# Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP

arXiv: https://arxiv.org/abs/2306.04410 · HTML: https://mlsanity.com/paper/2306.04410/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 74 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 23 of 23)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Meta Networks](https://mlsanity.com/paper/1703.00837/) (2017)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Bayesian Model-Agnostic Meta-Learning](https://mlsanity.com/paper/1806.03836/) (2018)
- [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018)
- [Neural Episodic Control](https://mlsanity.com/paper/1703.01988/) (2017)
- [Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation](https://mlsanity.com/paper/1910.13616/) (2019)
- [Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm](https://mlsanity.com/paper/1710.11622/) (2017)
- [Learning to Remember Rare Events](https://mlsanity.com/paper/1703.03129/) (2017)
- [The Omniglot challenge: a 3-year progress report](https://mlsanity.com/paper/1902.03477/) (2019)
- [Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences](https://mlsanity.com/paper/2005.02183/) (2020)
- [A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule](https://mlsanity.com/paper/1812.06574/) (2018)
- [Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks](https://mlsanity.com/paper/1804.00227/) (2018)
- [Been There, Done That: Meta-Learning with Episodic Recall](https://mlsanity.com/paper/1805.09692/) (2018)
- [STDP-based spiking deep convolutional neural networks for object recognition](https://mlsanity.com/paper/1611.01421/) (2016)
- [Meta-learning Spiking Neural Networks with Surrogate Gradient Descent](https://mlsanity.com/paper/2201.10777/) (2022)

## Built on by (most cited first, 2 of 2)

- [Domain Generalization through Meta-Learning: A Survey](https://mlsanity.com/paper/2404.02785/) (2024)
- [Accuracy-Efficiency Trade-Offs in Spiking Neural Networks: A Lempel-Ziv Complexity Perspective on Learning Rules](https://mlsanity.com/paper/2506.06750/) (2025)

## Abstract (arXiv)

The human brain constantly learns and rapidly adapts to new situations by integrating acquired knowledge and experiences into memory. Developing this capability in machine learning models is considered an important goal of AI research since deep neural networks perform poorly when there is limited data or when they need to adapt quickly to new unseen tasks. Meta-learning models are proposed to facilitate quick learning in low-data regimes by employing absorbed information from the past. Although some models have recently been introduced that reached high-performance levels, they are not biologically plausible. We have proposed a bio-plausible meta-learning model inspired by the hippocampus and the prefrontal cortex using spiking neural networks with a reward-based learning system. Our proposed model includes a memory designed to prevent catastrophic forgetting, a phenomenon that occurs when meta-learning models forget what they have learned as soon as the new task begins. Also, our new model can easily be applied to spike-based neuromorphic devices and enables fast learning in neuromorphic hardware. The final analysis will discuss the implications and predictions of the model for solving few-shot classification tasks. In solving these tasks, our model has demonstrated the ability to compete with the existing state-of-the-art meta-learning techniques.