NEURAL BASIS OF MONOCULAR AND BINOCULAR TEXTURE ANALYSIS

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The long-term objective of this research is to determine how the visual part of the cerebral cortex analyzes information that enter through the two eyes, and transforms it into a form useful for perception. The proposed project approaches the problem of visual information processing using electrophysiological recording methods combined with elaborate visual stimulation and analysis techniques. The methods are applied to the problem of how cortical neurons analyze visual texture information and integrate it with that from brightness cues. A hypothesis is proposed that texture-sensitive neurons represent third and fourth stages of hierarchical processing in the visual cortex. To test this hypothesis, a cascaded model of texture cells is constructed based on the notion of "texture receptive field." The following specific aims will be addressed. (1) A model of texture cells will be developed based on a cascaded organization in which the identical neural computation is performed twice serially. Computational studies will examine how well this model predicts known properties of texture perception and cell responses. (2) The cascade model will be tested neurophysiologically by measuring texture receptive fields of visual neurons and comparing them to predictions of the model. Specific neural connections predicted by the model will be sought by analyzing activities of simultaneously recorded groups of neurons. (3) Neural responses to mixed texture and brightness-defined stimuli will be studied to determine how texture neurons integrate these two cues. (4) By stimulating texture neurons simultaneously through the two eyes, the roles that texture information plays in binocular vision and stereopsis will be examined. (5) Developmental time course of neural texture responses will be studied in young animals. These studies will yield a functional schematic diagram of the texture- brightness processing pathway of the visual cortex. Such detailed description of the brain circuitry will be valuable in diagnosing and understanding visual disorders.