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Quantization in Spiking Neural Networks

arXiv 2305.08012 ↗First posted 13 May 2023cs.NE cs.DM cs.ETcited by 0

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Abstract (arXiv)

In spiking neural networks (SNN), at each node, an incoming sequence of weighted Dirac pulses is converted into an output sequence of weighted Dirac pulses by a leaky-integrate-and-fire (LIF) neuron model based on spike aggregation and thresholding. We show that this mapping can be understood as a quantization operator and state a corresponding formula for the quantization error by means of the Alexiewicz norm. This analysis has implications for rethinking re-initialization in the LIF model, leading to the proposal of 'reset-to-mod' as a modulo-based reset variant.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 10 references that are in the map. First posted on arXiv 13 May 2023.

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3 of 10 references.

References · 3

  1. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 259
  2. 2018On Quasi-Isometry of Threshold-Based Samplingcited by 2
  3. 2023Spiking Neural Networks in the Alexiewicz Topology: A New Perspective on Analysis and Error Boundscited by 2

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  1. 2024On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals
  2. 2021Q-SpiNN: A Framework for Quantizing Spiking Neural Networks
  3. 2026Quantization of Spiking Neural Networks Beyond Accuracy
  4. 2022The fine line between dead neurons and sparsity in binarized spiking neural networks
  5. 2026Integer-State Dynamics of Quantized Spiking Neural Networks for Efficient Hardware Acceleration
  6. 2025Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping
  7. 2023Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN
  8. 2024Q-SNNs: Quantized Spiking Neural Networks