TITLE:
Concept Learning in Neuromorphic Vision Systems: What Can We Learn from Insects?
AUTHORS:
Fredrik Sandin, Asad I. Khan, Adrian G. Dyer, Anang Hudaya M. Amin, Giacomo Indiveri, Elisabetta Chicca, Evgeny Osipov
KEYWORDS:
Concept Learning, Computer Vision, Computer Architecture, Neuromorphic Engineering, Insect
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.7 No.5,
May
13,
2014
ABSTRACT:
Vision systems that enable
collision avoidance, localization and navigation in complex and uncertain
environments are common in biology, but are extremely challenging to mimic in
artificial electronic systems, in particular when size and power limitations
apply. The development of neuromorphic electronic systems implementing models
of biological sensory-motor systems in silicon is one promising approach to addressing
these challenges. Concept learning is a central part of animal cognition that
enables appropriate motor response in novel situations by generalization of
former experience, possibly from a few examples. These aspects make concept
learning a challenging and important problem. Learning methods in computer
vision are typically inspired by mammals, but recent studies of insects
motivate an interesting complementary research direction. There are several
remarkable results showing that honeybees can learn to master abstract concepts,
providing a road map for future work to allow direct comparisons between
bio-inspired computing architectures and information processing in miniaturized
“real” brains. Considering that the brain of a bee has less than 0.01% as many
neurons as a human brain, the task to infer a minimal architecture and
mechanism of concept learning from studies of bees appears well motivated. The
relatively low complexity of insect sensory-motor systems makes them an
interesting model for the further development of bio-inspired computing
architectures, in particular for resource-constrained applications such as
miniature robots, wireless sensors and handheld or wearable devices. Work in
that direction is a natural step towards understanding and making use of
prototype circuits for concept learning, which eventually may also help us to
understand the more complex learning circuits of the human brain. By adapting
concept learning mechanisms to a polymorphic computing framework we could
possibly create large-scale decentralized computer vision systems, for example
in the form of wireless sensor networks.