Inverse Problems N eural nets are useful for pattern recognition problems. Neural nets, however, are only a faint image of real neural systems. Presumably the complexity seen in each of the hundreds of brain areas is needed to carry out specific functions, such as vision and object discrimination. A great deal is known about the structure of brain nuclei in the sensory systems, i.e., vision, hearing, touch, smell. This is the level that is believed to hold the secret of how the brain performs such complex functions as vision and audition and motor control, and it is also the level at which computational models may make their greatest contribution.
Models serve several purposes.
They provide a mechanism for uniting in a single operation a great deal of diverse information about neural systems. In addition, they allow for testing the self-consistency of such data under dynamic conditions. Further, they allow us to determine the relative contributions of various pathways and processes, and to test theories about the relationship between function and structure. Finally, they can make predictions which can be tested experimentally and thereby guide as well as interpret experimental research. The need for realistic computational models of neural circuitry is growing increasingly apparent.