Unlocking neural complexity with a robotic key

Stratton, Peter, Hasselmo, Michael and Milford, Michael (2016) Unlocking neural complexity with a robotic key. Journal of Physiology, 594 22: 6559-6567. doi:10.1113/JP271444

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Author Stratton, Peter
Hasselmo, Michael
Milford, Michael
Title Unlocking neural complexity with a robotic key
Journal name Journal of Physiology   Check publisher's open access policy
ISSN 1469-7793
0022-3751
Publication date 2016-03-09
Year available 2016
Sub-type Article (original research)
DOI 10.1113/JP271444
Open Access Status File (Author Post-print)
Volume 594
Issue 22
Start page 6559
End page 6567
Total pages 9
Place of publication Chichester, West Sussex, United Kingdom
Publisher Wiley-Blackwell Publishing
Language eng
Subject 1314 Physiology
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.
Keyword Active efficient encoding
AEC
Central pattern generator
CPG
Complex brains
Higher level processing
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID P50 MH094263
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Brain Institute Publications
 
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