# Learning to Learn with Feedback and Local Plasticity

arXiv: https://arxiv.org/abs/2006.09549 · HTML: https://mlsanity.com/paper/2006.09549/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

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

- [Meta-Learning Biologically Plausible Plasticity Rules with Random Feedback Pathways](https://mlsanity.com/paper/2210.16414/) (2022)
- [Meta-Learning Bidirectional Update Rules](https://mlsanity.com/paper/2104.04657/) (2021)
- [Meta-learning Spiking Neural Networks with Surrogate Gradient Descent](https://mlsanity.com/paper/2201.10777/) (2022)
- [Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16235/) (2023)
- [Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules](https://mlsanity.com/paper/2206.00823/) (2022)
- [Deep Layer-wise Networks Have Closed-Form Weights](https://mlsanity.com/paper/2202.01210/) (2022)
- [Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures](https://mlsanity.com/paper/2308.04539/) (2023)
- [Curl Descent: Non-Gradient Learning Dynamics with Sign-Diverse Plasticity](https://mlsanity.com/paper/2510.02765/) (2025)
- [Feature Alignment as a Generative Process](https://mlsanity.com/paper/2106.12562/) (2021)
- [Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback](https://mlsanity.com/paper/2512.09366/) (2025)
- [SAL: Selective Adaptive Learning for Backpropagation-Free Training with Sparsification](https://mlsanity.com/paper/2601.21561/) (2026)

## Abstract (arXiv)

Interest in biologically inspired alternatives to backpropagation is driven by the desire to both advance connections between deep learning and neuroscience and address backpropagation's shortcomings on tasks such as online, continual learning. However, local synaptic learning rules like those employed by the brain have so far failed to match the performance of backpropagation in deep networks. In this study, we employ meta-learning to discover networks that learn using feedback connections and local, biologically inspired learning rules. Importantly, the feedback connections are not tied to the feedforward weights, avoiding biologically implausible weight transport. Our experiments show that meta-trained networks effectively use feedback connections to perform online credit assignment in multi-layer architectures. Surprisingly, this approach matches or exceeds a state-of-the-art gradient-based online meta-learning algorithm on regression and classification tasks, excelling in particular at continual learning. Analysis of the weight updates employed by these models reveals that they differ qualitatively from gradient descent in a way that reduces interference between updates. Our results suggest the existence of a class of biologically plausible learning mechanisms that not only match gradient descent-based learning, but also overcome its limitations.