ArticleFrontiers in neuroscience2019
A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Algorithm-hardware co-design of neuromorphic networks with dual memory pathways.Nature machine intelligence · 2026Article
- Three factor delay learning rules for spiking neural networks.Frontiers in neuroscience · 2026Article
- Efficient event-based delay learning in spiking neural networks.Nature communications · 2025Article
- Co-learning synaptic delays, weights and adaptation in spiking neural networks.Frontiers in neuroscience · 2024Article
- Learning delays through gradients and structure: emergence of spatiotemporal patterns in spiking neural networks.Frontiers in computational neuroscience · 2024Article
- Learnable axonal delay in spiking neural networks improves spoken word recognition.Frontiers in neuroscience · 2023Article
- Precise Spiking Motifs in Neurobiological and Neuromorphic Data.Brain sciences · 2022Review
- A surrogate gradient spiking baseline for speech command recognition.Frontiers in neuroscience · 2022Article
- Article
- Supervised Learning Algorithm for Multilayer Spiking Neural Networks with Long-Term Memory Spike Response Model.Computational intelligence and neuroscience · 2021Article
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3 authors.
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Abstract
Neuroscience research confirms that the synaptic delays are not constant, but can be modulated. This paper proposes a supervised delay learning algorithm for spiking neurons with temporal encoding, in which both the weight and delay of a synaptic connection can be adjusted to enhance the learning performance. The proposed algorithm firstly defines spike train kernels to transform discrete spike trains during the learning phase into continuous analog signals so that common mathematical operations can be performed on them, and then deduces the supervised learning rules of synaptic weights and delays by gradient descent method. The proposed algorithm is successfully applied to various spike train learning tasks, and the effects of parameters of synaptic delays are analyzed in detail. Experimental results show that the network with dynamic delays achieves higher learning accuracy and less learning epochs than the network with static delays. The delay learning algorithm is further validated on a practical example of an image classification problem. The results again show that it can achieve a good classification performance with a proper receptive field. Therefore, the synaptic delay learning is significant for practical applications and theoretical researches of spiking neural networks.
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