The brain is perhaps the most complex organ, consisting of myriads of elementary units (neurons and synapses), and it is still the greatest challenge of science to understand how their collective functioning gives rise to meaningful, adaptive behaviour. The seemingly effortless ease with which we perceive the world, extract relevant information from memory and move our lips when we speak masks the true complexity of the processes involved. This is evident when we try to build machines to perform human tasks. While computers can now beat grandmasters at chess, no computer can yet control a robot to manipulate a chess piece with the dexterity of a six-year-old child or recognize a chess set that it has never seen before.
Using the formal approaches of computational neuroscience, a discipline that studies the nervous system through mathematical models, we have the hope both to understand the fundamental organising principles of the brain and to employ these to build more efficient machines. As the superiority of biological systems over machines is rooted in their remarkable adaptive capabilities our research is focussed on the computational foundations of biological learning.
Biological Learning consists of three groups: