Important examples involve first passage distributions and maxima of random fields.
It is also autoassociative if it outputs the actual character inferred rather than just identifies it. However, many of the standard associative memories of neural net theory seem to operate rather differently, and it is argued that the task they implicitly attempt is that of coping with fading data , i.e. of forming an inference from the data even as its memory of that data degrades. Rather simple statistical considerations lead to a net with optimality properties and a simple interpretation. In the particular case when composite traces can be formed by linear superposition, this net exhibits a structure which shows striking parallels with some of the anatomy of the olfactory system. This seminar is complete in itself, but the ideas will be developed in a different direction in the seminar on 12th February.