Redundancy reduction as the basis for visual signal processing

An environmentally driven, self-organizing principle for encoding sensory messages is proposed, based on the need to learn their statistical properties. Optimal encodings are found for two cases: First, for linear maps the optimal transformation eliminates pairwise correlations between input `pixels.' This solution is applied to predict the retinal transform based on the autocorrelator for natural scenes. Second, when the input `images' consist of a set of weakly coupled, local `bound states,' then a series of non-linear maps is found which optimally segments the input. This is demonstrated by using it to efficiently learn, without supervision, the statistics of English text.