training in correlated noise

[Urbashi Mitra]

i'm interested in any work that may have been done on the
convergence properties (i.e. will the learning algorithm
converge?) of neural networks (perceptron models)
that are trained with *correlated noise* present.  the individual
training (desired) data are independent from time sample to time
sample, but the noise is not.  the noise is, however, m-dependent,i.e.
noise samples more than m time samples apart, *are* independent.

i'm looking for work along the lines of john shynk's (ece dept. ucsb)
that is similar to adaptive filtering results, but ANYTHING, in any form
in this area would be greatly appreciated.

thanks,

urbashi mitra