Grant winners – 1 December 2016

A round-up of recent recipients of research council cash

December 1, 2016
Grant winners tab on folder

Health Research Council of New Zealand

Explorer Grants

Can we rehabilitate a reflex? A treatment protocol for the cough reflex


Preclinical development of non-addictive pain medications


Using principles of the “slow movement” to prevent obesity from birth


Designing diagnostic and rehabilitation landscapes for the disabled


Royal Society

Dorothy Hodgkin Fellowships

The neurobiology of resilience after child maltreatment


Star formation: linking galaxy evolution with planetary systems


Exploring heterogeneity in implicit mentalising and its consequences in autism


Engineering and Physical Sciences Research Council

Fellowships

Personalised nanomedicine for cancer therapy


Autonomous discovery of functional small molecules


Physics of life – noise, information and evolution in protein binding


Printable micro-rockets for rapid medical diagnosis and biomarker detection


In detail

Research Grant

Award winner: James Marshall
Institution: University of Sheffield
Value: £4,816,680

Brains on board: neuromorphic control of flying robots

This five-year project will focus on developing a small flying robot with intelligence and physical abilities to rival a bumblebee. Even simple, “small”-brained animals such as bees – which have 100,000 times fewer neurons than humans – are capable of intricate coordination, multitasking and adapting to new scenarios through their lifespans. This is because they can recognise patterns and learn from previous experiences. The computational equivalent of this is machine learning, whereby a computer gradually “learns” to complete new tasks without having been specifically programmed to do so. James Marshall’s team at the University of Sheffield and their collaborators will use computational and experimental neuroscience to design lightweight, energy-efficient “brain on board” robots, armed with a computational simulation of the bee’s neural circuitry. These – the first of their kind – may be capable of learning to navigate around new environments.

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