论文标题
在峰值神经网络中提取时间特征的生物可行的无监督延迟学习
Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks
论文作者
论文摘要
神经元之间传导延迟的可塑性在学习中起着基本作用。但是，该调制的大脑中确切的潜在机制仍然是一个开放的问题。了解突触延迟的精确调整可以帮助我们开发有效的大脑启发的计算模型，以提供与实验证据的一致见解。在本文中，我们提出了一个无监督的生物学上合理的学习规则，以调整尖峰神经网络中的突触延迟。然后，我们提供了一些数学证明，以表明我们的学习规则使神经元具有学习重复时空模式的能力。此外，在随机DOT运动学图上应用了基于STDP的尖峰神经网络的实验结果表明，拟议的延迟学习规则在提取时间特征方面的功效。
The plasticity of the conduction delay between neurons plays a fundamental role in learning. However, the exact underlying mechanisms in the brain for this modulation is still an open problem. Understanding the precise adjustment of synaptic delays could help us in developing effective brain-inspired computational models in providing aligned insights with the experimental evidence. In this paper, we propose an unsupervised biologically plausible learning rule for adjusting the synaptic delays in spiking neural networks. Then, we provided some mathematical proofs to show that our learning rule gives a neuron the ability to learn repeating spatio-temporal patterns. Furthermore, the experimental results of applying an STDP-based spiking neural network equipped with our proposed delay learning rule on Random Dot Kinematogram indicate the efficacy of the proposed delay learning rule in extracting temporal features.
