First published online January 25, 2005
Journal of Experimental Biology 208, 561-569 (2005)
Published by The Company of Biologists 2005
doi: 10.1242/jeb.01371
Rats are able to navigate in virtual environments
C. Hölscher1,
A. Schnee2,
H. Dahmen2,*,
L. Setia3 and
H. A. Mallot2
1 School of Biomedical Sciences, University of Ulster, Coleraine BT52 1SA,
Northern Ireland
2 Cognitive Neuroscience, Faculty of Biology, Tübingen University, Auf
der Morgenstelle 28, 72076 Tübingen, Germany
3 University of Freiburg, Chair of Pattern Recognition and Image Processing,
Georges Köhler Allee 52, 79110 Freiburg, Germany
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Fig. 1. The machine. (A) A cross section of the machine. The rat is haltered (see
in more detail in B) on top of an air-cushioned (ain, air inlet) polystyrene
spherical shell of 50 cm diameter and mass about 400 g. The haltering consists
of a soft leather harness fastened by a hook and loop ribbon to three aluminum
plates with axial joints. The haltering allows the animal to lift and lower
its body, to rotate it about the yaw axis (the body orientation is measured by
the angular incremental encoder, aie) and to tilt it somewhat. The animal
rests with its full weight on top of the sphere. Any rotation of the sphere
about the yaw axis is prevented by two wheels (w), which are supported by
horizontal axes with tip bearings and which touch the sphere on its equator
under 90°. The air cup is tilted by about 7° and lets the sphere swim
against the wheels (w). The sphere can easily be rotated around any horizontal
axis. Any rotation of the sphere is monitored by two motion detectors
(HDNS2000, Agilent), which `look' on two 7 mm x 7 mm squares under
90° on the equator of the sphere. On the sphere surface a fine statistical
pattern of black points is sputtered with an air brush. The signals from the
motion detectors are fed to an incremental counter board implemented into the
PC that controls the VR image generation. The image of the VE is projected
from a DMD beamer (b) via two plane mirrors (p) from vertically below
onto an angular amplification mirror (aam) (black lines indicate the limits of
the illuminating light bundle). The aam is a polished rotational symmetric
aluminum surface with a vertical rotation axis, which widens the elevation of
the incoming light bundle by a constant factor of 39 over its whole surface
(red light bundle). From the aam the light is projected onto a toroidal screen
made of 24 segments of white elastic cotton. The torus acts as a horopter. The
rat sees any object on the screen under the same angle as it is projected from
the aam (blue light bundle). The screen covers the visual field of the rat
from 20° below until 60° above the horizon and 360° of azimuth
(green light bundle).
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Fig. 2. Photographs of the machine. (A) The machine in working configuration,
screen lowered. w, one of two wheels preventing the sphere from rotation about
the vertical axis. (In the lower left corner the small sister of the 50 cm
sphere can be seen, designed for insect work.) (B) Machine with screen lifted.
b, DDV 1800 (Liesegang) beamer; c, two small video `finger' cameras to control
the rat's behaviour within the screen; md, motion detector. In the background
the air support tube and the crank handle to lift and lower the screen are
visible. The photo is a portrait of the late Sigmund Exner, 1846–1926
(physiologist at Vienna). (C) The optics in more detail. aam, angular
amplification mirror; p, two plane mirrors. The beamer and its shielded output
is seen on top. (D) The haltering of the rat. aie, angular incremental
encoder. The light gray leather harness attached to aluminum sheets with axial
joints is seen. The thin brass tube ending at the rat's mouth is always
rotated with the haltering and delivers sugar water drops via a
PC-controlled valve. For details, see text.
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Fig. 3. Results of Experiment 1 in a real environment. (A) Only the area under the
white cylinder was baited. Shown are the percentages of visits under each
cylinder (shading of bar represents the colour of the cylinders in the
experiments). Animals learned the task in four sessions, with three runs per
day and per session. Animals practically stopped visiting unbaited cylinders
(one-way RM-ANOVA, F3,11=8.34, P<0.0001;
post-hoc Bonferroni multiple comparison test,
*P<0.01; N=6). (B) Time needed by animals to
find the target area below the white cylinder was reduced over sessions
(P<0.001). (C) Reversal-learning task. The black cylinder was
baited. Animals learned to visit the black cylinder and practically stopped
visiting the white cylinder (one-way ANOVA, F3,11=6.67,
P<0.001; post-hoc Bonferroni multiple comparison test,
*P<0.01, N=6). (D) Time needed by animals to
visit the black cylinder was reduced over the course of the trials
(P=0.002). The lines over the bars depict the significance between
bars/data sets and which data set was found significant compared with which
other data set in the experiments.
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Fig. 4. Experiment 2 in a virtual environment (distance between cylinders, 2 m).
Entering the area under a virtual cylinder was rewarded by a sugar water drop.
The cylinders had a radius of 25 cm, were 80 cm high, 2 m apart, suspended 20
cm above ground, and textured with black and white stripes. After a reward the
animals had to cross an outer circle of r0=0.5 m before
they could get another reward under the same cylinder. (A) The numbers of
rewards (hits) per 10 min run increased over the course of 10 days (means
± S.E.M., N=12 animals; one-way RM-ANOVA,
F9,11=40.7, P<0.0001; post-hoc
Bonferroni multiple comparison test, NS, non significant,
**P<0.01; ***P<0.001;
N=12). (B) The number of hits per 2 m distance increased over time
(ANOVA, F9,11=22.1, P<0.0001;
post-hoc test, NS, non significant; **P<0.01;
***P<0.001). The maximum number of hits achievable per
2 m was 1, animals reached a mean value of 0.76±0.024 (±
S.E.M.). (C) The average trace length per 10 min increased over 10
days, i.e. the average speed of the animals. (D) The inter-individual
variation of the trace length, averaged over all 10 days, versus the
animal number.
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Fig. 5. Sample trajectories of day 1 (blue) and day 10 (red) for rat 9 in
Experiment 2. (A) Map of virtual environment with cylinders drawn to scale.
Hit cylinders are encircled by light grey discs. Starting points appear
towards the top of the figure. (B) Detail of day 1 trajectory, as boxed in A.
Dots mark every 30th trace sample (every 1.4 s). (C) Detail of day 10
trajectory, as boxed in A. (D) Rat average position relative to closest
cylinder on day 1. (E) Histogram of body orientations on day 1 as measured
using the angular incremental encoder. (F) Rat average position relative to
closest cylinder on day 10. Note the much more peaked distribution, indicating
that the rat spent more time in the vicinity of the cylinder than on day 1.
(G) Histogram of body orientation on day 10. Note the pronounced orientation
to S on day 10 compared to E, superimposed on a broadly distributed general
orientation to SE. For details, see text.
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Fig. 7. Sample trajectories of day 1 (blue) and day 5 (red) for rat 9 in Experiment
3. (A) Map of virtual environment with cylinders drawn to scale. Hit cylinders
are encircled by light grey discs. Starting points appear towards the top of
the figure. (B) Detail of day 1 trajectory, as boxed in A. Dots mark every
30th trace sample (every 1.4 s). (C) Detail of day 5 trajectory, as boxed in
A. (D) Rat average position relative to closest cylinder on day 1. (E)
Histogram of body orientations on day 1 as measured using the angular
incremental encoder. (F) Rat average position relative to closest cylinder on
day 5. (G) Histogram of body orientation on day 5. Note the dominant
orientation to ESE on day 5 compared to the orientation peak to S in day 1.
For details, see text.
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Fig. 6. Experiment 3 was performed in an identical environment as Experiment 2
except the distance between columns was increased to 10 m and
r0 to 2 m. (A) The numbers of rewards (hits) per 10 min
run increased over the course of 5 days (ANOVA, F4,11=7.3,
P<0.0001; post-hoc test, NS, non significant;
*P<0.05; **P<0.01;
***P<0.001; N=12). (B) The numbers of hits per
10 m distance run increased over time (ANOVA, F4,11=5.37,
P<0.002; post-hoc test, NS, non significant,
*P<0.05; **P<0.01). The maximum
achievable number of hits per 10 m was 1, animals reached 0.644. (C) The
average trace length per 10 min increased a bit over 5 days, the animals were
trained in Experiment 1. (D) The inter-individual variation of the trace
length, averaged over all 5 days, versus the animal number.
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© The Company of Biologists Ltd 2005
