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Evolutionary Optimization of Liquid State Machines for Robust Learning

  • Yan Zhou
  • Yaochu JinEmail author
  • Jinliang Ding
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)

Abstract

Liquid State Machines (LSMs) are a computational model of spiking neural networks with recurrent connections in a reservoir. Although they are believed to be biologically more plausible, LSMs have not yet been as successful as other artificial neural networks in solving real world learning problems mainly due to their highly sensitive learning performance to different types of stimuli. To address this issue, a covariance matrix adaptation evolution strategy has been adopted in this paper to optimize the topology and parameters of the LSM, thereby sparing the arduous task of fine tuning the parameters of the LSM for different tasks. The performance of the evolved LSM is demonstrated on three complex real-world pattern classification problems including image recognition and spatio-temporal classification.

Keywords

Liquid State Machine Evolution strategy CMA-ES Pattern recognition 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Synthetical Automation for Process IndustryNortheastern UniversityShenyangChina
  2. 2.Department of Computer ScienceUniversity of SurreyGuildfordUK

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