Analog electronic neural network circuits

Abstract

It is argued that the large interconnectivity and the precision required in neural network models present novel opportunities for analog computing. Analog circuits for a wide variety of problems such as pattern matching, optimization, and learning have been proposed and a few have been built. Most of the circuits built so far are relatively small, exploratory designs. Circuits implementing several different neural algorithms, namely, template matching, associative memory, learning, and two-dimensional resistor networks inspired by the architecture of the retina are discussed. The most mature circuits are those for template matching, and chips performing this function are now being applied to pattern-recognition problems. Examples of analog implementation are examined.< >