The scientific theme of the conference is the encoding and processing of complex natural environmental stimuli by biological systems. The conference is intended to bring together neuroscientists and psychologists interested in understanding the representation and use of such information in the brain, with researchers from engineering, physics, mathematics and other quantitative disciplines who are interested in characterizing the structure of natural sensory signals (images, sounds, odors, etc).

Traditionally, neuroscientists and psychologists have used relatively simple, "controlled" stimuli (e.g., sine-wave gratings, pure tones, spots, clicks, taps or vibration to the skin, etc) to probe the response properties of sensory neurons and characterize perceptual abilities. This approach has been successful at determining basic stimulus selectivities of neurons and in elucidating circuit mechanisms. But processing in neural circuits is highly non-linear and adaptive, so circuits can react quite differently to natural stimuli and experiments using artificial stimuli are often of limited usefulness in predicting these responses. Neural circuits are presumably encoding or extracting certain spatio-temporal patterns from the input stream, but one cannot discover the nature of these patterns by simply probing with one element at a time drawn from a limited set. Since the process of evolution has presumably built sensory systems in order to process signals from the natural environment, it seems natural that these signals can provide clues, or even be used as stimuli, in the study of sensory systems.

An interdisciplinary field has emerged in recent years that has focused on a set of inter-related questions: What are the forms of structure that tend to occur in the environment; how do we characterize these mathematically/statistically? How are these forms of structure encoded and represented by neurons in the brain? This requires not only drawing upon the methods of neurophysiology, neuroethology, and psychophysics, but also developing mathematical theories and building computational models.