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A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices

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arxiv 2303.10780 v2 pith:3TIZMMTA submitted 2023-03-19 cs.NE cs.LGeess.IV

classification cs.NEcs.LGeess.IV
keywords neuralnetworksspikingoptimizationefficiencyinterpretationnetworkreview
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Biological neural networks continue to inspire breakthroughs in neural network performance. And yet, one key area of neural computation that has been under-appreciated and under-investigated is biologically plausible, energy-efficient spiking neural networks, whose potential is especially attractive for low-power, mobile, or otherwise hardware-constrained settings. We present a literature review of recent developments in the interpretation, optimization, efficiency, and accuracy of spiking neural networks. Key contributions include identification, discussion, and comparison of cutting-edge methods in spiking neural network optimization, energy-efficiency, and evaluation, starting from first principles so as to be accessible to new practitioners.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks

    eess.SP 2026-07 conditional novelty 4.0 of 10

    On a shared 1D-CNN baseline for CHB-MIT seizure detection, INT8 quantization cut model size from 1.63 to 0.44 MB and latency by 2.8x with preserved AUC, while SNN conversion was 288x slower on CPU.

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