We present a realistic neural network - the canonical cortical module - built on basic principles of cortical organization. These principles are: opponent cells principle, canonical cortical circuit principle and modular principle. When applied to visual images, the network explains orientational and spatial frequency filtering functions of neurons in the striate cortex. Two patterns of joint distribution of opponent cells in the inhibitory cortical layer are presented: pinwheel and circular. These two patterns provide two Gestalt descriptions of local visual image: circle-ness and cross-ness. These modules were shown to have a power for shape detection and texture discrimination. they also provide an enhancement of signal- to-noise ratio of input images. Being modality independent, the canonical cortical module seems to be a good tool for bio-fusion for intelligent system control.

Date Published: 12 March 1999
PDF: 8 pages
Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (12 March 1999); doi: 10.1117/12.341364

Published in SPIE Proceedings Vol. 3719:
Sensor Fusion: Architectures, Algorithms, and Applications III
Belur V. Dasarathy, Editor(s)