Computational design and nonlinear dynamics of a
recurrent network model of the primary visual cortex
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
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
and connection structures.
13/8, p. 1749-1780
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