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.
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.
I didn't think this was new... I remember hearing about this effect last year and having it attributed to Oligodendrocytes, I believe.
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.
This article was from last year (don't know if it's the one you're thinking of), but the paper just theorizes that glia may be involved in the described mechanism.
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.
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.
There have been recent discoveries about synapses as well. It turns out that synapses are pretty complex. Lots of single celled organisms seem to have pretty "smart" behavior for their size. It turns out a lot of that data processing happens around the cell membrane. These mechanisms are the evolutionary roots of synapses.
Indeed, it seems that some kind of 'backpropagation' does happen in the brain, in contrary to what was always believed. This might have impact on machine learning research.
If the network has significant feedback, couldn't these slower "backward" signals be understood in a similar fashion as a fast-moving propeller that appears to reverse direction? I'm curious about how they measured this, but I don't have thirty dollars to spend.
[+] [-] tgflynn|15 years ago|reply
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|15 years ago|reply
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|15 years ago|reply
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|15 years ago|reply
Be more successful in avoiding predators, acquiring food, mating.
Mother Nature will get every little advantage she could scrounge up.
[+] [-] a-priori|15 years ago|reply
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.
[+] [-] ihodes|15 years ago|reply
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.
[+] [-] ihodes|15 years ago|reply
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.
[+] [-] marshray|15 years ago|reply
Is it simply a matter of time before we find a quantum computer in there?
[+] [-] ihodes|15 years ago|reply
[+] [-] iwwr|15 years ago|reply
[+] [-] stcredzero|15 years ago|reply
http://www.brainsciencepodcast.com/bsp/2008/12/6/surprising-...
[+] [-] wladimir|15 years ago|reply
[+] [-] guscost|15 years ago|reply
[+] [-] DavidSTO|15 years ago|reply
"...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)
[+] [-] tjmaxal|15 years ago|reply
[+] [-] drstrangevibes|15 years ago|reply