Bringing Knowing-When and Knowing-What Together: Periodically Tuned Categorization and Category-Based Timing Modeled with the Recurrent Oscillatory Self-Organizing Map (ROSOM)

Authors:
Mauri Kaipainen;Pasi Karhu
Affiliations:
University of Helsinki, Cognitive Science Program, PL 13, 00014 Helsingin yliopisto, Finland, (E-mail: mauri.kaipainen@helsinki.fi pasi.karhu@helsinki.fi);University of Helsinki, Cognitive Science Program, PL 13, 00014 Helsingin yliopisto, Finland, (E-mail: mauri.kaipainen@helsinki.fi pasi.karhu@helsinki.fi)
Venue:
Minds and Machines
Year:
2000

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Abstract

The study addresses the cyclically temporal aspect of sequence recognition, storage and recall using the Recurrent Oscillatory Self-Organizing Map (ROSOM), first introduced by Kaipainen, Papadopoulos and Karhu (1997). The unique solution of the network is that oscillatory States are assigned to network units, corresponding to their `readiness-to-fire'. The ROSOM is a categorizer, a temporal sequence storage system and a periodicity detector designed for use in an ambiguous cyclically repetitive environment. As its external input, the model accepts a multidimensional stream of environment-describing feature configurations with implicit periodicities. The output of the model is one or a few closed cycles abstracted from such a stream, mapped as trajectories on a two-dimensional sheet with an organization reminiscent of multi-dimensional scaling. The model's capabilities are explored with a variety of workbench data.