‘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