Ted Hesselroth and Klaus Schulten.
The dynamics of image processing by feature maps in the primary
visual cortex.
arXiv:q-bio.NC/0505010v1, 2005.
HESS2005-KS
The operational characteristics of a linear neural network image
processing system based on the brain’s vision system are investigated. The final
stage of the network consists of edge detectors of various orienations arranged in
a feature map, corresponding to the primary visual cortex, or V1. The lateral
geniculate nucleus is modeled as a preprocessing stage. Excitatory forward and
inhibitory backward connections exist between the LGN and V1. By a method of
reconstructing the input images in terms of V1 activities, the simulations show that
images can be faithfully represented in V1 by the proposed network. The signal-
to-noise ratio of the image is improved by the representation, and compression
ratios of well over two-hundred are possible. Lateral interacations between V1
neurons sharpen their orientational tuning. We further study the dynamics of the
processing, showing that the rate of decrease of the error of the representation is
maximized for the receptive fields used, and we develop a Fokker-Planck equation
for a more detailed prediction of the error value vs. time. Finally, we show how
the eigenvalues of the covariance matrix of the inputs can be employed to predict
the rate of error decrease.
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