让神经网络同时学习突触强度和延迟，提升类脑计算性能。
Extending Spike-Timing Dependent Plasticity to Learning Synaptic Delays
- 扩展STDP规则，实现权重与延迟同步学习
- 在多种测试中表现优于现有方法，准确率显著提升
- 揭示突触效能与延迟的协同作用机制，适合类脑计算研究者
突触延迟在生物神经网络中起关键作用，其调节已在哺乳动物学习过程中被观察到。尽管脉冲神经网络（SNNs）旨在比传统人工神经网络更贴近生物机制，但其仿真中很少考虑突触延迟。本文提出一种新学习规则，通过扩展常见的赫布型学习方法——尖峰时序依赖可塑性（STDP），实现突触连接强度与延迟的联合学习。我们在一个广泛使用的无监督学习分类SNN模型上验证该方法，并与现有联合学习权重与延迟的方法，以及不包含延迟的STDP进行对比。结果表明，所提方法在多种测试场景下均持续取得更优性能。此外，实验还揭示了突触效能与延迟之间的相互作用规律。
原文摘要 · Abstract (English)
Synaptic delays play a crucial role in biological neuronal networks, where their modulation has been observed in mammalian learning processes. In the realm of neuromorphic computing, although spiking neural networks (SNNs) aim to emulate biology more closely than traditional artificial neural networks do, synaptic delays are rarely incorporated into their simulation. We introduce a novel learning rule for simultaneously learning synaptic connection strengths and delays, by extending spike-timing dependent plasticity (STDP), a Hebbian method commonly used for learning synaptic weights. We validate our approach by extending a widely-used SNN model for classification trained with unsupervised learning. Then we demonstrate the effectiveness of our new method by comparing it against another existing methods for co-learning synaptic weights and delays as well as against STDP without synaptic delays. Results demonstrate that our proposed method consistently achieves superior performance across a variety of test scenarios. Furthermore, our experimental results yield insight into the interplay between synaptic efficacy and delay.
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