Insects are particularly suitable for behavioral-neural analyses. Such devices have been developed and combined with neural recordings of EEG, local field potentials and single neurons for humans ( Gillner and Mallot, 1998 Araújo et al., 2002 Baumeister et al., 2010 Doeller et al., 2010) as well as for animals ( Mizunami et al., 1998 Harvey et al., 2009, 2012 Dombeck et al., 2010 Takalo et al., 2012 Aronov and Tank, 2014).
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Nevertheless, animals and humans can learn and navigate in a virtual reality set-up that produces the relevant visual feedback to the intended movements ( Gillner and Mallot, 1998 Holscher et al., 2005, design guidelines for VEs can be found at: ). Its disadvantages relate to compromised sensory feedback provided by the moving visual world and the stationary conditions of the animal. The latter has the advantage that the simulated environment can be large and fully manipulated. In the past, two different approaches have been followed to search for neural correlates of operant learning and navigation: Monitoring neural activity of animals (usually rats) while navigating in a rather small space ( O'keefe and Nadel, 1978 McNaughton et al., 2006 Puryear et al., 2010 Ball et al., 2014), and animals navigating in a virtual environment. Our results suggest an involvement of the mushroom body extrinsic neurons in operant learning in the honeybee ( Apis mellifera). We found changes in the neural activity specific to the rewarded and unrewarded visual stimuli. In search for neural correlates of learning in the VE, mushroom body extrinsic neurons were recorded over days during learning.
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We show that honeybees perceive the stimuli in the VE as meaningful by transferring learned information from free flight to the virtual world. We developed a virtual environment (VE) that simulates a simplified 3D world for honeybees walking stationary on an air-supported spherical treadmill. The search for neural correlates of operant and observational learning requires a combination of two (experimental) conditions that are very difficult to combine: stable recording from high order neurons and free movement of the animal in a rather natural environment.