Chapter 2. Prehension

400 A pp e n dices
of computations neural networks can perform include error correction,
pattern analysis, and data reconstruction. Relationships between
discrete or continuous-valued inputs and outputs can be determined;
such a mapping can be an abstract spatial, functional, or temporal one.
In order to build a computational model using a neural network
architecture, decisions have to be made about the types of processing
units (McCulloch-Pitts or leaky integrators), their state of activation
(continuous or discrete), what output function to use, the pattern of
connectivity (feedforward or recurrent; number of layers), type of
information coding, what propagation rule to use, what activation rule
to use (linear or logistic), what learning rule to use (if adaptive), and
the environment. Most commonly used in the literature today are
feedforward, numerical mapping, gradient descent-trained networks.
This is because: 1) most computational problems can be phrased as
mappings from one real-valued array to another; 2) it is often the case
that the problem is posed as a set of sample values and solutions
(rather than as an algorithm); 3) feedforward networks are the most
easily understood and trained; and 4) gradient descent is a very
general, iterative optimization process.
While no one claims that the various algorithms, learning rules,
assumptions, and simplifications used in artificial neural networks are
biologically plausible, the argument for exploring such computations
is that they are neurally-inspired. In the long run, they have much
more potential for providing an understanding of the fundamental
processing going on in the central nervous system than does the
conventional computer metaphor (of static memory, separate from
processing) that has existed for the last 40 years.