# Spiking Neural Networks and Online Learning: An Overview and Perspectives

arXiv: https://arxiv.org/abs/1908.08019 · HTML: https://mlsanity.com/paper/1908.08019/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 30 of 41)

- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2305.06360/) (2023)
- [A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays](https://mlsanity.com/paper/2110.10486/) (2021)
- [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)
- [Exploring Temporal Information Dynamics in Spiking Neural Networks](https://mlsanity.com/paper/2211.14406/) (2022)
- [Online PAC-Bayes Learning](https://mlsanity.com/paper/2206.00024/) (2022)
- [SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition](https://mlsanity.com/paper/2308.04369/) (2023)
- [BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons](https://mlsanity.com/paper/2105.12917/) (2021)
- [Adaptive Online Incremental Learning for Evolving Data Streams](https://mlsanity.com/paper/2201.01633/) (2022)
- [Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review](https://mlsanity.com/paper/2205.01081/) (2022)
- [Learning from Data Streams: An Overview and Update](https://mlsanity.com/paper/2212.14720/) (2022)
- [Realtime Facial Expression Recognition: Neuromorphic Hardware vs. Edge AI Accelerators](https://mlsanity.com/paper/2403.08792/) (2024)
- [BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks](https://mlsanity.com/paper/2111.13188/) (2021)
- [Spike-based computational models of bio-inspired memories in the hippocampal CA3 region on SpiNNaker](https://mlsanity.com/paper/2205.04782/) (2022)
- [A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model](https://mlsanity.com/paper/2206.04924/) (2022)
- [A survey on learning models of spiking neural membrane systems and spiking neural networks](https://mlsanity.com/paper/2403.18609/) (2024)
- [Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing](https://mlsanity.com/paper/2407.06452/) (2024)
- [Towards Efficient Deployment of Hybrid SNNs on Neuromorphic and Edge AI Hardware](https://mlsanity.com/paper/2407.08704/) (2024)
- [AT-SNN: Adaptive Tokens for Vision Transformer on Spiking Neural Network](https://mlsanity.com/paper/2408.12293/) (2024)
- [Incremental Online Learning of Randomized Neural Network with Forward Regularization](https://mlsanity.com/paper/2412.13096/) (2024)
- [Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks](https://mlsanity.com/paper/2201.07602/) (2022)
- [Cryogenic Neuromorphic Hardware](https://mlsanity.com/paper/2204.07503/) (2022)
- [A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks](https://mlsanity.com/paper/2210.00894/) (2022)
- [Bio-inspired spike-based Hippocampus and Posterior Parietal Cortex models for robot navigation and environment pseudo-mapping](https://mlsanity.com/paper/2305.12892/) (2023)
- [Enhancing Energy Efficiency and Reliability in Autonomous Systems Estimation using Neuromorphic Approach](https://mlsanity.com/paper/2307.07963/) (2023)
- [When In-memory Computing Meets Spiking Neural Networks -- A Perspective on Device-Circuit-System-and-Algorithm Co-design](https://mlsanity.com/paper/2408.12767/) (2024)
- [Structuring the Processing Frameworks for Data Stream Evaluation and Application](https://mlsanity.com/paper/2411.06799/) (2024)
- [U-Net-Like Spiking Neural Networks for Single Image Dehazing](https://mlsanity.com/paper/2512.23950/) (2025)
- [Astrocytes mediate analogous memory in a multi-layer neuron-astrocytic network](https://mlsanity.com/paper/2108.13414/) (2021)
- [Online Deep Learning based on Auto-Encoder](https://mlsanity.com/paper/2201.07383/) (2022)

## Abstract (arXiv)

Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These conditions usually impose memory and processing time restrictions, and they often turn into evolving environments where a change may affect the input data distribution. Such a change causes that predictive models trained over these stream data become obsolete and do not adapt suitably to new distributions. Specially in these non-stationary scenarios, there is a pressing need for new algorithms that adapt to these changes as fast as possible, while maintaining good performance scores. Unfortunately, most off-the-shelf classification models need to be retrained if they are used in changing environments, and fail to scale properly. Spiking Neural Networks have revealed themselves as one of the most successful approaches to model the behavior and learning potential of the brain, and exploit them to undertake practical online learning tasks. Besides, some specific flavors of Spiking Neural Networks can overcome the necessity of retraining after a drift occurs. This work intends to merge both fields by serving as a comprehensive overview, motivating further developments that embrace Spiking Neural Networks for online learning scenarios, and being a friendly entry point for non-experts.