Optimal Algorithmic Complexity of Fuzzy ART
Neural Processing Letters
Factor Analysis Using Delta-Rule Wake-Sleep Learning
Neural Computation
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Recent neurophysiological experiments appear to indicate that the responses of visual cortical neurons in a monkey freely viewing a natural scene can sometimes differ substantially from those obtained when the same image subregions are flashed during a conventional fixation task. These new findings attain significance from the fact that neurophysiological research in the past has been based predominantly on cell recordings obtained during fixation tasks, under the assumption that these data would be useful in predicting responses in more general situations. We describe a hierarchical model of visual memory that reconciles the two differing experimental results mentioned above by predicting neural responses in both fixating and free-viewing conditions. The model dynamically combines input-driven bottom-up signals with expectation-driven top-down signals to achieve optimal estimation of current state using a Kalman filter based framework. The architecture of the model posits a role for the reciprocal connections between adjoining visual cortical areas in determining neural response properties.