ation were brushed aside by the neurobiologist community as abstract conceptions that bore little relationship to reality. This was partially due to the over-excitement in the 1950s and 1960s for networks that could recognize some patterns, but were limited in their learning abilities because of hardware limitations. In the 1990's computer simulations of brain functions are gaining respect as the simulations increase their abilities to predict the behavior of the nervous system. This respect is illustrated by the fact that many neurobiologists are increasingly moving toward neural network type simulations. One such neurobiologist, Sejnowski, introduced a three-layer net which has made some excellent predictions about how biological systems behave. Figure 2 illustrates this network consisting of three layers, in which a middle layer of units connects the input and output layers. When the network is given an input, it sends signals through the middle layer which checks for correct output. An algorithm used in the middle layer reduces errors by strengthening or weakening connections in the network. This system, in which the system learns to adapt to the changing conditions, is called back-propagation. The value of Sejnowski's network is illustrated by an experiment by Richard Andersen at the Massachusetts Institute of Technology. Andersens team spent years researching the neurons monkeys use to locate an object in space (Dreyfus and Dreyfus 42-61). Anderson decided to use a neural network to replicate the findings from their research. They "trained" the neural network to locate objects by retina and eye position, then observed the middle layer to see how it responded to the input. The result was nearly identical to what they found in their experiments with monkeys. Computer-Synthesized Senses Visual Recognition The ability of a computer to distinguish one custom...
