Neural networks have gone a long way in the last half a century. It is important to have clear direction and therefore there is a need for a panel discussing perspectives of the field.
Neural networks have branched into many areas, giving inspirations to machine learning (kernel systems, signal analysis) on one extreme, and to computational cognitive neuroscience with rather faithful biophysical models on the other side. In recent years most of the improvements of current models are still concerned with learning in relatively simple situations, from single dataset. Although we can do classification and approximation quite well with many learning models we still do not have systems that can learn from natural perception, or can learn difficult logical problems.
One direction is to look for neurocognitive inspirations at higher level than single neurons. What are the most promising directions to reach human-level competence? Important advances in recent years have been made in biologically inspired vision, auditory scene analysis, neural hardware and many other directions. Where should we concentrate our efforts?
Please send us questions to be posted below and we will invite top experts in the field to answer these questions and to discuss them during the panel. We do not plan position statements of the panelist to have more time for real discussion. After the panel these pages will be opened for further discussion.