# Low-activity supervised convolutional spiking neural networks applied to speech commands recognition

arXiv: https://arxiv.org/abs/2011.06846 · HTML: https://mlsanity.com/paper/2011.06846/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 19 of 19)

- [Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks](https://mlsanity.com/paper/2103.12593/) (2021)
- [Object Detection with Spiking Neural Networks on Automotive Event Data](https://mlsanity.com/paper/2205.04339/) (2022)
- [AutoSNN: Towards Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2201.12738/) (2022)
- [Direct Learning-Based Deep Spiking Neural Networks: A Review](https://mlsanity.com/paper/2305.19725/) (2023)
- [Learning from Event Cameras with Sparse Spiking Convolutional Neural Networks](https://mlsanity.com/paper/2104.12579/) (2021)
- [StereoSpike: Depth Learning with a Spiking Neural Network](https://mlsanity.com/paper/2109.13751/) (2021)
- [Spiking Neural Networks for Frame-based and Event-based Single Object Localization](https://mlsanity.com/paper/2206.06506/) (2022)
- [A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks](https://mlsanity.com/paper/2206.06129/) (2022)
- [Single Channel Speech Enhancement Using U-Net Spiking Neural Networks](https://mlsanity.com/paper/2307.14464/) (2023)
- [Optimizing the Consumption of Spiking Neural Networks with Activity Regularization](https://mlsanity.com/paper/2204.01460/) (2022)
- [Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting](https://mlsanity.com/paper/2406.13179/) (2024)
- [Sparse-firing regularization methods for spiking neural networks with time-to-first spike coding](https://mlsanity.com/paper/2307.13007/) (2023)
- [Encrypted Internet traffic classification using a supervised Spiking Neural Network](https://mlsanity.com/paper/2101.09818/) (2021)
- [Spiking neural networks trained via proxy](https://mlsanity.com/paper/2109.13208/) (2021)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [Spiking Synaptic Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2302.01500/) (2023)
- [On Reducing Activity with Distillation and Regularization for Energy Efficient Spiking Neural Networks](https://mlsanity.com/paper/2406.18350/) (2024)
- [Deep activity propagation via weight initialization in spiking neural networks](https://mlsanity.com/paper/2410.00580/) (2024)
- [SpikeLite: Lightweight Spiking Neural Networks for Time-Series Forecasting](https://mlsanity.com/paper/2609.35097/) (2026)

## Abstract (arXiv)

Deep Neural Networks (DNNs) are the current state-of-the-art models in many speech related tasks. There is a growing interest, though, for more biologically realistic, hardware friendly and energy efficient models, named Spiking Neural Networks (SNNs). Recently, it has been shown that SNNs can be trained efficiently, in a supervised manner, using backpropagation with a surrogate gradient trick. In this work, we report speech command (SC) recognition experiments using supervised SNNs. We explored the Leaky-Integrate-Fire (LIF) neuron model for this task, and show that a model comprised of stacked dilated convolution spiking layers can reach an error rate very close to standard DNNs on the Google SC v1 dataset: 5.5%, while keeping a very sparse spiking activity, below 5%, thank to a new regularization term. We also show that modeling the leakage of the neuron membrane potential is useful, since the LIF model outperformed its non-leaky model counterpart significantly.