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‘Learning place cell responses fromomni-directional camera footage’
The Khepera robot as a rodent model
Thomas Walther, Intitut für Neuroinformatik (Computational Neuroscience)
Motivation
● Rodent navigates the Morris-Water-Maze
RatLandmark
Camera
Platform (submerged, invisible for the rodent)
Insertion points
N
EW
S
150 cm
Inspired by: https://de.wikipedia.org/wiki/Morris-Wasserlabyrinth#/media/File:MorrisWaterMaze.svg (by Samuel John)
Water basin
Approach
SFA via MDP
Navigationcommands
Visual data
SFA
fe
atu
res
RL methods
Act
ua
tor
con
tro
l
(generated from a rectangular maze!)
Background
● 2 Khepera IV robots are available
● 2 omnicams (‘Theta V’) used for image capturing
● Slow feature analysis scheme is provided by the ‘Modular Toolkit for Data Processing’ (MDP)
● Algorithms are already implemented in a simulated environment (‘Blender’ framework)
Goals
● Build a generic interface that controls the Khepera’s behavior in physical environments
● Acquire camera footage via omnicams in simple test environments (set up manually)
● Employ slow feature analysis (MDP-based) to learn place cell responses from the acquired images
● Cleanly document the software modules for continued use in upcoming research
Group structure
● Level:– Bachelor and master students
● Number of participants:– 4-6
● Programming skills:– Python
– Matlab®
– C/C++
● Known operating systems:– Ubuntu
– MINT
Inte
rest
ed in
robo
tics!
Time planning (estimated!)
● 05.04.2018:– project starts
● 07.05.2018: – literature review complete
– start report writing
– control interface and camera interface implemented
● 05.06.2018:– test environments assembled
– visual data acquisition done, SFA tests start
– start writing documentation
● 05.07.2018:– interfaces: stable version available and tested
– SFA results available from test environments
– finalize documentation
– finalize report
● 27.07.2018:– project finishes