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An Online Supervised Learning Algorithm Based on Nonlinear Spike Train Kernels

  • Xianghong Lin
  • Ning Zhang
  • Xiangwen Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9225)

Abstract

The online learning algorithm is shown to be more appropriate and effective for the processing of spatiotemporal information, but very little researches have been achieved in developing online learning approaches for spiking neural networks. This paper presents an online supervised learning algorithm based on nonlinear spike train kernels to process the spatiotemporal information, which is more biological interpretability. The main idea adopts online learning algorithm and selects a suitable kernel function. At first, the Laplacian kernel function is selected, however, in some ways, the spike trains expressed by the simple kernel function are linear in the postsynaptic neuron. Then this paper uses nonlinear functions to transform the spike train model and presents the detail experimental analysis. The proposed learning algorithm is evaluated by the learning of spike trains, and the experimental results show that the online nonlinear spike train kernels own a super-duper learning effect.

Keywords

Spiking neural networks Supervised learning Spike train kernels Online learning 

Notes

Acknowledgement

The work is supported by the National Natural Science Foundation of China under Grants No. 61165002 and No. 61363059.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNorthwest Normal UniversityLanzhouChina

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