Learning complex cell invariance from natural videos: A plausibility proof

Abstract : One of the most striking features of the cortex is its ability to wire itself. Understanding how the visual cortex wires up through development and how visual experience refines connections into adulthood is a key question for Neuroscience. While computational models of the visual cortex are becoming increasingly detailed, the question of how such architecture could self-organize through visual experience is often overlooked. Here we focus on the class of hierarchical feed-forward models of the ventral stream of the visual cortex, which extend the classical simple-to-complex cells model by Hubel and Wiesel to extra-striate areas, and have been shown to account for a host of experimental data. Such models assume two functional classes of simple and complex cells with specific predictions about their respective wiring and resulting functionalities. In these networks, the issue of learning, especially for complex cells, is perhaps the least well understood. In fact, in most of these models, the connectivity between simple and complex cells is not learned but rather hard-wired. Several algorithms have been proposed for learning invariances at the complex cell level based on a trace rule to exploit the temporal continuity of sequences of natural images.