Article,
A unifying framework for natural image statistics: spatiotemporal activity bubbles
Neurocomputing, (2004)
Abstract
Recently, different models of the statistical structure of natural images (and sequences) have been proposed. Maximizing sparseness, or alternatively temporal coherence of linear filter outputs leads to the emergence of simple cell properties. Taking account of the basic dependencies of linear filter outputs enables modelling of complex cell and topographic properties as well. Here, we propose a unifying framework for all these statistical properties, based on the concept of spatiotemporal activity bubbles.
