Navigation-specific neural coding in the visual system of Drosophila

Abstract

Please cite this article in press as: Dewar, A.D.M., et al., Navigation-specific neural coding in the visual system of Drosophila. BioSystems (2015), http://dx.doi.org/10.1016/j.biosystems.2015.07.008 Drosophila melanogaster are a good system in which to understand the minimal requirements for widespread visually guided behaviours such as navigation, due to their small brains (adults possess only 100,000 neurons) and the availability of neurogenetic techniques which allow the identification of task-specific cell types. Recently published data describe the receptive fields for two classes of visually responsive neurons (R2 and R3/R4d ring neurons in the central complex) that are essential for visual tasks such as orientation memory for salient objects and simple pattern discriminations. What is interesting is that these cells have very large receptive fields and are very small in number, suggesting that each subpopulation of cells might be a bottleneck in the processing of visual information for a specific behaviour, as each subset of cells effectively condenses information from approximately 3000 visual receptors in the eye, to fewer than 50 neurons in total. It has recently been shown how R1 ring neurons, which receive input from the same areas as the R2 and R3/R4d cells, are necessary for place learning in Drosophila. However, how R1 neurons enable place learning is unknown. By examining the information provided by different populations of hypothetical visual neurons in simulations of experimental arenas, we show that neurons with ring neuron-like receptive fields are sufficient for defining a location visually. In this way we provide a link between the type of information conveyed by ring neurons and the behaviour they support. ARTICLE IN PRESS

Rotation invariant visual processing for spatial memory in insects

“…This problem has been approached from the other direction, i.e. from known properties of insect visual receptive fields to navigational behaviour [82][83][84]. This work uses the receptive fields described in [80], or a generalized version of them, to encode omnidirectional views for an insect navigation task.…”

Generating Executable Models of the Drosophila Central Complex

“…Although, the BU-EB neurons have not been systematically characterized, available information regarding these neurons was used to hypothesize the arborization structure for a total of 80 BU-EB neurons in each hemisphere of the fly brain (Hanesch et al, 1989; Young and Armstrong, 2010b; Seelig and Jayaraman, 2013; Dewar et al, 2015). Likewise, we also hypothesized isomorphic sets of pontine neurons that link regions in FB based upon (Hanesch et al, 1989).…”

“…EB appears to be involved in visual place learning (Ofstad et al, 2011; Dewar et al, 2015), short-term orientation memory (Neuser et al, 2008; Seelig and Jayaraman, 2013; Wystrach et al, 2014), angular path integration (Seelig and Jayaraman, 2015), and left-right bargaining (Strauss, 2014);…”

Using Deep Autoencoders to Investigate Image Matching in Visual Navigation

“…They achieve this task by using retinotopic imagematching methods, which has inspired a range of bio-inspired algorithms (Ants: [6][7][8]; Bees: [3]; Review: [9]). We have previously shown that panoramic images can be used for navigation in desert ant-inspired algorithms even if images are low-resolution [10], processed through coarse visual filters modelled on parts of the drosophila visual system [11], or processed so that only the height of objects against the skyline is used [12]. This work not only demonstrates the robustness of using low-resolution images for navigation but also that they can be better than high-resolution images [10].…”