Learning as a Factor in the Evolution of Neural Architecture and Life History

Monday 17 November 2014, 1.00PM

Speaker(s): Dr John Bullinaria, University of Birmingham

Synopsis:

A key aspect of my recent research has involved investigations into how lifetime learning has affected the evolution of different animal species. That is useful in its own right, but it is also important to understand what has led to the evolution of different species if we want to use them as inspiration for building useful artificial systems. The two issues I have been concentrating on are: Neural Architecture (in particular, the advantages and causes of modularity), and Life History (the sequence of changes that take place during an organism’s lifetime: stages of growth, age at first reproduction, frequency of childbirth, litter size, protection of children, menopause, death). These don’t sound particularly related, but they do influence each other, and the same Artificial Life approach has proved effective for studying both. My talk will give an overview of my general approach, and also present some detailed results for a few particularly interesting aspects.

Biography:

John Bullinaria completed a BSc in Physics at Imperial College London, Maths Part III at Cambridge University, and a PhD in Theoretical Physics at Southampton University. He then spent two years as a Research Fellow in Mathematics at Durham University working on Superstring Theory and Quantum Gravity, before leaving academia to travel the world for three years. He returned to get a "proper job" but instead obtained an MSc in Artificial Intelligence from Cranfield University, and spent the following nine years as a Research Fellow carrying out computational modelling in various Psychology departments. He moved to the School of Computer Science at the University of Birmingham in 2001, where he is now a Senior Lecturer. His current research interests are mainly in the fields of Computational Intelligence, Cognitive Science, and Artificial Life.

Location: PL005

Email: hf510@ohm.york.ac.uk