Neural Networks and Neuroscience-Inspired Computer Vision
Venue
Current Biology, vol. 24 (2014), pp. 921-929
Publication Year
2014
Authors
David Cox, Tom Dean
BibTeX
Abstract
Brains are, at a fundamental level, biological computing machines. They transform a
torrent of complex and ambiguous sensory information into coherent thought and
action, allowing an organism to perceive and model its environment, synthesize and
make decisions from disparate streams of information, and adapt to a changing
environment. Against this backdrop, it is perhaps not surprising that computer
science, the science of building artificial computational systems, has long looked
to biology for inspiration. However, while the opportunities for cross-pollination
between neuroscience and computer science are great, the road to achieving
brain-like algorithms has been long and rocky. Here, we review the historical
connections between neuroscience and computer science, and we look forward to a new
era of potential collaboration, enabled by recent rapid advances in both
biologically-inspired computer vision and in experimental neuroscience methods. In
particular, we explore where neuroscience-inspired algorithms have succeeded, where
they still fail, and we identify areas where deeper connections are likely to be
fruitful.
