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A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices
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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
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Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks
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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