New paper: How delays affect neural dynamics and learning

[Pierre F. Baldi]

FTP-host: archive.cis.ohio-state.edu
FTP-filename: /pub/neuroprose/baldi.delays1.ps.Z
FTP-filename: /pub/neuroprose/baldi.delays2.ps.Z

The following paper is available from the Ohio State neuroprose
archive. It is scheduled to appear in: IEEE Transactions on Neural Networks,
Vol. 5, 4, 626-635 (1994).

       How delays affect neural dynamics and learning

                   P. Baldi
                   JPL/Caltech

                   A. Atiya
                   Cairo University

email:  pfbaldi at juliet.caltech.edu
   or:  amir at csvax.cs.caltech.edu

We investigate the effects of delays on the dynamics and, in particular,
the oscillatory properties of simple artificial neural network models.
We treat in detail the case of ring networks, for which we derive
simple conditions for oscillating behavior, and several formulas to
predict the regions of bifurcation, the periods of the limit cycles and
the phases of the various neurons. These results in turn can be applied
to more complex architectures. In general, delays tend to increase the
period of oscillations and broaden the spectrum of possible frequencies,
in a quantifiable way. Theoretically predicted values are in excellent 
agreement with simulations. Adjustable delays are then proposed as one
additional mechanism through which neural systems could taylor their
own dynamics. Recurrent back-propagation learning equations are derived
for the adjustment of delays and other parameters in networks with delayed 
interactions and applications are briefly discussed.



Retrieve this paper by anonymous ftp from:
archive.cis.ohio-state.edu (128.146.8.52)
    in the /pub/neuroprose    directory

The name of the paper in this archive is:
    baldi.delays1.ps.Z          [24 pages]
    baldi.delays2.ps.Z (figures)[5 pages  pages]   

No hard copies available.