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Dynamic Model of visual Recognition Predicts Neural Response Properties in the Visual Cortex
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@Article{Rao:NC95,
  author = 	 {Rajesh P.N. Rao and Dana H. Ballard},
  title = 	 {Dynamic Model of visual Recognition Predicts Neural Response Properties in the Visual Cortex},
  journal = 	 {Neural Computation},
  year = 	 {1995},
  abstract =     { 
The responses of visual cortical neurons during fixation tasks can be significantly modulated by stimuli
from beyond the classical receptive field. Modulatory effects in neural responses have also been recently
reported in a task where a monkey freely views a natural scene. In this paper, we describe a hierarchical
network model of visual recognition that explains these experimental observations by using a form of
the extended Kalman filter as given by the Minimum Description Length (MDL) principle. The model
dynamically combines input­driven bottom­up signals with expectation­driven top­down signals to predict
current recognition state. Synaptic weights in the model are adapted in a Hebbian manner according to a
learning rule also derived from the MDL principle. The resulting prediction/learning scheme can be viewed
as implementing a form of the Expectation­Maximization (EM) algorithm. The architecture of the model
posits an active computational role for the reciprocal connections between adjoining visual cortical areas in
determining neural response properties. In particular, the model demonstrates the possible role of feedback
from higher cortical areas in mediating neurophysiological effects due to stimuli from beyond the classical
receptive field. Simulations of the model are provided that help explain the experimental observations
regarding neural responses in both free viewing and fixating conditions.  }
}