SPATIOTEMPORAL LINEAR SIMPLE-CELL MODELS BASED ON TEMPORAL COHERENCE AND INDEPENDENT COMPONENT ANALYSIS

摘要

The search for computational principles that underlie the functionality of different cortical areas is a fundamental scientific challenge. In the case of sensory areas, one approach is to examine how the statistical properties of natural stimuli - in the case of vision, natural images and image sequences - are related to the response properties of neurons. For simple cells, located in V1, the most prominent computational theories linking neural properties and stimulus statistics are temporal coherence and independent component analysis. For these theories, the case of spatial linear cell models has been studied in a number of recent publications, but the case of spatiotemporal models has received fairly little attention. Here we examine the spatiotemporal case by applying the theories to natural image sequence data, and by analyzing the obtained results quantitatively. We compare the properties of the spatiotemporal linear cell models learned with the methods against each other, and against parameters measured from real visual systems.