# A survey on learning models of spiking neural membrane systems and spiking neural networks

arXiv: https://arxiv.org/abs/2403.18609 · HTML: https://mlsanity.com/paper/2403.18609/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 148 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2003.01811/) (2020)
- [Attention Spiking Neural Networks](https://mlsanity.com/paper/2209.13929/) (2022)
- [S4NN: temporal backpropagation for spiking neural networks with one spike per neuron](https://mlsanity.com/paper/1910.09495/) (2019)
- [Spiking Neural Networks and Online Learning: An Overview and Perspectives](https://mlsanity.com/paper/1908.08019/) (2019)
- [AutoSNN: Towards Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2201.12738/) (2022)
- [Exploring the Connection Between Binary and Spiking Neural Networks](https://mlsanity.com/paper/2002.10064/) (2020)
- [A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule](https://mlsanity.com/paper/1812.06574/) (2018)
- [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)
- [BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity](https://mlsanity.com/paper/1711.04214/) (2017)
- [Locally Connected Spiking Neural Networks for Unsupervised Feature Learning](https://mlsanity.com/paper/1904.06269/) (2019)
- [Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsules](https://mlsanity.com/paper/2111.07785/) (2021)
- [An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks](https://mlsanity.com/paper/2203.03379/) (2022)
- [A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-based Manipulation Tasks](https://mlsanity.com/paper/2102.01966/) (2021)
- [BioLCNet: Reward-modulated Locally Connected Spiking Neural Networks](https://mlsanity.com/paper/2109.05539/) (2021)

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

- [Modality-Dependent Memory Mechanisms in Cross-Modal Neuromorphic Computing](https://mlsanity.com/paper/2512.18575/) (2025)
- [Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision](https://mlsanity.com/paper/2602.23514/) (2026)

## Abstract (arXiv)

Spiking neural networks (SNN) are a biologically inspired model of neural networks with certain brain-like properties. In the past few decades, this model has received increasing attention in computer science community, owing also to the successful phenomenon of deep learning. In SNN, communication between neurons takes place through the spikes and spike trains. This differentiates these models from the ``standard'' artificial neural networks (ANN) where the frequency of spikes is replaced by real-valued signals. Spiking neural P systems (SNPS) can be considered a branch of SNN based more on the principles of formal automata, with many variants developed within the framework of the membrane computing theory. In this paper, we first briefly compare structure and function, advantages and drawbacks of SNN and SNPS. A key part of the article is a survey of recent results and applications of machine learning and deep learning models of both SNN and SNPS formalisms.