ReviewSensors (Basel, Switzerland)2023
Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Recovery from disorders of consciousness: Lesions and GABAergic modulation in a biologically inspired spiking neural network.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- AHCSIS: adaptive evaluation model for stereoscopic visual comfort based on HHT-CSP-SSA and improved SNN.Frontiers in neuroscience · 2026Article
- Development of digital hardware for a spiking image recognition network employing a novel burst-based reinforcement learning approach.Scientific reports · 2025Article
- Developing a brain inspired multilobar neural networks architecture for rapidly and accurately estimating concrete compressive strength.Scientific reports · 2025Article
- A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Brain-inspired biomimetic robot control: a review.Frontiers in neurorobotics · 2024Review
- Article
- The computational power of the human brain.Frontiers in cellular neuroscience · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Spiking neural networks (SNNs) are subjects of a topic that is gaining more and more interest nowadays. They more closely resemble actual neural networks in the brain than their second-generation counterparts, artificial neural networks (ANNs). SNNs have the potential to be more energy efficient than ANNs on event-driven neuromorphic hardware. This can yield drastic maintenance cost reduction for neural network models, as the energy consumption would be much lower in comparison to regular deep learning models hosted in the cloud today. However, such hardware is still not yet widely available. On standard computer architectures consisting mainly of central processing units (CPUs) and graphics processing units (GPUs) ANNs, due to simpler models of neurons and simpler models of connections between neurons, have the upper hand in terms of execution speed. In general, they also win in terms of learning algorithms, as SNNs do not reach the same levels of performance as their second-generation counterparts in typical machine learning benchmark tasks, such as classification. In this paper, we review existing learning algorithms for spiking neural networks, divide them into categories by type, and assess their computational complexity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.