Connectionists: Brain-like computing fanfare and big data fanfare

[Dan Goodman]

The comparison with physics is an interesting one, but we have to 
remember that neuroscience isn't physics. For a start, neuroscience is 
clearly much harder than physics in many ways. Linear and separable 
phenomena are much harder to find in neuroscience, and so both analysing 
and modelling data is much more difficult. Experimentally, it is much 
more difficult to control for independent variables in addition to the 
difficulty of working with living animals.

So although we might be able to learn things from the history of physics 
- and I tend to agree with Axel Hutt that one of those lessons is to use 
the simplest possible model rather than trying to include all the 
biophysical details we know to exist - while neuroscience is in its 
pre-paradigmatic phase (agreed with Jim Bower on this) I would say we 
need to try a diverse set of methodological approaches and see what 
wins. In terms of funding agencies, I think the best thing they could do 
would be to not insist on any one methodological approach to the 
exclusion of others.

I also share doubts about the idea that if we collect enough data then 
interesting results will just pop out. On the other hand, there are some 
valid hypotheses about brain function that require the collection of 
large amounts of data. Personally, I think that we need to understand 
the coordinated behaviour of many neurons to understand how information 
is encoded and processed in the brain. At present, it's hard to look at 
enough neurons simultaneously to be very sure of finding this sort of 
coordinated activity, and this is one of the things that the HBP and 
BRAIN initiative are aiming at.

Dan