ReviewFrontiers in neuroscience2023
Direct learning-based deep spiking neural networks: a review.
Review in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A Fire Detection Method Based on a Mind-Linked Continuous-Coupled Neural Network.Biomimetics (Basel, Switzerland) · 2026Article
- Exploring the spiking neural autoencoder: from hyperexcitability to noise-driven compensation.Frontiers in systems neuroscience · 2026Article
- Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks.Frontiers in neuroscience · 2026Article
- Bridging neuromorphic computing and deep learning for next-generation neural data interpretation.Frontiers in computational neuroscience · 2025Article
- Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding.Frontiers in neuroscience · 2025Article
- SpikeAtConv: an integrated spiking-convolutional attention architecture for energy-efficient neuromorphic vision processing.Frontiers in neuroscience · 2025Article
- Efficient spiking convolutional neural networks accelerator with multi-structure compatibility.Frontiers in neuroscience · 2025Article
- Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review.Nanomaterials (Basel, Switzerland) · 2024Review
- BayesianSpikeFusion: accelerating spiking neural network inference via Bayesian fusion of early prediction.Frontiers in neuroscience · 2024Article
- Learnable Leakage and Onset-Spiking Self-Attention in SNNs with Local Error Signals.Sensors (Basel, Switzerland) · 2023Article
- Direct training high-performance spiking neural networks for object recognition and detection.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected.
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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.