The neural architecture of the visual system with its unique and elaborate response properties in space, time and color provides us with all the features and richness of our visual world. The main goals of our research are: 1) to understand how the visual neural architecture is matched to the natural image and how it samples, codes and processes the different features of the image. 2) To identify the attributes of natural images that are most critical for coding in the visual system. Questions we ask are: What are the significant correlations and functional relations among the different image features? How do critical parameters such as image spatial distribution, contrast, wavelength content and local and global details affect visual image processing? How does visual processing match and adapt to dynamic changes in the image? How does the visual system extract image features such as spatial detail, motion and color? To investigate these questions, we apply quantitative analysis and simulation methods from image processing and neural networks that are rigorously based on known anatomy and physiology and explore the design principles and strategy underlying the visual system neural architecture.
