Self-Organized Formation of a Set of Scaling Filters and their
Neighbouring Connections
T. Rozgonyi, L. Balázs, T. Fomin and A. Lõrincz
Biological Cybernetics
75,
37--47
(1996)
Abstract
A set of scaling feedforward filters is developed in an unsupervised
way via inputting pixel discretized extended objects into a
winner-take-all artificial neural network. The system discretizes the
input space by both position and size. Depending on the distribution of
input samples and below a certain number of neurons the spatial filters
may form groups of similar filter sizes with each group covering the whole
input space in a quasi-uniform fashion. Thus a multi-discretizing system
may be formed. Interneural connections of scaling filters are also
developed with the help of extended objects. It is shown both
theoretically and with the help of numerical simulation that competitive
Hebbian learning is suitable for defining neighbours for the
multi-discretizing system. Taking into account the neighbouring
connections between filters of similar sizes only, i.e. within the groups
of filters the system may be considered as a self-organizing multi-grid
system.
