[en] 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.
Disciplines :
Computer science
Author, co-author :
Antonelo, Eric Aislan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Schrauwen, Benjamin
Stroobandt, Dirk
External co-authors :
yes
Language :
English
Title :
Unsupervised Learning in Reservoir Computing: Modeling Hippocampal Place Cells for Small Mobile Robots
Publication date :
2009
Event name :
19th International Conference on Artificial Neural Networks
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