c. The neuron is capable of temporal and spatial integration. Many subthreshold stimuli arriving at the neuron from different sources, or at slightly different times, can add up to a sufficient level to fire the neuron.
d. Some inputs are excitatory, some are inhibitory.
e. There is a refractory period. Once fired, there is a subsequent period during which the neuron cannot be fired again, no matter how large the stimulus. This places an upper limit on the pulse rate of any particular neuron.
f. The neuron can learn. This property is conjectural in living neurons, since it appears that at the present time learning has not been clearly demonstrated in isolated living neurons. However, the learning property is basic to all self-organizing models.
Neuron models with the above characteristics have been built, although none seem to have incorporated all of them in a single model. [Harman (3)] at Bell Labs has built neuron models which have the characteristics (a) through (e), with which he has built extremely interesting devices which simulate portions of the peripheral neuron system.
Various attempts at learning elements have been made, perhaps best exemplified by those of [Widrow (4)]. These devices are capable of “learning,” but are static, and lack all the temporal characteristics listed in (a) through (e). Such devices can be used to deal with temporal patterns only by a mapping technique, in which a temporal pattern is converted to a spatial one.
Having listed which seem to be the important properties of a neuron, it is possible to synthesize a simple model which has all of them.
A number of input stimuli are fed to the neuron through a resistive summing network which establishes the threshold and accomplishes spatial integration. The voltage at the summing junction triggers a “one-shot” circuit, which, by its very nature, accomplishes pulse generation and exhibits temporal integration and a refractory period. The polarity of an individual input determines whether it shall be excitatory or inhibitory. This much of the circuitry is very similar to Harmon’s model.
Learning is postulated to take place in the following way: when the neuron fires, an outside influence (the environment, or a “trainer”) determines whether or not the result of firing was desirable or not. If it was desirable, the threshold of the neuron is lowered, making it easier to fire the next time. If the result was not desirable, the threshold is raised, making it more difficult for the neuron to fire the next time.
In a self-organizing system, many model neurons would be interconnected. A “punish-reward” (P-R) signal would be connected to all neurons in common. However, means would be provided for only those which have recently fired to be susceptible to the effects of the P-R signal. Therefore, only those which had taken part in a recent response are modified. This idea is due to [Stewart (5)], who applies it to his electrochemical devices instead of to an electronic device.