Topographical mapping forms of objects into gaussian elastic net

Abstract

A gaussian elastic net for modeling visual processing of object forms is proposed. The mechanism of the model is based on a cognitive fact that a form of a geometrical object is characterised by borders of the object. Gaussian receptive fields are used as templates which are activated by local detection of borders. The gaussian receptive fields are topological arranged such that a gross global shape of the object is mapped into the retina. By coupling gaussian receptive fields to the nodes of an elastic net, which adaptively matches the form of an object to the nodes, the gross global shape of an object is sharpened by the net as cortical processing. To achieve this, an energy function is defined. The energy function depends on the neighbourhood distances of the nodes and the activation of the gaussian receptive fields coupling to the nodes of the net. The object form is mapped into the net and sharpened by adaptively modifying the energy function to reach an optimal value. Simulation was done with an irregular object to show the processing of the net for an arbitrary object.