Non-linear Media based Computers:
Chemical & Neuronal Networks through Machine Learning
Investigators: Professor P Husbands and Professor M O'Shea
Researchers: Dr. K Dale, Dr. S. Ott
Project Partners: University of West of England, University of Leeds
There is growing interest in research into the development of hybrid wetware-silicon devices focused on exploiting their potential for 'non-linear computing'. The aim is to harness the as yet only partially understood intricate dynamics of non-linear media to perform complex 'computations' (potentially) more effectively than with traditional architectures and to further the understanding of how such systems function. The area provides the prospect of radically new forms of machines and is enabled by improving capabilities in wetware-silicon interfacing.
The research proposed here will present an approach by which networks of non-linear media - neurons and reaction-diffusion systems - can be produced to achieve a user-defined computation (or behaviour) in a way that allows control of the media used and the substrate in which they exist. Simulated evolutionary algorithms and other machine learning methods will be used to design and manipulate the appropriate network structures to create a ‘computing’ resource capable of satisfying a given objective(s).
Funding: EPSRC
Starts: 1 January 2005 Ends: 31 December 2007