http://www.brainsciencepodcast.com/bsp/2008/12/6/surprising-...
This is the statement that always comes back to bite biologists. If it's "theoretically possible", biology is probably already doing it somewhere.
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.
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.
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.
Be more successful in avoiding predators, acquiring food, mating.
Mother Nature will get every little advantage she could scrounge up.
Is it simply a matter of time before we find a quantum computer in there?
And since our brain, like the rest of our bodies, is full of enzymes, I would in no way be surprised if we find other quantum effects.
That said, it's a very important development, because until the last few years the glial cells have mostly been considered to be support cells (e.g. supplying nutrients to the neurons, removing waste products and dead cells, myelinating axons, etc.). But, now we know that they can affect the surrounding neurons and may play a role in things like learning and memory.
I think we can be fairly certain that glial cells are involved in neuronal communications, but I'd not say this paper at all proves that.
So sorry, but I don't have primary sources for what I said in my earlier comment...
[1]: http://www.brainsciencepodcast.com/bsp/2010/5/12/exploring-g...
We had known previously that the axons could send messenger proteins back to the soma (cell body), thus modulating transmitter productions, and could have an inhibitory or excitatory effect on the cell as a whole. We were also aware of axo-axonic synapses, whereby axons could inhibit other axons (among some other things).
EDIT: The above is just extremely brief background of well-known facts about axon messaging.
"...Maintenance of presynaptic inputs may depend on a post-synaptic factor that is transported from the terminal back toward the soma."
-Neuron: Cell and Molecular Biology (1st edition c 1991)
