Adaptive Pattern Classification and Universal Recoding I: Parallel Development and Coding of Neural Feature Detectors

  • Stephen Grossberg
Part of the Boston Studies in the Philosophy of Science book series (BSPS, volume 70)


This is the second of a three part series of articles on code development that appeared in 1976. The first article [36] responded to Malsburg’s addition of a normalization rule to the equations of Chapter 7. Malsburg’s rule directly constrains the total LTM strength of synaptic contacts to each cell. I realized that if this rule held in all learning cells, then classical conditioning would be impossible. I had realized a decade earlier (Chapter 2) that a direct LTM constraint can often be replaced by an STM constraint that influences LTM indirectly. Also my 1973 work on STM in shunting competitive networks was done, so I knew that the STM competition produces the normalization property for free, and does not contradict classical conditioning. My first article made these points, substituted shunting STM competition for additive competition and eliminated Malsburg’s LTM normalization rule.


Bipolar Cell Amacrine Cell Tuning Curve Inhibitory Interneuron Arousal Level 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Barlow, H.B., Pettigrew, J.D.: Lack of specificity of neurones in the visual cortex of young kittens. J. Physiol. (Lond.) 218, 98–100 (1971)Google Scholar
  2. Bennett, M.V.L.: Analysis of parallel excitatory and inhibitory synaptic channels. J. Neurophysiol. 34, 69–75 (1971)Google Scholar
  3. Blackenship, J.E., Wachtel, H., Kandel, E.R.: Ionic mechanisms of excitatory, inhibitory, and dual synaptic actions mediated by an identified interneuron in abdominal ganglion of Aplysia. J. Neurophysiol. 34, 76–92 (1971)Google Scholar
  4. Blakemore,C., Cooper, G.F.: Development of the brain depends on the visual environment. Nature (Lond.) 228, 477–478 (1970)CrossRefGoogle Scholar
  5. Blakemore,C., Mitchell, D.E.: Environmental modification of the visual cortex and the neural basis of learning and memory. Nature (Lond.) New Biol. 241, 467–468 (1973)CrossRefGoogle Scholar
  6. Boycott,B.B., Dowling,J.E.: Organization of the primate retina: light microscopy. Phil. Trans, roy. Soc. B. 255, 109–184 (1969)Google Scholar
  7. Ellias,S.A., Grossberg,S.: Pattern formation, contrast control, and oscillations in the short term memory of shunting on-center off- surround networks. Biol. Cybernetics 20, 69–98 (1975)CrossRefGoogle Scholar
  8. Freeman,W.J.: Neural coding through mass action in the olfactory system. Proceeding IEEE Conference on biologically motivated automata theory 1974Google Scholar
  9. Gierer,A., Meinhardt,H.: A theory of biological pattern formation. Kybernetik 12, 30–39 (1972)CrossRefGoogle Scholar
  10. Greenspan,H.P., Benney,D.J.: Calculus. New York: McGraw-Hill 1973Google Scholar
  11. Grossberg,S.: Nonlinear difference-differential equations in prediction and learning theory. Proc. nat. Acad. Sci. (Wash.) 58, 1329–1334(1967)Google Scholar
  12. Grossberg,S.: Some networks that can learn, remember, and reproduce any number of complicated space-time patterns, II. Stud. appl. Math. 49, 135–166 (1970a)Google Scholar
  13. Grossberg,S.: Neural pattern discrimination. J. theor. Biol. 27, 291–337 (1970b)CrossRefGoogle Scholar
  14. Grossberg,S.: Pavlovian pattern learning by nonlinear neural networks. Proc. nat. Acad. Sci. (Wash,) 68, 828–831 (1971)CrossRefGoogle Scholar
  15. Grossberg,S.: Neural expectation: cerebellar and retinal analogs of cells fired by learnable or unlearned pattern classes. Kybernetik 10, 49–57 (1972)CrossRefGoogle Scholar
  16. Grossberg,S.: Contour enhancement, short term memory, and constancies in reverberating neural networks. Stud. appl. Math. 52, 213–257 (1973)Google Scholar
  17. Grossberg,S.: Classical and instrumental learning by neural networks. In: Rosen,R. and Snell,F. (Eds.): Progress in Theoretical Biology, pp. 51–141. New York: Academic Press 1974Google Scholar
  18. Grossberg,S.: A neural model of attention, reinforcement, and discrimination learning. Int. Rev. Neurobiol. 18, 263–327 (1975)CrossRefGoogle Scholar
  19. Grossberg,S.: On the development of feature detectors in the visual cortex with applications to learning and reaction-diffusion systems. Biol. Cybernetics 21, 145–159 (1976)CrossRefGoogle Scholar
  20. Grossberg,S., Levine, D.S.: Some developmental and attentional biases in the contrast enhancement and short term memory of recurrent neural networks. J, theor. Biol. 53, 341–380 (1975)Google Scholar
  21. Hebb,D.O.: The organization of behavior. New York: Wiley 1949Google Scholar
  22. Hirsch,H.V.B., Spinelli,D.N.: Visual experience modifies distribution of horizontally and vertically oriented receptive fields in cats. Science 168, 869–871 (1970)CrossRefGoogle Scholar
  23. Hirsch,H.V.B., Spinelli,D.N.: Modification of the distribution of receptive field orientation in cats by selective visual exposure during development. Exp. Brain Res. 12, 509–527 (1971)CrossRefGoogle Scholar
  24. Hubel,D.H., Wiesel,T.N.: The period of susceptibility to the physiological effects of unilateral eye closure in kittens. J. Physiol. (Lond.) 206, 419–436 (1970)Google Scholar
  25. Kimble, G. A.: Foundations of conditioning and learning. New York: Appleton-Century-Crofts 1967Google Scholar
  26. Levine,D.S., Grossberg,S.: Visual illusions in neural networks: line neutralization, tilt aftereffect, and angle expansion. J. theor. Biol., in press (1976)Google Scholar
  27. Meinhardt, H., Gierer,A.: Applications of a theory of biological pattern formation based on lateral inhibition. J. Cell. Sci. 15, 321–346Google Scholar
  28. Pérez, R., Glass, L., Shlaer,R.: Development of specificity in the cat visual cortex. J. math. Biol. (1974)Google Scholar
  29. Von der Malsburg,C.: Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14, 85–100 (1973)CrossRefGoogle Scholar
  30. Wachtel,H., Kandel,E.R.: Conversion of synaptic excitation to inhibition at a dual chemical synapse. J. Neurophysiol. 34, 56–00(1971)Google Scholar
  31. Wiesel,T.N., Hubel,D.H.: Single-cell responses in striate cortex of kittens deprived of vision in one eye. J. Neurophysiol. 26, 1003–1017 (1963)Google Scholar
  32. Wiesel,T.N., Hubel,D.H.: Comparison of the effects of unilateral and bilateral eye closure on cortical unit responses in kittens. J. Neurophysiol. 28, 1029–1040 (1965)Google Scholar

Copyright information

© D. Reidel Publishing Company, Dordrecht, Holland 1982

Authors and Affiliations

  • Stephen Grossberg
    • 1
  1. 1.Department of MathematicsBoston UniversityUSA

Personalised recommendations