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    A temporal attractor framework for the development of analogical completion

    Leech, R. and Mareschal, Denis and Cooper, R. (2004) A temporal attractor framework for the development of analogical completion. In: Howe, P. and Labouise, C. (eds.) Connectionist models of cognition and perception II: Proceedings of the Eighth Neural Computation and Psychology Workshop (NCPW8). London, UK: World Scientific, pp. 201-210. ISBN 9789812388056.

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    Abstract

    The current model is an adaptation of [1], extending it to draw more complex and abstract analogies. Units are connected by two types of modifiable connections: fast connections which transmit the current activation of the units and slow connections which implement a delay transmitting an earlier activation state of the network. The fast connections drive the network into attractor states corresponding to objects. The slow connections implement transformations between states by pushing the network out of its stable state and into another attractor basin. The fast and slow connections work together to move the network from one attractor state to another in an ordered way. Since the network can learn transformations between more than two objects we suggest how the network could draw analogies involving more than two objects.

    Metadata

    Item Type: Book Section
    School: Birkbeck Schools and Departments > School of Science > Psychological Sciences
    Depositing User: Sarah Hall
    Date Deposited: 17 Sep 2019 08:09
    Last Modified: 17 Sep 2019 08:09
    URI: http://eprints.bbk.ac.uk/id/eprint/28974

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