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

  • Cesar Torres-Huitzil
  • Bernard Girau
  • Miguel Arias-Estrada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)

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.

Keywords

Input Image Clock Cycle Primary Visual Cortex Systolic Array Orientation Selectivity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ferster, D., Miller, K.D.: Neural mechanisms of orientation selectivity in the visual cortex. Annu. Rev. Neurosci. 23, 441–471 (2000)CrossRefGoogle Scholar
  2. 2.
    Rust, N.C., Shwartz, O., Movshon, J.A., Simoncelli, E.: Spatiotemporal elements of macaque v1 receptive field. Neuron 46, 945–956 (2005)CrossRefGoogle Scholar
  3. 3.
    Itti, L., Koch, C.: Computational modeling of visual attention. Nature Reviews NeuroscienceNature Reviews Neuroscience 2(3), 194–203 (2003)CrossRefGoogle Scholar
  4. 4.
    Mead, C.A.: Neuromorphic electronic systems. Proc. IEEE 78, 1629–1636 (1990)CrossRefGoogle Scholar
  5. 5.
    Choi, T.Y.W., Shi, B.E., Bohanen, K.A.: A multi-chip implementation of cortical orientation hypercolumns. In: Proceedings ISCAS, pp. 13–16 (2004)Google Scholar
  6. 6.
    Choi, T.Y.W., Merolla, P.A., Arthur, J.V., Bohanen, K.A., Shi, B.E.: Neuromorphic implementation of orientation hypercolumn. IEEE Transactions on Circuits and Systems -I 52(6), 1049–1060 (2005)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Herbordt, M.C., VanCourt, T., Gu, Y., Sukhwani, B., Conti, A., Model, J., DiSabello, D.: Achieving high performance with fpga-based computing. IEEE Computer Magazine, 50–57 (March 2007)Google Scholar
  8. 8.
    Girau, B., Torres-Huitzil, C.: Massively distributed digital implementation of an integrate-and.fire legion network for visual scene segmentation. Neurocomputing 50, 1186–1197 (2007)CrossRefGoogle Scholar
  9. 9.
    Torres-Huitzil, C., Girau, B., Gauffriau, A.: Hardware/software co-design for embedded implementation of neural networks. In: Diniz, P.C., Marques, E., Bertels, K., Fernandes, M.M., Cardoso, J.M.P. (eds.) ARCS 2007. LNCS, vol. 4419, pp. 167–178. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Tsi, D.M., Lin, C.P., Huang, K.T.: Defect detection in coloured texture surfaces using gabor filters. The Imaging Science Journal 53, 27–37 (2005)CrossRefGoogle Scholar
  11. 11.
    Petkov, N., Kruzinga, P.: Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: Bar and gratings cells. Biological Cybernetics 75, 83–96 (1997)CrossRefGoogle Scholar
  12. 12.
    Grigorescu, S.E., Petkov, N., Kruizinga, P.: Comparison of texture features based on gabor filter. IEEE Transactions on Neural Networks 11(10), 1160–1167 (2002)MathSciNetGoogle Scholar
  13. 13.
    Torres-Huitzil, C., Arias-Estrada, M.: Fpga-based configurable hardware architecture for real-time window-based image processing. EURASIP Journal on Applied Signal Processing 7, 1024–1034 (2005)CrossRefGoogle Scholar
  14. 14.
    Himavathi, S., Anitha, D., Muthuramalingam, A.: Feedforward neural network implementation in fpga using layer multiplexing for effective resource utilization. IEEE Transactions on Neural Networks 18(3), 880–888 (2007)CrossRefGoogle Scholar
  15. 15.
    Kung, H.T.: Why systolic architectures? IEEE Computer 15(1), 37–46 (1982)Google Scholar
  16. 16.
    Savich, A.W., Moussa, M., Areibi, S.: The impact of arithmetic representation on implementing mlp-bp on fpgas: A study. IEEE Transactions on Neural Networks 18(1), 240–252 (2007)CrossRefGoogle Scholar
  17. 17.
    Adelson, E.H., Berge, J.R.: Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. A 2(2) (February 1985)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cesar Torres-Huitzil
    • 1
  • Bernard Girau
    • 2
  • Miguel Arias-Estrada
    • 3
  1. 1.Information Technology DepartmentPolytechnic University of Victoria, Ciudad VictoriaTamaulipasMexico
  2. 2.CORTEX team, LORIA-INRIA Grand EstCampus ScientifiqueVandoeuvre-les-Nancy CedexFrance
  3. 3.Computer Science DepartmentINAOE, ApdoPueblaMexico

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