Invariant property of spatio-temporal feature maps using gated neuronal architecture

In this paper it is shown that the spatio-temporal signature generated for any input pattern on a topologically ordered feature map using a gated neuronal architecture is invariant over a neighbourhood of the input pattern provided the input patterns lie in the interior of the decision space and the regions of competition created by n-dimensional spatial grating function at any given spatial frequency are open. The spatio-temporal signature in a Gated Neuronal Architecture uniquely represents a collection of disjoint regions in the feature space. For pattern classification the labeling of the set of disjoint regions represented by the spatio-temporal signature is obtained by using Bayes conditional probabilities. Simulation results indicate improved performance