Context dependent and multimodal learning: from insect brains to robot controllers

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Informatics

Abstract

A central issue in current robotics is how to scale up to more complex cognitive abilities, such as context dependent learning and prediction. Although insects are often viewed as simple reflexive systems, they are in fact more competent than any existing autonomous robots. They are capable of learning, integration of multisensory cues, real-world navigation, and flexible behavioural choice. As they obtain such competences with relatively small brains, understanding these mechanisms should lead to efficient robot applications. Within biology, there has recently been great interest and substantial advance in understanding the insect brain. So far, modelling of these systems has lagged behind, but it is essential for many reasons. By building models of the insect brain we can evaluate precisely expressed hypotheses about its function, and test which elements are crucial for complex behaviour. Moreover by implementing these hypotheses in hardware on robots we can understand the systems in real behavioural contexts. Thus there is a real opportunity to contribute to biological knowledge at the same time as developing systems that have useful application as robot controllers. Our intention in this project is to develop and evaluate models of learning in insect brains, using a combination of biological experiments, computational modelling, and hardware implementations. In particular we want to examine the neural mechanisms that support forms of learning more complex than simple association. These include context dependence, generalisation, and expectation-based expression of responses. Insight into these capabilities requires closer attention to the details of the mechanisms in the insect. For example, it may be important to understand the different stages and time-scales of learning and how these are supported by different biochemical processes. We can exploit an ideal combination of circumstances to make substantial advances in this area. The PI (Webb) has extensive experience in building robot models of insect behaviour, including implementing sensory and neural processing mechanisms in hardware. Along with the researcher-CI (Wessnitzer) she has developed initial models of the relevant insect brain mechanisms, and has strong connections to the leading biologists working in this area. One of these is the CI (Armstrong) who is using advanced genetic techniques to determine the roles of different structures and signalling pathways in the insect brain. Thus we intend to develop a tightly linked paradigm in which: - behavioural experiments suggested by the models provide data for model evaluation; - hardware implementation of the models provides real world evaluation and motivates abstraction; - abstracted models suggest key functional roles that can be tested using genetic manipulations on the insects. The outcome will be both significantly improved understanding of insect brains and a substantial step towards cognitive controllers in robots.

Publications

10 25 50

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Webb B (2012) Cognition in insects. in Philosophical transactions of the Royal Society of London. Series B, Biological sciences

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Wessnitzer J (2012) A model of non-elemental olfactory learning in Drosophila. in Journal of computational neuroscience

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Young JM (2011) Elemental and non-elemental olfactory learning in Drosophila. in Neurobiology of learning and memory

 
Description We have better understood the learning capacities of fruitflies, allowing us to gain general insight into the brain mechanisms underlying learning. A computational model of the neural circuits has been developed and shown to account for some but not all the biological data.
Exploitation Route The learning algorithms may have application both to biological understanding and new technologies. There is potential for use in robotic domains requiring cheap efficient processing, e.g. agriculture or environmental monitoring.
Sectors Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Electronics,Environment

 
Description European Union Framework 7
Amount £150,000 (GBP)
Funding ID FliACT 
Organisation European Commission 
Department Seventh Framework Programme (FP7)
Sector Public
Country European Union (EU)
Start  
 
Description European Union Framework 7
Amount £150,000 (GBP)
Funding ID FliACT 
Organisation European Commission 
Department Seventh Framework Programme (FP7)
Sector Public
Country European Union (EU)
Start  
 
Description FETOpen
Amount € 2,297,522 (EUR)
Funding ID FP7 618045 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 01/2014 
End 12/2016
 
Description LIN 
Organisation Leibniz Association
Department Leibniz Institute for Neurobiology
Country Germany 
Sector Academic/University 
PI Contribution Developing computational models of behaviour of Drosophila larva.
Collaborator Contribution Experimental data on behaviour of Drosophila larva.
Impact Current EU grant under FET-Open. Multidisciplinary: biology, robotics
Start Year 2008
 
Description Robotic Visions 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach Regional
Primary Audience Schools
Results and Impact 'Robotic Visions' involved roboticists and science centres working together to engage young adults in dialogue on the future of robotics research. A one day event at Glasgow Science Centre was held for final year school students.
Year(s) Of Engagement Activity 2010
URL http://www.publicengagement.ac.uk/case-studies/robotic-visions