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The plasticity of the conduction delay between neurons plays a fundamentalrole in learning. However, the exact underlying mechanisms in the brain forthis modulation is still an open problem. Understanding the precise adjustmentof synaptic delays could help us in developing effective brain-inspiredcomputational models in providing aligned insights with the experimentalevidence. In this paper, we propose an unsupervised biologically plausiblelearning rule for adjusting the synaptic delays in spiking neural networks.Then, we provided some mathematical proofs to show that our learning rule givesa neuron the ability to learn repeating spatio-temporal patterns. Furthermore,the experimental results of applying an STDP-based spiking neural networkequipped with our proposed delay learning rule on Random Dot Kinematogramindicate the efficacy of the proposed delay learning rule in extractingtemporal features.

Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks