Thesis announcement

[john kolen]

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Ftp-site: cis.ohio-state.edu
Ftp-directory:  pub/neuroprose/Thesis/kolen.thesis.ps.Z
PrintedPages: 191


     Exploring the Computational Capabilities of Recurrent Neural Networks

				 John F. Kolen


While many researchers have successfully organized neural networks into
structures displaying universal computational capability, most have ignored the
more daunting endeavor of identifying ongoing computation, or information
processing, as it occurs.  My thesis addresses this problem as it relates to
the understanding of the information processing capabilities of recurrent
neural networks.  I have isolated three important facets of recurrent neural
networks that contribute to their computational power: internal dynamics, input
modulation, and output generation.  Theories of dynamical systems and iterated
function systems have proven crucial in developing an understanding of these
facets.  With respect to my original question of identifying ongoing
computation, the evidence suggests that the any observed computational powers
of neural networks arise not from the network itself, but our application of it
as a symbol processing device.  If we consider recurrent neural networks as the
a cognitive e. coli, this dissertation is a step toward understanding the
nature cognition and intelligence.




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