Novelty of Neural Net Approach

[TERU]

.. a deeper methematical study of the nervous system ... will
affect our understanding of the aspects of mathematics itself
that are involved.  - John von Neumann, The Computer and the brain.


In the following is my attempt to list the differences between
conventional systems and neural network (or connectionist) systems.
The intent is to point out the novelty of neural network approach.
Although simplistically stated, it may serve as a seed for discussion.
Your comments, opinions and criticism are most welcomed.

(The word "conventional" is used here in a very loose sense only to highlight
the characteristics of neural net approach.)

   Conventional Systems                    Neural Net (Connectionist) Systems

1. linear ( in analog)                     pseudolinear
   logical ( in digital)                   softlogic

2. try to eliminate noise                  try to utilize noise
   (suffer from noise)                     (immune to noise)

3. usually need reliable                   work with unreliable
   components                              components

4. need designed fault-                    have built-in fault-tolerance
   tolerance

5. emphasize economy of                    emphasize redundancy
   operation

6. want sharp switches                     use dull switches
   (for digital)                           (with sigmoid function)

7. usually operate synchronously           may work asynchronously easily
   under a global clock                    without a global clock

8. have complex structure                  have rather uniform structure

9. need to decompose (if possible)         have built-in paralellism
   the process for parallel processing

10. designed or programmed with            learn from examples;
   rules specifying the system             may self-organize
   behavior

In designing a system, engineers have been working very hard to linearlize
the system, eliminate noises, produce reliable components, etc.
The issues listed above are very important ones in system design today.
It is interesting to see that neural network systems offer an alternative
approach in every issue listed above. In some cases, the approach is
opposite in its direction.

                                               Teru Homma
                                               Univ. of Washington, FT-10
                                               Seattle, WA 98195