Cooperative organization of connectivity patterns and receptive fields in the visual pathway: application to adaptive tresholding

  • J. Mira
  • A. Manjarrés
  • S. Ros
  • A. E. Delgado
  • J. R. Alvarez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 930)


A biologically plausible theoretical framework to embody cooperative computation in the visual pathway is proposed. From photoreceptors to ganglion cells, visual processing is properly interpreted by means of linear and nonlinear spatio-temporal filters with center-periphery receptive fields and analogic computation.

At cortical level (mainly recurrent pyramidals) hybrid formulations using a combination of local operators (sum plus sigmoid) and conditionals (inferential rules) are more appropriate. This inferential model is quite general, supports analogic and logic computation as particular cases, and should be applicable to bridge the gap between connectionistic and symbolic artificial intelligence in general and between low level and high level vision, in particular.

To illustrate the possibilities of the model, topographic reorganization of connectivity patterns and receptive fields are considered. Adaptive thresholding as a consequence of cooperative consensus on homogeneity measures in the neighbourhood of each neuron has been simulated. Other properties such as self-organization of columns of contrast, orientation, speed or preferred direction can also be modelled as cooperative processes.


Cooperative processes inferential models adaptive neighbourhood functional receptive fields 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Lashley, K.S. “In Search of the Engram”. Society of Experimental Biology Symposium n∘ 4: Physiological Mechanisms in Animal Behaviour, University Press, Cambridge, 1950, pp. 454–480.Google Scholar
  2. [2]
    Luria, A.R., El Cerebro en Acción. Ed. Fontanella. Barcelona, 1974.Google Scholar
  3. [3]
    Gonzalo, J. Las Funciones Cerebrales Humanas según Nuevos Datos y Bases Fisiológicas. Intituto Cajal de Investigaciones Biológicas, Vol. XLIV. Madrid, 1952.Google Scholar
  4. [4]
    Delgado, A.E., Modelos Neurocibernéticos de Dinámica Cerebral, Tesis Doctoral, ETSIT, Madrid, 1978.Google Scholar
  5. [5]
    Mira, J., Delgado, A.E. Zapata, E.L. & Cabello, D. “On the Lesion Tolerance Problem for Co-operative Processes”. In Implementing Finctions: Microprocessors and Firmware. Ed by L. Richter, P. Le Beux, G. Chroust and G. Noguez. pp. 71–80. North-Holland Publishing Company. Amsterdam, 1981.Google Scholar
  6. [6]
    Gilbert, C.D. & Wiesel, T.N. “Receptive Field Dynamics in Adult Primary Visual Cortex”. Nature, Vol.356, 12 March 1992. pp. 150–152.PubMedGoogle Scholar
  7. [7]
    Merzenich, M.M., Recanzone, G., Jenkins, W.M., Allard, T.T. & Nudo, R.J. (1988) “Cortical Representational Plasticity”. In P. Rakie & W. Singer, eds. Neurobiology of Neocortex. Wiley.Google Scholar
  8. [8]
    Mira, J., Delgado, A.E., Alvarez, J.R., de Madrid, A.P. & Santos, M. “Towards More Realistic Self Contained Models of Neurons: High-Order, Recurrence and local learning”. In J. Mira, J. Cabestany and A. Prieto eds. New trends in neural Computation, LNCS 686. Pp. 55–62. Springer Verlag, 1993.Google Scholar
  9. [9]
    Topkar, V., Kjell, B. and Sood, A. “Object Detection Using Scale-space. In Proceedings of the Applications of Artificial Intelligence VIII Conference, The Int. Society for Optical Engineering, pp. 2–13, Orlando, Fl, April 1990.Google Scholar
  10. [10]
    Witkin, A.P. “Scale-Space Filtering”. In Proc. of the 8th Joint Conf. on Art.I Imtellige, pp. 1019–1022, Karlsruhe, Germany, 1983.Google Scholar
  11. [11]
    Paranjape, R.B., Rangayyan, R.N., Morrow, W.M. and Nguyen, H.N. “Adaptive Beighborhood Image Processing”. In Pro. of Visual Communications and Image Processing, Boston, Ma, pages 198–207, SPIE, Bellingham, Wa, 1992.Google Scholar
  12. [12]
    Haralick, R. M. and Shapiro, L. G. Computer and Robot vision.Addison-Wesley Pub. Comp.Google Scholar
  13. [13]
    Sahoo, P.K., Soltani, S., Wong, A.K.C. and Chen, Y.C. “Survey of Thresholding Techniques”. Computer Vision, Graphics, and Image Processing, 41(2):233–260, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • J. Mira
    • 1
  • A. Manjarrés
    • 1
  • S. Ros
    • 1
  • A. E. Delgado
    • 1
  • J. R. Alvarez
    • 1
  1. 1.Dpto. Informática y Automática. Facultad de CienciasUNEDMadridSpain

Personalised recommendations