Are artificial neural nets as smart as a rat?

Abstract

The origins of artificial neural networks are related to animal conditioning theory: both are forms of connectionist theory which in turn derives from the empiricist philosophers' principle of association. The parallel between animal learning and neural nets suggests that interaction between them should benefit both sides. The paper examines relationships between neural networks and a range of key ideas and findings in modern learning theory. It draws on studies of both conditioning and perceptual learning. The need to avoid simplistic comparisons is stressed. Not all issues which have aroused interest in learning theory are relevant to neural net research, because old and new connectionism diverge in some important ways. It is also necessary to recognize that many learning phenomena do not lend themselves to simulation by a single net. However, once these points are recognized, the findings of learning theory provide a range of well-defined challenges which are potentially important for those who are concerned with developing biologically plausible networks.