mlsanityData to 14 Oct 2026

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

arXiv 2403.18609 ↗First posted 27 Mar 2024cs.NE cs.CLcited by 2

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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.

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

19 of 148 references.

References · 19

  1. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 406
  2. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
  3. 2018Deep Learning in Spiking Neural Networkscited by 263
  4. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 259
  5. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 177
  6. 2020RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Networkcited by 145
  7. 2022Attention Spiking Neural Networkscited by 107
  8. 2019S4NN: temporal backpropagation for spiking neural networks with one spike per neuroncited by 49
  9. 2019Spiking Neural Networks and Online Learning: An Overview and Perspectivescited by 41
  10. 2022AutoSNN: Towards Energy-Efficient Spiking Neural Networkscited by 40
  11. 2020Exploring the Connection Between Binary and Spiking Neural Networkscited by 38
  12. 2018A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rulecited by 36
7 more
  1. 2021SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environmentscited by 29
  2. 2017BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticitycited by 23
  3. 2019Locally Connected Spiking Neural Networks for Unsupervised Feature Learningcited by 9
  4. 2021Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsulescited by 6
  5. 2022An STDP-Based Supervised Learning Algorithm for Spiking Neural Networkscited by 3
  6. 2021A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-based Manipulation Taskscited by 2
  7. 2021BioLCNet: Reward-modulated Locally Connected Spiking Neural Networkscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Modality-Dependent Memory Mechanisms in Cross-Modal Neuromorphic Computingcited by 0
  2. 2026Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Visioncited by 0