# Neuron-centric Hebbian Learning

arXiv: https://arxiv.org/abs/2403.12076 · HTML: https://mlsanity.com/paper/2403.12076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [The State of Sparsity in Deep Neural Networks](https://mlsanity.com/paper/1902.09574/) (2019)
- [Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science](https://mlsanity.com/paper/1707.04780/) (2017)
- [AutoML-Zero: Evolving Machine Learning Algorithms From Scratch](https://mlsanity.com/paper/2003.03384/) (2020)
- [Graph Structure of Neural Networks](https://mlsanity.com/paper/2007.06559/) (2020)
- [Meta-Learning through Hebbian Plasticity in Random Networks](https://mlsanity.com/paper/2007.02686/) (2020)
- [Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks](https://mlsanity.com/paper/1703.10371/) (2017)
- [Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation](https://mlsanity.com/paper/2303.00914/) (2023)
- [Topological Insights into Sparse Neural Networks](https://mlsanity.com/paper/2006.14085/) (2020)
- [Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking](https://mlsanity.com/paper/2011.07537/) (2020)
- [Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs](https://mlsanity.com/paper/2307.08197/) (2023)
- [Evolving and Merging Hebbian Learning Rules: Increasing Generalization by Decreasing the Number of Rules](https://mlsanity.com/paper/2104.07959/) (2021)
- [Structural Analysis of Sparse Neural Networks](https://mlsanity.com/paper/1910.07225/) (2019)
- [Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions](https://mlsanity.com/paper/1904.01709/) (2019)
- [Discovering Adaptable Symbolic Algorithms from Scratch](https://mlsanity.com/paper/2307.16890/) (2023)
- [Embodied Synaptic Plasticity with Online Reinforcement learning](https://mlsanity.com/paper/2003.01431/) (2020)

## Abstract (arXiv)

One of the most striking capabilities behind the learning mechanisms of the brain is the adaptation, through structural and functional plasticity, of its synapses. While synapses have the fundamental role of transmitting information across the brain, several studies show that it is the neuron activations that produce changes on synapses. Yet, most plasticity models devised for artificial Neural Networks (NNs), e.g., the ABCD rule, focus on synapses, rather than neurons, therefore optimizing synaptic-specific Hebbian parameters. This approach, however, increases the complexity of the optimization process since each synapse is associated to multiple Hebbian parameters. To overcome this limitation, we propose a novel plasticity model, called Neuron-centric Hebbian Learning (NcHL), where optimization focuses on neuron- rather than synaptic-specific Hebbian parameters. Compared to the ABCD rule, NcHL reduces the parameters from 5W to 5N, being W and N the number of weights and neurons, and usually N ll W. We also devise a ``weightless'' NcHL model, which requires less memory by approximating the weights based on a record of neuron activations. Our experiments on two robotic locomotion tasks reveal that NcHL performs comparably to the ABCD rule, despite using up to ~97 times less parameters, thus allowing for scalable plasticity