Brain modelling

Question

Just wondering, since we've reached 1 teraflop per PC, yet we are still not able to model an insect's brain. Has anyone seen a decent implementation of a self-learning, self-developing neural network?

Answer 1

I saw an interesting experiment mapping the physical neural layout of a rat's brain to a digital neural network with weighting modelled on the neuron chemistry of each component taken using MRI and others. Quite interesting. (new scientist or Focus, 2 issues ago?)

IBM Blue Brain comes to mind http://news.bbc.co.uk/1/hi/sci/tech/8012496.stm

The problem is computation power as you rightly point out. But for a sequence of stimuli to a neural network the range of calculations tends to be exponential as that stimuli encounters deeper nested nodes. Any complex weighting algorithm means that time spent at each node can get expensive. Domain specific neural-maps tend to be quicker because they are specialized. Brains in mammals have many general paths, making it harder to teach them, and for a computer to model a real mammal brain in a given space/time.

Real brains also have tons of cross-talk like static (some people think this is where creativity or original thought stems from). Brains also don't learn using 'direct' stimulus/reward ... they use past experience of non-related matter to create their own learning. Recreating the neurons is one thing in a computational space, creating an accurate learning is another. Never-mind the dopamine (octopamine in insects) and other neurological chemicals.

imagine giving a digital brain LSD or anti-depressants. As a real simulation. Awesome. That would be a complex simulation I suspect.

Answer 2

In 2007, they simulated the equivalent of a half mouse brain for 10 seconds at half the actual speed: http://news.bbc.co.uk/1/hi/technology/6600965.stm