So reflexes are not the simple thing that people have taken to be,
they're neurologically much more complex.
But they do make the point that for very good reasons,
the experience over evolutionary time and lifetime as well, but
over evolutionary times as we've discussed before, is doing the heavy lifting,
you can generate an association between an input and
an output more or less automatically, that serves a good purpose.
The purpose served by this reflex may not be obvious to you,
but it is if you think about it, straight forward.
If you're walking along and your toe stubs a root and
you are about to trip, the extension of the leg that's generated in
this reflex manner keeps you from falling down.
And is obviously biologically useful, even lifesaving,
but certainly one of the contributors to our longevity as a species.
So what is the alternative idea that the brain is not acting
as a computer in the normal sense of this phrase, but
is solving a problem by making associations that are useful?
Well, there's whole other way of thinking about neural networks,
other than thinking about them as executors of algorithms.
I should have said before but let me emphasize here,
that algorithms are logical sequences of operations that can be expressed and
are expressed in software,
as a series of rule based steps that lead you to the solution of some problem.
There's a very different way of thinking about computation
that is unsupervised and simply operates empirically.
And this is an idea about the way that computers
might work that dates from the 1940s by McCulloch and
Pitts, two workers at MIT in those days.
And they established the idea of artificial neural
networks as a system of connections that simply operated
by taking information that was inputted to the network,
and this is a very simple network diagrammed here.
And through the connections established empirically, just by trial and
error, the behavior out could eventually after enough learning,
evolutionary or lifetime learning, succeed, and
the success of the information would be fit back into the input.
And through this loop, gradually an artificial network would
learn how to solve basically any problem that you put to it.
Computer scientists haven't really liked that idea so
much, although artificial neural networks have had
their ups and downs over the last 70 or 80 years.
But they are widely recognized as a possible solution to problems
that a computer encounters or biological agent encounters.
And I think that they come much closer to the kind of work that the brain is doing.
And present a much better analogy than does the sort of ordinary
algorithmically driven computer that's executing a program.
So what's the network role?
Well, as I said, this is quite a simple network but
it's just a bunch of neurons that are connected,
there are a large variety of ways, of course, of connecting them.
And much work has been done on this over the last 60, 70 years to
demonstrate that yes, artificial networks can solve a variety of problems.
And this idea that was inspired by neurons in the first place,
that's where McCulloch and Pitts took their idea from,
that you can make these networks as complicated as you'd like.