[tgflynn]

What I find surprising about this type of news is why the brain would need so much complexity.

It seems to me that a network with 10^11 neurons and 10^14 synapses should have sufficient computational power to carry out the information processing tasks that humans perform using only simple function neurons.

This belief is based on the following observations : - I have personal experience with ANN's with only thousands of nodes that are able to rival humans at handwriting recognition. - Current computers are far from being powerful enough to simulate a 10^14 synapse ANN yet they seem to be rapidly approaching human level performance on many cognitive tasks (ie. Watson).

If individual neurons are as complex as recent research results suggest I wonder what all that computational power is being used for. Or is the human brain just hopelessly inefficient as an information processing machine ? Maybe it's such a recent development that evolution just hasn't had time to get things right.

[roc]

Hazarding a guess... redundancy and adaptability?

Watson's not going to suffer damage to his neurons and still function, nor lose a swath of them permanently, but eventually relearn how to talk.

Nor is it going to be able to ever independently 'learn' a new skill in general.

[Udo]

It's not about complexity or raw computational power, it's about functionality. One area where brain research still struggles to model the basics is how the brain actually integrates information over time, and how internal models and representations are formed.

The "classical" synaptic response model was always good at explaining basic signal transmission, but it was essentially stateless. Now we know that neurons are far from stateless, there is extensive chemical modification going on working at different timescales and I guess this "new" discovery is also an important piece that was missing from the standard model. It may explain advanced neuronal states that surpasses simple chemical sensitization and suppression - and it may also provide hints about how feedback works in learning and building internal representations.

ANNs and other AI techniques are getting very good and efficient, but one reason why general artificial intelligence (as in artificial persons) continues to escape us is that we still don't have a good model how the brain organizes and improves itself to form a consistent but autonomously adapting unit which can rightfully be called a mind. I hope that AI people can use these pointers provided by bio research and advance toward this goal.

[Florin_Andrei]

> why the brain would need so much complexity

Be more successful in avoiding predators, acquiring food, mating.

Mother Nature will get every little advantage she could scrounge up.