Paper on computational design and nonlinear dynamics of a V1 model

[Dr Zhaoping Li]

Title:  Computational design and nonlinear dynamics
        of a recurrent network model of the primary visual cortex

Author: Zhaoping Li

Accepted for publication in Neural Computation


available at:
       http://www.gatsby.ucl.ac.uk/~zhaoping/preattentivevision.html


Abstract:

Recurrent interactions in the primary visual cortex makes its output a
complex nonlinear transform of its input. This transform serves
pre-attentive visual segmentation, i.e., autonomously processing 
visual inputs to give outputs that selectively emphasize certain 
features for segmentation. An analytical understanding of the 
nonlinear dynamics of the recurrent neural circuit is
essential to harness its computational power. We derive requirements 
on the neural architecture, components, and connection weights of 
a biologically plausible model of the cortex such that region
segmentation, figure-ground segregation, and contour enhancement can 
be achieved simultaneously. In addition, we analyze the conditions
governing neural oscillations, illusory contours, and the absence of
visual hallucinations. Many of our analytical techniques can be applied 
to other recurrent networks with translation invariant neural 
connection structures.