What I find surprising about this type of news is why the brain would need so much complexity.<p>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.<p>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).<p>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.
I didn't think this was new... I remember hearing about this effect last year and having it attributed to Oligodendrocytes, I believe.<p>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.
Here's the Nature paper if anyone's interested (from Dec 2010) <a href="http://www.nature.com/neuro/journal/v14/n2/full/nn.2728.html" rel="nofollow">http://www.nature.com/neuro/journal/v14/n2/full/nn.2728.html</a><p>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).<p>EDIT: The above is just extremely brief background of well-known facts about axon messaging.
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.
Info/background:<p>"...Maintenance of presynaptic inputs may depend on a post-synaptic factor that is transported from the terminal back toward the soma."<p>-Neuron: Cell and Molecular Biology (1st edition c 1991)