A novel learning rule, Cross-Correlated Delay Shift (CCDS) learning algorithm, is proposed for processing spatiotemporal patterns in this study. CCDS is a supervised learning rule that is able to learn association of arbitrary spike trains in a supervised fashion. Single spiking neuron trained according to CCDS algorithm is capable of learning and precisely reproducing arbitrary target sequences of spikes. Unlike the ReSuMe learning rule, synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. Besides biological plausibility, CCDS is also computationally efficient. In the presented experimental analysis, the proposed learning algorithm is evaluated by it properties including its robustness in dealing with noisy environment, and its adaptive learning performance to different spatio-temporal patterns. Simulation results have shown that the proposed CCDS learning method achieves learning accuracy and learning speed improvements comparable to ReSuMe.
