Analysis of Short Term Memories for Neural Networks

Time varying signals, natural or man made, carry information in their time structure. The problem is then one of devising methods and topologies (in the case of interest here, neural topologies) that explore information along time.This problem can be appropriately called temporal pattern recognition, as opposed to the more traditional case of static pattern recognition. In static pattern recognition an input is represented by a point in a space with dimensionality given by the number of signal features, while in temporal pattern recognition the inputs are sequence of features. These sequence of features can also be thought as a point but in a vector space of increasing dimensionality. Fortunately the recent history of the input signal is the one that bears more information to the decision making, so the effective dimensionality is finite but very large and unspecified a priori.