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Authored by: Anonymous on Monday, December 31 2012 @ 04:59 PM EST |
it's still a matter of debate as to how much digital is in your brain. But in
any case, as I have repeatedly pointed out, the brain is simply not fast enough
for a lot of tasks.
[ Reply to This | Parent | # ]
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- Actually - Authored by: jesse on Tuesday, January 01 2013 @ 07:07 AM EST
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Authored by: jesse on Tuesday, January 01 2013 @ 10:31 AM EST |
The problem is in the analog nature itself.
If I remember right, there are two problems:
1) calibration - Each operational unit must be calibrated to function within its
circuit. Each circuit must then be calibrated to function within the larger
circuits involving other units. Setting one unit can alter the functioning of
the other units.
2) maintaining calibration - over time, the parts wear (in/out), thus changing
their calibration. To compensate for this requires additional circuit elements
to identify and feed back the error into the original circuit (to keep them
stabilized). These additional elements also require calibration...
And thus become a recursive problem...
Even during WW2, the fanciest bomb sight (the Norden one) had to be kept at a
proper temperature, or the targeting would be off due to the
expansion/contraction of the gears causing inaccurate runs.
So calibration can only be done "close enough". But the result isn't
stable over time.
Part of neural net inputs is nothing but such calibration feedback. The problem
the research has is identifying/separating out which inputs are for
calibration... and which are the data. After all they both look the same.
Most of the neural net research being done on a digital computer ignore the
calibration -- other than calling it feedback learning.
The organic neural nets have more inputs than just the interconnection network.
They also have food/oxygen/hormone/chemical/magnetic/... inputs. For the most
part these seem to be ignored or classed as "internal state", but none
of the formula for neural nets (at least the ones I've been able to find)
include them.[ Reply to This | Parent | # ]
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