ArticleFrontiers in neuroscience2022
A surrogate gradient spiking baseline for speech command recognition.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A Lightweight Keyword Spotting Method Using a Convolutional Spiking Neural Network with Learnable Synaptic Delays.Sensors (Basel, Switzerland) · 2026Article
- Model-agnostic linear-memory online learning in spiking neural networks.Nature communications · 2026Article
- Compute-in-memory implementation of state space models for event sequence processing.Nature communications · 2026Article
- Multi-plasticity synergy with adaptive mechanism assignment for training spiking neural networks.Frontiers in neuroscience · 2026Article
- Three factor delay learning rules for spiking neural networks.Frontiers in neuroscience · 2026Article
- Conductance-based reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance.Frontiers in computational neuroscience · 2026Article
- Efficient event-based delay learning in spiking neural networks.Nature communications · 2025Article
- DelGrad: exact event-based gradients for training delays and weights on spiking neuromorphic hardware.Nature communications · 2025Article
- Biologically-informed excitatory and inhibitory ratio for robust spiking neural network training.Scientific reports · 2025Article
- Advancing spatio-temporal processing through adaptation in spiking neural networks.Nature communications · 2025Article
- The neurobench framework for benchmarking neuromorphic computing algorithms and systems.Nature communications · 2025Review
- Bridging neuromorphic computing and deep learning for next-generation neural data interpretation.Frontiers in computational neuroscience · 2025Article
- Spiking neural networks for physiological and speech signals: a review.Biomedical engineering letters · 2024Review
- DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays.Nature communications · 2024Article
- Co-learning synaptic delays, weights and adaptation in spiking neural networks.Frontiers in neuroscience · 2024Article
- Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks.Frontiers in neuroscience · 2024Article
- Scalable network emulation on analog neuromorphic hardware.Frontiers in neuroscience · 2024Article
- Direct learning-based deep spiking neural networks: a review.Frontiers in neuroscience · 2023Review
- Learnable axonal delay in spiking neural networks improves spoken word recognition.Frontiers in neuroscience · 2023Article
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2 authors.
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Abstract
Artificial neural networks (ANNs) are the basis of recent advances in artificial intelligence (AI); they typically use real valued neuron responses. By contrast, biological neurons are known to operate using spike trains. In principle, spiking neural networks (SNNs) may have a greater representational capability than ANNs, especially for time series such as speech; however their adoption has been held back by both a lack of stable training algorithms and a lack of compatible baselines. We begin with a fairly thorough review of literature around the conjunction of ANNs and SNNs. Focusing on surrogate gradient approaches, we proceed to define a simple but relevant evaluation based on recent speech command tasks. After evaluating a representative selection of architectures, we show that a combination of adaptation, recurrence and surrogate gradients can yield light spiking architectures that are not only able to compete with ANN solutions, but also retain a high degree of compatibility with them in modern deep learning frameworks. We conclude tangibly that SNNs are appropriate for future research in AI, in particular for speech processing applications, and more speculatively that they may also assist in inference about biological function.
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