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Unsupervised Learning in Reservoir Computing: Modeling Hippocampal Place Cells for Small Mobile Robots

  • Eric A. Antonelo
  • Benjamin Schrauwen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

Abstract

Biological systems (e.g., rats) have efficient and robust localization abilities provided by the so called, place cells, which are found in the hippocampus of rodents and primates (these cells encode locations of the animal’s environment). This work seeks to model these place cells by employing three (biologically plausible) techniques: Reservoir Computing (RC), Slow Feature Analysis (SFA), and Independent Component Analysis (ICA). The proposed architecture is composed of three layers, where the bottom layer is a dynamic reservoir of recurrent nodes with fixed weights. The upper layers (SFA and ICA) provides a self-organized formation of place cells, learned in an unsupervised way. Experiments show that a simulated mobile robot with 17 noisy short-range distance sensors is able to self-localize in its environment with the proposed architecture, forming a spatial representation which is dependent on the robot direction.

Keywords

Reservoir computing slow feature analysis place cells 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Eric A. Antonelo
    • 1
  • Benjamin Schrauwen
    • 1
  1. 1.Electronics and Information Systems DepartmentGhent UniversityGhentBelgium

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