Sparsifying Spiking Networks through Local Rhythms
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Abstract (arXiv)
It has been well-established that within conventional neural networks, many of the values produced at each layer are zero. In this work, I demonstrate that spiking neural networks can prevent the transmission of spikes representing values close to zero using local information. This can reduce the amount of energy required for communication and computation in these networks while preserving accuracy. Additionally, this demonstrates a novel application of biologically observed spiking rhythms.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 19 references that are in the map. First posted on arXiv 30 Apr 2023.
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References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
- 2019The State of Sparsity in Deep Neural Networkscited by 476
- 2022The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformerscited by 122
- 2023SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Modelscited by 28
- 2021Deep Phasor Networks: Connecting Conventional and Spiking Neural Networkscited by 4
- 2022Deep Learning in Spiking Phasor Neural Networkscited by 3
- 2022Residual and Attentional Architectures for Vector-Symbolscited by 1
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