Elsevier

Behavioural Brain Research

Volume 266, 1 June 2014, Pages 161-168
Behavioural Brain Research

Research report
Mongolian gerbils learn to navigate in complex virtual spaces

https://doi.org/10.1016/j.bbr.2014.03.007Get rights and content

Highlights

  • We implemented complex virtual realities to investigate rodent navigation.

  • Mongolian gerbils were successfully trained to navigate in such virtual environments.

  • The animals generalized to unknown environments after training on a different maze.

Abstract

Virtual reality (VR) environments are increasingly used to study spatial navigation in rodents. So far behavioral paradigms in virtual realities have been limited to linear tracks or open fields. However, little is known whether rodents can learn to navigate in more complex virtual spaces. We used a VR setup with a spherical treadmill but no head-fixation, which permits animals not only to move in a virtual environment but also to freely rotate around their vertical body axis. We trained Mongolian gerbils to perform spatial tasks in virtual mazes of different complexity. Initially the animals learned to run back and forth between the two ends of a virtual linear track for food reward. Performance, measured as path length and running time between the virtual reward locations, improved to asymptotic performance within about five training sessions. When more complex mazes were presented after this training epoch, the animals generalized and explored the new environments already at their first exposure. In a final experiment, the animals also learned to perform a two-alternative forced choice task in a virtual Y-maze. Our data thus shows that gerbils can be trained to solve spatial tasks in virtual mazes and that this behavior can be used as a readout for psychophysical measurements.

Introduction

Rodents are the most widely used model animals for studying spatial learning and navigation [1], [2], [3] and the underlying neuronal processes [4], [5], [6], [7], [8], [9]. Traditionally, spatial behavior has been investigated in these studies using linear tracks [10] or open fields in various enclosures [11]. Building mazes for more complex spatial tasks [8], [12] is possible with much greater effort but does not overcome the restrictions of the typical lab scale of a few meters. More recently, virtual reality (VR) paradigms have been developed [13], [14], [15], [16], [17], [18], which not only make it feasible to investigate behaviors on arbitrary spatial scales but are also suitable for closed-loop manipulations of the environments, and even allow one to generate physically impossible environments to discriminate between alternative navigation strategies [for reviews see [19], [20]]. Such VR setups allow one to use stable head-fixed preparations in awake behaving animals and combine navigational experiments with advanced recording techniques, such as intracellular recordings [14], [15] or two-photon imaging [21], [22]. In spite of the great success of these VR setups, there were only few attempts to train animals to carry out more complex navigational tasks in virtual environments [23], [24]. Behavioral paradigms in virtual realities for rodents mostly made use of open fields [13], linear tracks [14], [15], [17], [25] or were limited to providing optical flow [16], [22] and spatially unrelated visual stimuli [26]. Moreover, subtle differences in the neuronal space codes have been reported between running in real worlds and VR behavior [18] such that it is not entirely resolved, whether the behavior observed in VR is based on the spatial strategies to a similar extent than in real worlds or whether it is more strongly reflecting direct sensory (visual) stimulation.

In this paper, we report on virtual spatial behaviors in more complex environments, in which the animals are required to perform navigational or behavioral tasks. Since VR setups mainly stimulate the visual modality, we used Mongolian gerbils (Meriones unguiculatus) whose visual system is superior to those of mice or rats [27], [28], [29]. Moreover, spatial navigation in gerbils has been well documented in studies on path integration [1], [30], [31]. Our results demonstrate that gerbils which learned to operate a virtual linear maze were able to make use of their acquired skills in more complex virtual environments. There, the animals exhibited exploratory behavior, even upon first exposure.

Section snippets

Animals

Experiments were performed on adult Mongolian gerbils (Meriones unguiculatus). We used a total of ten gerbils of both sexes. Training started at ages between three and seven months and the animals weighed between 70 and 100 g. The animals received a diet which kept their weight at about 85–90% of their free feeding weight. All experiments were approved according to German Animal Welfare Act and linked European regulations (Reg. von Oberbayern, AZ 55.2-1-54-2532-10-11).

Experimental apparatus

As in previous approaches

Linear maze training

In a first step, we taught animals to move in a linear virtual maze and to orient themselves along straight virtual walls (see Fig. 1C and Section 2). The virtual track was two or four meters long, 23 cm wide and consisted of walls with different textures.

In the first training sessions we did not close the projection screen, i.e., the screen surrounded the animal only to 270°, which gave us easier access to the animal such that we could provide the gerbil with occasional manual feedback to

Discussion

This study demonstrates that rodents can navigate in complex virtual mazes. The stimulated modality was mostly vision and, to a lesser extent, proprioception and the vestibular sense. Our data show that, after training on a virtual linear track for about five days, gerbils accept visual walls without haptic feedback and generalize to more complex virtual mazes. The results support the hypothesis that in VR the animals use spatial strategies to collect food rewards and that they do not simply

Acknowledgements

The authors thank Hansjürgen Dahmen for extraordinary help during the construction of the VR setup, Moritz Dittmeyer for providing the photos and schematic drawing of the setup, and Michael Pecka for helpful comments on the manuscript. This work was funded by the German Ministry for Education and Research (BMBF) via Grant Number 01GQ0440 (Bernstein Center for Computational Neuroscience Munich).

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