Redefining Reality - The Intellectual Implications of Modern Science

The attempts to do this can be traced back to 1943, when Warren
switches and showed how one could build something like a Turing
machine out of them. If we treat neurons as on/off switches, we can
But neuroscience soon showed us that neurons are much more
complicated. Real neurons have multiple dendrites that branch
off and create incredibly complex webs of interconnections. Any
models we make would have to be intricate neural networks or
neural nets. This would require computers much faster and more
powerful than anything available, and for quite a while, the project
lay dormant. But it was revived in the 1980s when technology made
it viable once again.
Computer models need to have layers of analysis based on both
feedback and feed-forward behaviors. A
occurs when a system responds to a context, in this case, a set
of values, by doing something. A feedback loop is where an
algorithm’s output is fed back into the program as input.
When a program engages in feed-forward behavior, it acts;
then, the feedback behavior takes that action into account in
reassessing the situation. In this way, the computer can act
and learn from the results of past actions. It can improve itself
at tasks.
Add to this the ability to perform layers of analysis, and you
have the ability to not just judge the likely successfulness of
a given act but to come to generalized results about classes of
similar acts.
Having neural nets that are able to achieve results in different
frames—having the ability to achieve what we might think of
as different perspectives—may be the gateway to strong AI.
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