asymmetric connections and Hebbian updates](https://mlsanity.com/paper/1812.07965/) (2018)
- [Learning to Learn with Feedback and Local Plasticity](https://mlsanity.com/paper/2006.09549/) (2020)
- [Spike-based causal inference for weight alignment](https://mlsanity.com/paper/1910.01689/) (2019)
- [Meta-Learning Bidirectional Update Rules](https://mlsanity.com/paper/2104.04657/) (2021)

## Built on by (most cited first, 14 of 14)

- [Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation](https://mlsanity.com/paper/2304.02658/) (2023)
- [Pretraining with Random Noise for Fast and Robust Learning without Weight Transport](https://mlsanity.com/paper/2405.16731/) (2024)
- [Toward Lifelong Learning in Equilibrium Propagation: Sleep-like and Awake Rehearsal for Enhanced Stability](https://mlsanity.com/paper/2508.14081/) (2025)
- [Where to Bind Matters: Hebbian Fast Weights in Vision Transformers for Few-Shot Character Recognition](https://mlsanity.com/paper/2605.02920/) (2026)
- [Deep Learning without Weight Symmetry](https://mlsanity.com/paper/2405.20594/) (2024)
- [Oja's plasticity rule overcomes several challenges of training neural networks under biological constraints](https://mlsanity.com/paper/2408.08408/) (2024)
- [Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systems](https://mlsanity.com/paper/2412.05126/) (2024)
- [Can Local Representation Alignment RNNs Solve Temporal Tasks?](https://mlsanity.com/paper/2504.13531/) (2025)
- [Forward Target Propagation: A Forward-Only Approach to Global Error Credit Assignment via Local Losses](https://mlsanity.com/paper/2506.11030/) (2025)
- [Eigen Neural Network: Unlocking Generalizable Vision with Eigenbasis](https://mlsanity.com/paper/2508.01219/) (2025)
- [Flexible inference for animal learning rules using neural networks](https://mlsanity.com/paper/2509.04661/) (2025)
- [Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback](https://mlsanity.com/paper/2512.09366/) (2025)
- [Hierarchical Zero-Order Optimization for Deep Neural Networks](https://mlsanity.com/paper/2602.10607/) (2026)
- [Online Task Adaptation via Self-Organisation](https://mlsanity.com/paper/2609.29281/) (2026)

## Abstract (arXiv)

Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such symmetric backward connectivity. Random feedback alignment offers an alternative model in which errors are propagated backward through fixed, random backward connections. This approach successfully trains shallow models, but learns slowly and does not perform well with deeper models or online learning. In this study, we develop a meta-learning approach to discover interpretable, biologically plausible plasticity rules that improve online learning performance with fixed random feedback connections. The resulting plasticity rules show improved online training of deep models in the low data regime. Our results highlight the potential of meta-learning to discover effective, interpretable learning rules satisfying biological constraints.