Novelty-dependent learning and topological mapping

Unsupervised topological ordering, similar to Kohonen’s (1982) Self-organizing feature map, was achieved in a connectionist module for competitive learning (a CALM Map) by internally regulating the learning rate and the size of the active neighborhood on the basis of input novelty. In this module winner-take-all competition and the 'activity bubble' are due to graded lateral inhibition between units. It tends to separate representations as far apart as possible, which leads to interpolation abilities and an absence of catastrophic interference when the interfering set of patterns forms an interpolated set of the initial data set. More than the Kohonen maps, these maps provide an opportunity for building psychologically and neurophysiologically motivated multimodular connectionist models. As an example, the dual pathway connectionist model for fear conditioning by Armony, Servan-Schreiber, Cohen, and LeDoux (1997) was rebuilt and extended with CALM maps. If the detection of novelty enhances memory encoding in a canonical circuit, such as the CALM map, this could explain the finding of large distributed networks for novelty detection (e.g. Knight & Scabini, 1998) in the brain. A self-organising connectionist map 2