Dimitri Petritis
THERMODYNAMIC FORMALISM OF NEURAL COMPUTING
(267K, 51 pages, uuencoded postscript)

ABSTRACT.  Neural networks are systems of interconnected processors mimicking
some of the brain functions. 
After a rapid overview of neural computing, the thermodynamic formalism of 
the learning procedure is introduced. Besides its use in introducing 
efficient stochastic learning algorithms, it gives an insight in terms of 
information theory. Main emphasis is given in the information restitution
process; stochastic evolution is used as the starting point for introducing 
statistical mechanics of associative memory. Instead of formulating 
problems in their most general setting, it is preferred stating precise 
results on specific models. 
In this report are mainly presented those features that are relevant when
the neural net becomes very large.
A survey of the most recent results is given and the main open problems 
are pointed out.