Visual navigation in ants: from visual ecology to brain

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We will use complementary methodological approaches to understand the nature of the visual memory that supports navigation in ants. We will develop procedures for carrying out brain lesions in ants, targeting a range of locations in central complex and mushroom body, and investigate the consequences in visual navigation tasks. Subsequent histology will allow us to correlate lesion locations with behavioural deficits. In parallel, we will establish a new experimental system using a compensatory treadmill to allow precise control over the visual stimulation provided to freely walking ants. This method will enable extremely rapid and minimally invasive transfer of ants from a conventional arena training paradigm to this controlled testing paradigm, supporting high-throughput experiments. These experimental methods will be coupled with analytical approaches to the information content in natural scenes from the ant habitat, to refine the stimulus paradigms and provide realistic input to computational models. An agent model (a simulated ant moving through a virtual world) will allow us to test specific algorithms for visual navigation under precisely parallel conditions to the animal, and thus allow us to devise crucial paradigms for the experimental system under which alternative models make different predictions. In particular, we will examine what are the critical eye regions, the essential image information content, and the most efficient and effective encoding and retrieval schemes to account for navigational behaviour. In the same agent model, we will also test more detailed models of the relevant brain circuitry, to understand how it could support such processing, and close the loop with predictions for new trackball and lesion studies and potential extensions towards single-cell electrophysiology of neurons in relevant brain regions.

Insect-inspired visual algorithms have many potential applications in technology, as they offer efficient and economical solutions to real-world problems, including detection and tracking, visual stabilisation, impact prediction, and the focus of our study, navigation. Sensor and computational systems directly derived from research on insect behaviour and neurobiology have been applied to car safety systems, autonomous air vehicles and robotics. In most cases this has involved relatively simple reflex behaviours, where biological knowledge of the mechanisms is better established. We plan through the current research to make a significant advance in understanding the more complex algorithms that allow insects to navigate robustly and reliably, and will actively explore the potential for transfer to engineering in two main domains.

1) Supporting human navigation through the development of apps for mobile devices. While satnav has revolutionised human way-finding, there are still a number of scenarios in which it is unavailable or inaccurate, or simply not appropriate to the task, such as remembering routes through corridors in a multi-story building. This is both an everyday problem and one that is important in specific contexts such as hospitals where age-related memory loss complicates an already challenging task. An insect-inspired approach has a number of advantages in this context: it does not rely on object recognition; it is relies on the global scene and is thus relatively robust to local change; it does not involve hefty computation hence is viable to implement on small cheap devices; it depends largely on a single visual input (i.e. without direct depth information) so can exploit the ubiquity of cameras on hand-held devices. In the Pathways to Impact we describe our specific plans to develop and evaluate such a mobile phone app.

2) Supporting robot and autonomous vehicle navigation. Our intent here is not to challenge the current paradigm in autonomous car and conventional robotic development which relies on building complete 3D representations of the environment to plan motion, but rather to focus on systems that are suitable for small, cheap robots with relative limited computational power. A target application is agriculture, which is recognised as an industry with huge potential to benefit from increased automation if the problems of dealing with complex, cluttered natural terrain (very different to road systems) can be solved. Many of the advantages described above for the insect-inspired approach translate equally to this scenario, with the additional advantage that the navigational solutions of insects evolved for precisely such environments. Both Edinburgh and Sussex are already active in robot research and have excellent contacts and infrastructure to take this work forward, as described in the Pathways to Impact. Solutions in this domain may generalise to other important areas such as environmental monitoring and clean-up.
Both the technologies described above have obvious potential for societal benefits and link to RCUK national priorities.

In addition, ant navigation research is a very effective topic in public communication of science, as the problem faced by the animals is easy to present and imagine, but the abilities exhibited by this 'mere insect' highly surprising and impressive. We already have extensive involvement in outreach activities that present the 'navigation challenge' faced by ants to humans and use this to generate interest and understanding in biology and computation; with an additional side-effect of providing data on human navigation. As described in the Pathways to Impact, we plan to expand this approach to a web-based citizen science experiment. This will provide a better transfer of information than traditional science outreach through both higher quality engagement and higher quantity involvement.