Biologically-inspired Digital Architecture for a Cortical Model of Orientation Selectivity

César Torres-Huitzil 1 Bernard Girau 2 Miguel Arias-Estrada 3
1 Polytechnic University of Victoria
2 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
3 INAOE - Instituto Nacional de Astrofísica, Óptica y Electrónica
Abstract : This paper presents a biologically inspired modular hardware implementation of a cortical model of orientation selectivity of the visual stimuli in the primary visual cortex targeted to a Field Programmable Gate Array (FPGA) device. The architecture mimics the functionality and organization of neurons through spatial Gabor-like filtering and the so-called cortical hypercolumnar organization. A systolic array and a suitable image addressing scheme are used to partially overcome the von Neumann bottleneck of monolithic memory organization in conventional microprocessor-based system by processing small and local amounts of sensory information (image tiles) in an incremental way. A real-time FPGA implementation is presented for 8 different orientations and aspects such as flexibility, scalability, performance and precision are discussed to show the plausibility of implementing biologically-inspired processing for early visual perception in digital devices.
Type de document :
Communication dans un congrès
Vera Kurkova and Roman Neruda and Jan Koutník. 18th International Conference on Artificial Neural Networks - ICANN 2008, 2008, Prague, Czech Republic. Springer, 5164, pp.188-197, 2008, Lecture Notes in Computer Science. 〈10.1007/978-3-540-87559-8_20〉
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https://hal.inria.fr/inria-00337391
Contributeur : Bernard Girau <>
Soumis le : jeudi 6 novembre 2008 - 18:38:41
Dernière modification le : mercredi 18 juillet 2018 - 20:12:01

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César Torres-Huitzil, Bernard Girau, Miguel Arias-Estrada. Biologically-inspired Digital Architecture for a Cortical Model of Orientation Selectivity. Vera Kurkova and Roman Neruda and Jan Koutník. 18th International Conference on Artificial Neural Networks - ICANN 2008, 2008, Prague, Czech Republic. Springer, 5164, pp.188-197, 2008, Lecture Notes in Computer Science. 〈10.1007/978-3-540-87559-8_20〉. 〈inria-00337391〉

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