DocumentCode :
3393015
Title :
Cortical columns: Building blocks for intelligent systems
Author :
Hashmi, Atif G. ; Lipasti, Mikko H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Wisconsin - Madison, Madison, WI
fYear :
2009
fDate :
March 30 2009-April 2 2009
Firstpage :
21
Lastpage :
28
Abstract :
The neocortex appears to be a very efficient, uniformly structured, and hierarchical computational system. Researchers have made significant efforts to model intelligent systems that mimic these neocortical properties to perform a broad variety of pattern recognition and learning tasks. Unfortunately, many of these systems have drifted away from their cortical origins and incorporate or rely on attributes and algorithms that are not biologically plausible. In contrast, this paper describes a model for an intelligent system that is motivated by the properties of cortical columns, which can be viewed as the basic functional unit of the neocortex. Our model extends predictability minimization to mimic the behavior of cortical columns and incorporates neocortical properties such as hierarchy, structural uniformity, and plasticity, and enables adaptive, hierarchical independent feature detection. Initial results for an unsupervised learning task - identifying independent features in image data - are quite promising, both in a single-level and a hierarchical organization modeled after the visual cortex. The model is also able to forget learned patterns that no longer appear in the dataset, demonstrating its adaptivity, resilience, and stability under changing input conditions.
Keywords :
image recognition; neural nets; unsupervised learning; building blocks; cortical columns; hierarchical independent feature detection; intelligent systems; neocortex; neocortical properties; pattern recognition; predictability minimization; structural uniformity; unsupervised learning; Biological system modeling; Biology computing; Brain modeling; Computational intelligence; Computer vision; Intelligent structures; Intelligent systems; Pattern recognition; Predictive models; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Multimedia Signal and Vision Processing, 2009. CIMSVP '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2771-0
Type :
conf
DOI :
10.1109/CIMSVP.2009.4925643
Filename :
4925643
Link To Document :
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