ReviewBrain sciences2022
Spiking Neural Networks and Their Applications: A Review.
Review in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.
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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
89 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- The Difference Neuron-a new spiking neuron model.Biological cybernetics · 2026Article
- Effects of Gamma Irradiation on the Artificial Spiking Neurons Based on NbOSmall (Weinheim an der Bergstrasse, Germany) · 2026Article
- Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets Through Dynamic Dense Scattering Media.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Concept2Brain: an AI model for predicting neurophysiological responses to text and pictures.Nature communications · 2026Article
- DCPA-SNN, Direct-Coding-Physics-Aware Spiking Neural Network: A Framework for Wearable ECG Denoising Under Dynamic-Noise Conditions.Sensors (Basel, Switzerland) · 2026Article
- Dynamic performance enhancement of adjustable blade pitch angle for wind generation system applications based on artificial neural network control techniques.Scientific reports · 2026Article
- The Human Mind in Dialogue With its Replica AI - a Historical Perspective.Integrative psychological & behavioral science · 2026Article
- A Wilson-Cowan reservoir computer for interpretable spatiotemporal vision.Scientific reports · 2026Article
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Zone of inhibition prediction in silver nanoparticle antimicrobial activity via physics-guided liquid state machine.Biomaterials and biosystems · 2026Article
- A spiking neural network model for fractional proprioceptive encoding of limb posture and movement in insects.Biological cybernetics · 2026Article
- Eye Movement Classification Using Neuromorphic Vision Sensors.Journal of eye movement research · 2026Article
- Model-agnostic linear-memory online learning in spiking neural networks.Nature communications · 2026Article
- Encoding of movement primitives and body posture through distributed proprioception in walking and climbing insects.Biological cybernetics · 2026Article
- Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks.Frontiers in neuroscience · 2026Article
- Spiking neurons-based facial emotion recognition: a comparative analysis of leaky and a quadratic integrated neuron for the unprocessed dataset.Frontiers in computational neuroscience · 2026Article
- Spike-based Q-learning in a non-von Neumann architecture.Frontiers in neuroscience · 2026Article
- Self-adaptive forward-forward network for anomaly detection and medical image analysis.Frontiers in radiology · 2026Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The past decade has witnessed the great success of deep neural networks in various domains. However, deep neural networks are very resource-intensive in terms of energy consumption, data requirements, and high computational costs. With the recent increasing need for the autonomy of machines in the real world, e.g., self-driving vehicles, drones, and collaborative robots, exploitation of deep neural networks in those applications has been actively investigated. In those applications, energy and computational efficiencies are especially important because of the need for real-time responses and the limited energy supply. A promising solution to these previously infeasible applications has recently been given by biologically plausible spiking neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically realistic models of neurons to carry out the computation. Due to their functional similarity to the biological neural network, spiking neural networks can embrace the sparsity found in biology and are highly compatible with temporal code. Our contributions in this work are: (i) we give a comprehensive review of theories of biological neurons; (ii) we present various existing spike-based neuron models, which have been studied in neuroscience; (iii) we detail synapse models; (iv) we provide a review of artificial neural networks; (v) we provide detailed guidance on how to train spike-based neuron models; (vi) we revise available spike-based neuron frameworks that have been developed to support implementing spiking neural networks; (vii) finally, we cover existing spiking neural network applications in computer vision and robotics domains. The paper concludes with discussions of future perspectives.
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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.