Selection of Parameters forNeural Net Simulations[22]
R. K. Overton
Autonetics Research CenterAnaheim, California
Research of high quality has been presented at this Symposium. Of particular interest to me were the reports of the Aeronutronic group and the Librascope group. The Aeronutronic group was commendably systematic in its investigations of different arrangements of linear threshold elements, and the Librascope data, presenting the effects of attaching different values to the parameters of simulated neurons, are both systematic and interesting.
Unfortunately, however, interest in such research can obscure a more fundamental question which seems to merit study. That question concerns the parameters, or attributes, which describe the simulated neuron. Specifically, which parameters or attributes should be selected for simulation? (For example, should a period of supernormal sensitivity be simulated following an absolutely refractory period?)
Some selection obviously has to be made. Librascope, which is trying to simulate neurons more or less faithfully, plans to build a net of ten simulated neurons. In contrast, General Dynamics/Fort Worth, with roughly the same degree of effort, is working with 3900 unfaithfully-simulated neurons. This comparison is not a criticism of either group; the Librascope team has simply selected many more parameters for simulation than has the General Dynamics group. Each can make the selections it prefers, because the parameters of real neurons which are necessary and sufficient for learning have not been exhaustively identified.
From the point of view of one whose interests include real neurons, this lack of identification is unfortunate. I once wrote a book which included some guesses about the essential attributes of neurons. Since that time, many neuron simulation programs have been written. But these programs, although interesting and worthwhile in their own right, have done little to answer the question of the necessary parameters. That is, they do not make much better guesses possible. And yet better guesses would also make for more “intelligent” machines.