# Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits

arXiv: https://arxiv.org/abs/2303.18187 · HTML: https://mlsanity.com/paper/2303.18187/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 30 Mar 2023.

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

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [The Forward-Forward Algorithm: Some Preliminary Investigations](https://mlsanity.com/paper/2212.13345/) (2022)
- [Training Deep Spiking Neural Networks using Backpropagation](https://mlsanity.com/paper/1608.08782/) (2016)
- [Decoupled Neural Interfaces using Synthetic Gradients](https://mlsanity.com/paper/1608.05343/) (2016)
- [Deep supervised learning using local errors](https://mlsanity.com/paper/1711.06756/) (2017)
- [A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule](https://mlsanity.com/paper/1812.06574/) (2018)
- [The Predictive Forward-Forward Algorithm](https://mlsanity.com/paper/2301.01452/) (2023)
- [Signal Propagation: A Framework for Learning and Inference In a Forward Pass](https://mlsanity.com/paper/2204.01723/) (2022)
- [Spiking Neural Predictive Coding for Continual Learning from Data Streams](https://mlsanity.com/paper/1908.08655/) (2019)
- [The Cascaded Forward Algorithm for Neural Network Training](https://mlsanity.com/paper/2303.09728/) (2023)
- [Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning](https://mlsanity.com/paper/1808.03357/) (2018)
- [Unsupervised Learning with Self-Organizing Spiking Neural Networks](https://mlsanity.com/paper/1807.09374/) (2018)
- [Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations](https://mlsanity.com/paper/1709.06206/) (2017)
- [Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks](https://mlsanity.com/paper/1909.01311/) (2019)

## Built on by (most cited first, 10 of 10)

- [Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment](https://mlsanity.com/paper/2312.09257/) (2023)
- [A Review of Neuroscience-Inspired Machine Learning](https://mlsanity.com/paper/2403.18929/) (2024)
- [Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm](https://mlsanity.com/paper/2502.20411/) (2025)
- [The Forward-Forward Algorithm as a feature extractor for skin lesion classification: A preliminary study](https://mlsanity.com/paper/2307.00617/) (2023)
- [Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computing](https://mlsanity.com/paper/2406.16479/) (2024)
- [On the Improvement of Generalization and Stability of Forward-Only Learning via Neural Polarization](https://mlsanity.com/paper/2408.09210/) (2024)
- [FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks](https://mlsanity.com/paper/2507.23643/) (2025)
- [Meta-Representational Predictive Coding: Neuroscience-Informed Self-Supervised Learning](https://mlsanity.com/paper/2503.21796/) (2025)
- [NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework](https://mlsanity.com/paper/2605.15058/) (2026)
- [Forward-Only Convolutional Neural Networks with Learnable Channel-Class Assignment](https://mlsanity.com/paper/2606.09928/) (2026)

## Abstract (arXiv)

Brain-inspired machine intelligence research seeks to develop computational models that emulate the information processing and adaptability that distinguishes biological systems of neurons. This has led to the development of spiking neural networks, a class of models that promisingly addresses the biological implausibility and {the lack of energy efficiency} inherent to modern-day deep neural networks. In this work, we address the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks and propose contrastive-signal-dependent plasticity, a process which generalizes ideas behind self-supervised learning to facilitate local adaptation in architectures of event-based neuronal layers that operate in parallel. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks, crucially side-stepping the need for extra structure such as feedback synapses.