Description:
Despite a
promising beginning, pattern recognition software using a “neural network”
approach in general has encountered serious roadblocks limiting the rate of
progress. Traditional methods cannot “attend” and “recognize” using the same
network structure. For example, a system can find interesting regions, but
cannot recognize objects: a system can only recognize objects that have already
been segmented and separated from their natural background.
A crucial
challenge is to handle the demands for bottom-up “attention” to the presence of
given objects in a particular class and efficiently couple that with a top-down
knowledge-based “recognition” of the selected objects. This results in efficient
pattern recognition. Such a combined top-down and bottom-up architecture is
necessary for the kind of information processing that rapidly distinguishes
“friend from foe,” spots a looming pothole while ignoring other information, or
that analyzes information relating to the relative position of words and
features as opposed to merely identifying a string of information.
Michigan
State University’s software algorithm, while of the neural network variety, uses
no back propagation (a common feature of neural net architectures). The software
is massively parallel, uses a Hebbian Learning method, and mimics the modularity
of the brain, including the compartmentalization (in space and function) of
various cognitive functions (e.g., auditory, decision making, and positional
location). The algorithm incorporates several potential
breakthroughs:
* A model
of how the human brain focuses attention on designated objects in space and
time, allowing the algorithm to zero in on subjects of interest (e.g., a human
running in front of a car or a looming pothole) and effectively ignoring all
background information (e.g., houses, shadows, and so on).
* A
combination of a “top down” and “bottom up” architecture loosely mimicking how
the brain handles information processing in the cerebral cortex. It is a system
for putting the modular pieces together.
Benefits
* High
effectiveness: The software has already been tested on tasks, including
object recognition, and has been found to be superior to existing software
alternatives.
* Massive parallelism: The software
architecture is compatible with, and runs most effectively with, massively
parallel chips.
* Generality of applications: The
software incorporates a generalized information processing architecture loosely
modeled on the modular architecture and hierarchical information processing of
the human brain.
Applications
This
invention has applications in:
* text
understanding
* object
and situation recognition (e.g., steering, braking, and collision avoidance in
automotive applications)
* defense
applications requiring object recognition and situation assessment
*
advanced search engines providing the users with an experience like that of
talking with a knowledgeable friend
IP Protection
Status
Patent
pending
