A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy

Int J Neural Syst. 2017 May;27(3):1750002. doi: 10.1142/S0129065717500022. Epub 2016 Sep 1.


This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Keywords: Supervised learning; cross-correlated term; delay learning; interictal spike detection; spiking neurons.

Publication types

  • Validation Study

MeSH terms

  • Action Potentials
  • Cerebral Cortex / physiopathology
  • Epilepsy / physiopathology*
  • Humans
  • Neural Inhibition / physiology
  • Neural Networks, Computer*
  • Neurons / physiology*
  • Supervised Machine Learning*
  • Synapses / physiology
  • Time Factors
  • Wavelet Analysis