Introduction

The methods and data presented in Chapters 2,3, and 4 are essential for assessing the feasibility of neuronal circuits composed of a small number of elements. However, the neural network models presented in recent years have demonstrated that computations can also be carried out by massive interactions among a multitude of neurons. This chapter offers an introduction to current trends in neural network modeling.

Modeling of neural networks has been carried out extensively in the past five years. Among the first attempt to build circuits that would compute were McCullouch and Pitts [1943], who showed how to compute logic predicates with neurons. They later constructed a neuronal circuit that recognized shapes, regardless of their position in the visual field [Pitts and McCulloch, 1947]. Subsequently there were many other attempts to construct computing circuits from neural-like elements [e.g., Wooldridge, 1979], but most of those attempts did not leave a lasting impression in the neurosciences. The original McCulloch and Pitts paper was difficult to follow, even for mathematicians [Palm, 1986b]; thus, adoption and development of their ideas by neurophysiologists did not follow. The image-recognizing circuit they developed had connectivities that, so far as we know, did not exist in the visual areas. That seems to have been the fate of most “neural computers” suggested in the past.