Brain-Like Approximate Reasoning

Humans can easily recognize objects as complex as faces even if they have not seen them in such conditions before. We would like to find out computational basis of this ability. As an example of our approach we use the neurophysiological data from the visual system. In the retina and thalamus simple light spots are classified, in V1 - oriented lines and in V4 - simple shapes. The feedforward (FF) pathways by extracting above attributes from the object form hypotheses. The feedback (FB) pathways play different roles – they form predictions. In each area structure related predictions are tested against hypotheses. We formulate a theory in which different visual stimuli are described through their condition attributes. Responses in LGN, V1, and V4 neurons to different stimuli are divided into several ranges and are treated as decision attributes. Applying rough set theory (Pawlak, 1991 –[1]) we have divided our stimuli into equivalent classes in different brain areas. We propose that relationships between decision rules in each area are determined in two ways: by different logic of FF and FB pathways: FF pathways gather a huge number of possible objects attributes together using logical “AND” (drivers), and FB pathways choose the right one mainly by logical “OR” (modulators).