Project overview
Nature is uniquely good at constructing complex, versatile, self regulating systems from cells to us. Learning to manipulate complexity in a similar way should allow new solutions to many problems. In this project we are aiming to direct the assembly of conducting networks so that there is information (function) encoded in the structure of the product. This is very similar to the way in which the central nervous system both stores information and responds to stimuli, and our target is to physically realise a type of computational device known as a Neural net. To achieve function we have two approaches / 'learning' in which the net can be taught to have a function, and 'evolution' where the parameters used to construct the net act as a gene which can be evolved to code for nets with the required function. It is likely that a combination will be needed (evolution to establish a gross structure, and learning to 'fine tune' it) in much the same way as a our central nervous system develops.We will be using conducting polymers, nanoparticals, nanotubes, and dynamic chemical waves to construct our networks, mainly electrical potentials to direct formation, and conductance properties to characterise them.
Staff
Lead researchers
Research outputs
Santiago Martin, Francesco Giustiniano, Wolfgang Haiss, Simon J. Higgins, Richard J. Whitby & Richard J. Nichols,
2009, The Journal of Physical Chemistry C, 113(43), 18884-18890
DOI: 10.1021/jp906763p
Type: article
Dan H. Marsh, Graham A. Rance, Richard J. Whitby, Francesco Giustiniano & Andrei N. Khlobystov,
2008, Journal of Materials Chemistry, 18(19), 2249-2256
DOI: 10.1039/b801334a
Type: article
Dan H. Marsh, Graham A. Rance, Mujtaba H. Zaka, Richard J. Whitby & Andrei N. Khlobystov,
2007, Physical Chemistry Chemical Physics, 9(40), 5490-5496
DOI: 10.1039/b708460a
Type: article