Method for extracting spatial knowledge from an input signal...

Data processing: artificial intelligence – Machine learning

Reexamination Certificate

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C706S022000, C706S025000, C706S027000, C706S030000, C382S158000, C382S159000

Reexamination Certificate

active

08041651

ABSTRACT:
A processor architecture for a learning machine is presented which uses a massive array of processing elements having local, recurrent connections to form global associations between functions defined on manifolds. Associations between these functions provide the basis for learning cause-and-effect relationships involving vision, audition, tactile sensation and kinetic motion. Two arbitrary input signals hold each other in place in a manifold association processor and form the basis of short-term memory.

REFERENCES:
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Ritter, Recursion and Feedback in Image Algebra [online], 1990 [retrieved on Jun. 3, 2011]. Retrieved from the Internet:< URL:http://spiedigitallibrary.org/proceedings/resource/2/ psisdg/1406/1/74—1>.
Humpert, Bidirectional Associative Memory With Several Patterns [online], 1990 [retrieved on Jun. 3, 2011]. Retrieved from the Internet< URL:http://ieeexplore.ieee.org/xpls/abs—all.jsp?arnumber= 137657>.
Greer, A Unified System of Computational Manifolds [online], 2003 [retrieved on Jun. 3, 2011]. Retrieved from the Internet<URL:http://www.gmanif.com/pubs/TR-CIS-0602-03.pdf>.
Greer, An Image Association Model of the Brodmann Areas [online], 2007 [retrieved on Jun. 3, 2011]. Retrieved from the Internet:<URL:http://ieeexplore.ieee.org/xpls/abs—all.jsp?arnumber=4341934>. (Date for Greer'03).

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