Unlocking neural complexity with a robotic key. (9th March 2016) - Stratton, Peter; Hasselmo, Michael; Milford, Michael
Abstract : A, animal brains are complex because animals exist in a complex world. B and C, studying animals directly can be augmented by bio‐mimetic robotics ( B ), where the primary goal is to yield further insights into the biological system, and by bio‐inspired robotics ( C ), where some or all of the biological constraints are relaxed in order to achieve high levels of robotic performance. Ultimately, complex neural dynamics emerge from the interaction of the brain, the body and the complex world, and give rise to the brain's abilities for flexible cognitive information processing. These abilities cannot be understood through the study of isolated neural systems; instead, a holistic approach is required, and robotics can exploit real‐world complexity while simultaneously offering an entirely observable, fully controllable experimental model for study. ( A reproduced from Wikipedia, CC BY‐SA 4.0; B reproduced from robotsvsanimals.files.wordpress.com/2014/07/brl‐best‐023‐add‐black.jpg; C from Michael Milford.) Abstract: Complex brains evolved in order to comprehend and interact with complex environments in the real world. Despite significant progress in our understanding of perceptual representations in the brain, our understanding of how the brain carries out higher level processing remains largely superficial. This disconnect is understandable, since the direct mapping of sensory inputs to perceptual states is readily observed, while mappings between (unknown) stages of processing and intermediate neural states is not. We argue that testing theories of higher level neural processing on robots in the real world offers a clear path forward, since (1) the complexity of the neural robotic controllers can be staged as necessary, avoiding the almost intractable complexity apparent in even the simplest current living nervous systems; (2) robotic controller states are fully observable, avoiding the enormous technical challenge of recording from complete intact brains; and (3) unlike computational modelling, the real world can stand for itself when using robots, avoiding the computational intractability of simulating the world at an arbitrary level of detail. We suggest that embracing the complex and often unpredictable closed‐loop interactions between robotic neuro‐controllers and the physical world will bring about deeper understanding of the role of complex brain function in the high‐level processing of information and the control of behaviour.