# ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks

arXiv: https://arxiv.org/abs/1703.07655 · HTML: https://mlsanity.com/paper/1703.07655/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2017.

## Built on by (most cited first, 16 of 16)

- [SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments](https://mlsanity.com/paper/2103.00424/) (2021)
- [lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents](https://mlsanity.com/paper/2205.12295/) (2022)
- [TopSpark: A Timestep Optimization Methodology for Energy-Efficient Spiking Neural Networks on Autonomous Mobile Agents](https://mlsanity.com/paper/2303.01826/) (2023)
- [Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networks](https://mlsanity.com/paper/2105.14677/) (2021)
- [Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks](https://mlsanity.com/paper/2402.11984/) (2024)
- [Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks](https://mlsanity.com/paper/2309.09550/) (2023)
- [Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks](https://mlsanity.com/paper/2212.12620/) (2022)
- [tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2206.08656/) (2022)
- [Replay4NCL: An Efficient Memory Replay-based Methodology for Neuromorphic Continual Learning in Embedded AI Systems](https://mlsanity.com/paper/2503.17061/) (2025)
- [Machine Unlearning using Forgetting Neural Networks](https://mlsanity.com/paper/2410.22374/) (2024)
- [Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning](https://mlsanity.com/paper/2508.04610/) (2025)
- [TACOS: Task Agnostic Continual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2409.00021/) (2024)
- [Similarity-based context aware continual learning for spiking neural networks](https://mlsanity.com/paper/2411.05802/) (2024)
- [Temporal Regularization Training: Unleashing the Potential of Spiking Neural Networks](https://mlsanity.com/paper/2506.19256/) (2025)
- [CLANE: Continual Learning of Actions on Neuromorphic Hardware from Event Cameras](https://mlsanity.com/paper/2605.28387/) (2026)
- [Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks](https://mlsanity.com/paper/2610.17998/) (2026)

## Abstract (arXiv)

A fundamental feature of learning in animals is the "ability to forget" that allows an organism to perceive, model and make decisions from disparate streams of information and adapt to changing environments. Against this backdrop, we present a novel unsupervised learning mechanism ASP (Adaptive Synaptic Plasticity) for improved recognition with Spiking Neural Networks (SNNs) for real time on-line learning in a dynamic environment. We incorporate an adaptive weight decay mechanism with the traditional Spike Timing Dependent Plasticity (STDP) learning to model adaptivity in SNNs. The leak rate of the synaptic weights is modulated based on the temporal correlation between the spiking patterns of the pre- and post-synaptic neurons. This mechanism helps in gradual forgetting of insignificant data while retaining significant, yet old, information. ASP, thus, maintains a balance between forgetting and immediate learning to construct a stable-plastic self-adaptive SNN for continuously changing inputs. We demonstrate that the proposed learning methodology addresses catastrophic forgetting while yielding significantly improved accuracy over the conventional STDP learning method for digit recognition applications. Additionally, we observe that the proposed learning model automatically encodes selective attention towards relevant features in the input data while eliminating the influence of background noise (or denoising) further improving the robustness of the ASP learning.