A neural network theory of proportional analogy-making

Abstract

A neural network model that can simulate the learning of some simple proportional analogies is presented. These analogies include, for example, (a) red-square:red-circle∷yellow-square:?, (b) apple:red∷banana: ?, (c) a:b∷c:?. Underlying the development of this network is a theory for how the brain learns the nature of association between pairs of concepts. Traditional Hebbian learning of associations is necessary for this process but not sufficient. This is because it simply says, for example, that the concepts “apple” and “red” have been associated, but says nothing about the nature of this relationship. The types of context-dependent interlevel connections in the network suggest a semilocal type of learning that in some manner involves association among more than two nodes or neurons at once. Such connections have been called synaptic triads, and related to potential cell responses in the prefrontal cortex. Some additional types of connections are suggested by the problem of modeling analogies. These types of connections have not yet been verified by brain imaging, but the work herein suggests that they may occur and, possibly, be made and broken quickly in the course of working memory encoding. These working memory connections are referred to as differential, delayed and anti-Hebbian connections. In these connections, one can learn transitions such as “keep red the same”; “change red to yellow”; “turn off red”; “turn on yellow,” and so forth. Also, included in the network is a kind of weight transport so that, for example, red to red can be transported to a different instance of color, such as yellow to yellow. The network instantiation developed here, based on common connectionist building blocks such as associative learning, competition, and adaptive resonance, along with additional principles suggested by analogy data, is a step toward a theory of interactions among several brain areas to develop and learn meaningful relationships between concepts.