On the Prospects for Building a Working Model of the Visual Cortex
Venue
Proceedings of AAAI-07, MIT Press, Cambridge, Massachusetts (2007), pp. 1597-1600
Publication Year
2007
Authors
Thomas Dean, Glenn Carroll, Richard Washington
BibTeX
Abstract
Human-level visual performance has remained largely beyond the reach of engineered
systems despite decades of research and significant advances in problem
formulation, algorithms and computing power. We posit that significant progress can
be made by combining existing technologies from machine vision, insights from
theoretical neuroscience and large-scale distributed computing. Such claims have
been made before and so it is quite reasonable to ask what are the new ideas we
bring to the table that might make a difference this time around. From a
theoretical standpoint, our primary point of departure from current practice is our
reliance on exploiting time in order to turn an otherwise intractable unsupervised
problem into a locally semi-supervised, and plausibly tractable, learning problem.
From a pragmatic perspective, our system architecture follows what we know of
cortical neuroanatomy and provides a solid foundation for scalable hierarchical
inference. This combination of features provides the framework for implementing a
wide range of robust object-recognition capabilities.
