# Spike-based local synaptic plasticity: A survey of computational models and neuromorphic circuits

arXiv: https://arxiv.org/abs/2209.15536 · HTML: https://mlsanity.com/paper/2209.15536/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2022.

## Built on by (most cited first, 13 of 13)

- [Brain-inspired learning in artificial neural networks: a review](https://mlsanity.com/paper/2305.11252/) (2023)
- [Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization](https://mlsanity.com/paper/2302.05440/) (2023)
- [Paired Competing Neurons Improving STDP Supervised Local Learning In Spiking Neural Networks](https://mlsanity.com/paper/2308.02194/) (2023)
- [Neuronal Competition Groups with Supervised STDP for Spike-Based Classification](https://mlsanity.com/paper/2410.17066/) (2024)
- [Faster Predictive Coding Networks via Better Initialization](https://mlsanity.com/paper/2601.20895/) (2026)
- [Neuromorphic dreaming: A pathway to efficient learning in artificial agents](https://mlsanity.com/paper/2405.15616/) (2024)
- [HiBerNAC: Hierarchical Brain-inspired Robotic Neural Agent Collective for Disentangling Complex Manipulation](https://mlsanity.com/paper/2506.08296/) (2025)
- [Training slow silicon neurons to control extremely fast robots with spiking reinforcement learning](https://mlsanity.com/paper/2601.21548/) (2026)
- [A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures](https://mlsanity.com/paper/2409.14918/) (2024)
- [TEXEL: A neuromorphic processor with on-chip learning for beyond-CMOS device integration](https://mlsanity.com/paper/2410.15854/) (2024)
- [Learning in Spiking Neural Networks with a Calcium-based Hebbian Rule for Spike-timing-dependent Plasticity](https://mlsanity.com/paper/2504.06796/) (2025)
- [Forward Only Learning for Orthogonal Neural Networks of any Depth](https://mlsanity.com/paper/2512.20668/) (2025)
- [NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework](https://mlsanity.com/paper/2605.15058/) (2026)

## Abstract (arXiv)

Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if and how they could be mapped onto neuromorphic hardware, and to compare their features and ease of implementation. To this end, in this survey, we provide a comprehensive overview of representative brain-inspired synaptic plasticity models and mixed-signal CMOS neuromorphic circuits within a unified framework. We review historical, bottom-up, and top-down approaches to modeling synaptic plasticity, and we identify computational primitives that can support low-latency and low-power hardware implementations of spike-based learning rules. We provide a common definition of a locality principle based on pre- and post-synaptic neuron information, which we propose as a fundamental requirement for physical implementations of synaptic plasticity. Based on this principle, we compare the properties of these models within the same framework, and describe the mixed-signal electronic circuits that implement their computing primitives, pointing out how these building blocks enable efficient on-chip and online learning in neuromorphic processing systems.