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Chapter 11
Postscript
The Search for the Source of Human Intelligence
There has always been a search for deeper computational power within the brain,
needed for not only higher intelligence but also common sense, truth judgment,
intuition, and artistic appraisal. Are computers with conventional gates capable of
delivering inspired intelligence? Not according to several psychological and philo-
sophical writers [ 1 , 2 ] who seldom miss an opportunity to point out that the brain
could not function as it does using conventional gates, and that artificial intelligence
using computers as we know them is futile and doomed to failure.
But what if there were a much wider variety of logic? What if it were connected
in new and different ways? And what if there were incomprehensible numbers,
hundreds of trillions (~10 12 ) of gates, instead of just millions (~10 6 ) as in current
computers? Under these conditions skeptics might have to reevaluate a brain's
computational possibilities.
Energized by topics read as a youth [ 3 ], the author has revealed that neurons do
indeed provide a wider variety of logic, such as pulsed dendritic logic gates,
enabled logic gates, regular and weak synapses to send signals where they are
needed, and short-term memory neurons to serve as timers. The choice of gates is
very much wider than what is possible using solid state, which tends to be limited to
direct current logic, that is, false is one voltage, and true is another. This style of
logic generally takes excessive energy to switch from one state to another. Neural
pulses, in contrast, avoid voltage levels, and are likely more efficient with power
and energy.
Pulsating logic better supports an asynchronous system, since it avoids the need
for a central clock as used in desktop computers. Unfortunately, high-speed
clocking requires a lot of power because of incessant high current spikes and
because clocks actually make parallel processing more difficult. Clocking suffers
from the need to employ synchronous registers everywhere, and clock skew, where
slow-moving signals as in neurons lose synchronization.
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