  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.
