A neural network theory of proportional analogy-making

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...

• Type: Journal
• Year: 2000
• Where: NN
• Authors: Nilendu G. Jani, Daniel S. Levine
• Keywords: Context-dependent Interlevel Connections, Neural Network Model, Neural Networks, NN 2000, Simple Proportional Analogies