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

on Thu, 09/29/2016 - 19:51
TitleUnsupervised Learning in Reservoir Computing: Modeling Hippocampal Place Cells for Small Mobile Robots
Publication TypeConference Paper
Year of Publication2009
AuthorsAntonelo EA, Schrauwen B, Stroobandt D
Conference Name{ICANN '09: Proceedings of the 19th International Conference on Artificial Neural Networks}
PublisherSpringer-Verlag
Conference LocationBerlin, Heidelberg
Abstract

Biological systems (e.g., rats) have efficient and robust local- ization 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 Com- puting (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 ar- chitecture, forming a spatial representation which is dependent on the robot direction.

DOI10.1007/978-3-642-04274-4_77