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Pattern recognition computation using action potential timing for stimulus representation

Nature volume 376, pages 33–36 (1995)Cite this article

Abstract

A computational model is described in which the sizes of variables are represented by the explicit times at which action potentials occur, rather than by the more usual 'firing rate' of neurons. The comparison of patterns over sets of analogue variables is done by a network using different delays for different information paths. This mode of computation explains how one scheme of neuroarchitecture can be used for very different sensory modalities and seemingly different computations. The oscillations and anatomy of the mammalian olfactory systems have a simple interpretation in terms of this representation, and relate to processing in the auditory system. Single-electrode recording would not detect such neural computing. Recognition 'units' in this style respond more like radial basis function units than elementary sigmoid units.

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References

  1. McClurkin, J. W., Optician, L. M., Richmond, B. J. & Gawne, T. J. Science 253, 675–677 (1991).

    Article  ADS  CAS  Google Scholar 

  2. Bialek, W. & Rieke, F. Trends Neurosci. 15, 428–434 (1992).

    Article  CAS  Google Scholar 

  3. Atick, J. J. Network 3, 213–251 (1992).

    Article  Google Scholar 

  4. Perkel, D. H. & Bullock, T. H. Neurosci. Res. Prog. Bull. 6, 221–248 (1968).

    Google Scholar 

  5. Bullock, T. H. A. Rev. Neurosci. 16, 1–15 (1993).

    Article  CAS  Google Scholar 

  6. Burgess, N., O'Keefe, J. M. & Recce, M. in Advances in Neural Information Processing Systems Vol. 5 (eds Hanson, S. J., Giles, C. L. & Cowan, J. D.) 929–936 (Morgan Kaufman, San Mateo, CA, 1993).

    Google Scholar 

  7. Heiligenberg, W. F. Neural Nets in Electric Fish 51–60 (MIT Press, Cambridge, MA, 1991).

    Google Scholar 

  8. Reichardt, W. Z. Naturf. 12b, 448–457 (1957).

    Article  Google Scholar 

  9. Carr, C. E. & Konishi, K. J. Neurosci. 10, 3227–3246 (1990).

    Article  CAS  Google Scholar 

  10. Kauer, J. S. Trends Neurosci. 14, 79–85 (1991).

    Article  CAS  Google Scholar 

  11. Herz, J., Krogh, A. & Palmer, R. G. Introduction to the Theory of Neural Computation (Addison Wesley, Redwood City, CA, 1991).

    Google Scholar 

  12. Alonso, A. & Llinas, R. R. Nature 342, 175–177 (1989).

    Article  ADS  CAS  Google Scholar 

  13. Silva, L. R., Amital, Y. & Connors, B. W. Science 252, 432–435 (1991).

    Article  ADS  Google Scholar 

  14. Shepherd, G. M. & Brayton, R. K. Brain Res. 175, 377–382 (1979).

    Article  CAS  Google Scholar 

  15. Rodieck, R. W. The Vertebrate Retina (Freeman, San Francisco, 1973).

    Google Scholar 

  16. Tank, D. W. & Hopfield, J. J. Proc. natn. Acad. Sci. U.S.A. 84, 1896–1900 (1987).

    Article  ADS  CAS  Google Scholar 

  17. Kuwabara, N. & Suga, N. J. Neurophysiol. 69, 1713–1724 (1993).

    Article  CAS  Google Scholar 

  18. Unnikrishnan, K. P., Hopfield, J. J. & Tank, D. W. IEEE Trans. Signal. Process 39, 698–713 (1991).

    Article  ADS  Google Scholar 

  19. Adrian, E. D. J. Physiol., Lond. 100, 459–473 (1941).

    Article  Google Scholar 

  20. Shepherd, G. M. The Synaptic Organization of the Brain 152–183 (Oxford Univ. Press, Oxford, 1979).

    Google Scholar 

  21. Haberly, L. Chem. Senses 10, 219–238 (1985).

    Article  Google Scholar 

  22. Nickell, W. T. & Shipley, M. T. in Science of Olfaction (eds Serby, M. J. & Chobor, K. L.) 172–212 (Springer, New York, 1992).

    Book  Google Scholar 

  23. Laurent, G. & Naraghi, M. J. Neurosci. 14, 2993–3004 (1994).

    Article  CAS  Google Scholar 

  24. Delaney, K. R. et al. Proc. natn. Acad. Sci. U.S.A. 91, 669–673 (1994).

    Article  ADS  CAS  Google Scholar 

  25. Tank, D. W., Gelperin, A. & Kleinfeld, D. Science 265, 1819–1820 (1994).

    Article  ADS  CAS  Google Scholar 

  26. Shepherd, G. M. The Synaptic Organization of the Brain 289–307 (Oxford Univ. Press, Oxford, 1979).

    Google Scholar 

  27. Eckhorn, R. et al. Biol. Cybern. 60, 121–130 (1988).

    Article  CAS  Google Scholar 

  28. Gray, C. M. & Singer, W. Proc. natn. Acad. Sci. U.S.A. 86, 1698–1702 (1989).

    Article  ADS  CAS  Google Scholar 

  29. Carr, C. E. & Konishi, M. Proc. natn. Acad. Sci. U.S.A. 85, 8311–8315 (1988).

    Article  ADS  CAS  Google Scholar 

  30. Harnischfeger, G., Neuweiler, G. & Schlegel, P. J. Neurophysiol. 53, 89–109 (1985).

    Article  CAS  Google Scholar 

  31. Revial, M. F., Sicard, G., Duchamp, A. & Holley, A. Chem. Senses 7, 175–190 (1982).

    Article  CAS  Google Scholar 

  32. Girosi, F. & Poggio, T. Biol. Cybern. 63, 169–176 (1990).

    Article  Google Scholar 

  33. Hossam, O. & Fahmy, M. M. Neural Computat. 6, 927–943 (1994).

    Article  Google Scholar 

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Authors and Affiliations

  1. Division of Chemistry and Chemical Engineering, and Division of Biology, California Institute of Technology, Pasadena, California, 91125, USA

    J. J. Hopfield

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  1. J. J. Hopfield
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Hopfield, J. Pattern recognition computation using action potential timing for stimulus representation. Nature 376, 33–36 (1995). https://doi.org/10.1038/376033a0

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  1. Celestine P. Lawrence

    I employed an approximation of this algorithm to outperform a deep learning method (ref: https://openreview.net/foru... ). That is to my knowledge the first empirical demonstration on a real-life dataset "in the wild" where a signal processing method to solve the analog match problem outperforms other machine learning methods. Kindly let me know if you are aware of any other earlier demonstrations. Hopfield's illustrative demonstration is from a pre-designed dataset, and thus does not qualify.

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