Direct Learning-Based Deep Spiking Neural Networks: A Review

2023/05/31 by Yufei Guo, Guo, Yufei, Xuhui Huang +3 · 9 citations
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Neural Networks and Reservoir Computing #Neural dynamics and brain function

paper · pdf · doi:10.48550/arxiv.2305.19725

openalex publication_date 2023/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Sparse by Rule: Probability-Based N:M Pruning for Spiking Neural Networks
• Stabilizing Direct Training of Spiking Neural Networks: Membrane Potential Initialization and Threshold-robust Surrogate Gradient
• SPACT18: Spiking Human Action Recognition Benchmark Dataset with Complementary RGB and Thermal Modalities
• Optimization of Low-Latency Spiking Neural Networks Utilizing Historical Dynamics of Refractory Periods
• ReverB-SNN: Reversing Bit of the Weight and Activation for Spiking Neural Networks
• Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time
• Energy efficiency analysis of Spiking Neural Networks for space applications
• PASCAL: Precise and Efficient ANN- SNN Conversion using Spike Accumulation and Adaptive Layerwise Activation
• Revisiting the Haken Lighthouse Model

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