Skip to main content
NCBI home page
Proceedings of the National Academy of Sciences of the United States of America logoLink to Proceedings of the National Academy of Sciences of the United States of America
. 1987 May;84(9):2727–2731. doi: 10.1073/pnas.84.9.2727

Neural networks that learn temporal sequences by selection.

S Dehaene, J P Changeux, J P Nadal
PMCID: PMC304731  PMID: 3472233

Abstract

A model for formal neural networks that learn temporal sequences by selection is proposed on the basis of observations on the acquisition of song by birds, on sequence-detecting neurons, and on allosteric receptors. The model relies on hypothetical elementary devices made up of three neurons, the synaptic triads, which yield short-term modification of synaptic efficacy through heterosynaptic interactions, and on a local Hebbian learning rule. The functional units postulated are mutually inhibiting clusters of synergic neurons and bundles of synapses. Networks formalized on this basis display capacities for passive recognition and for production of temporal sequences that may include repetitions. Introduction of the learning rule leads to the differentiation of sequence-detecting neurons and to the stabilization of ongoing temporal sequences. A network architecture composed of three layers of neuronal clusters is shown to exhibit active recognition and learning of time sequences by selection: the network spontaneously produces prerepresentations that are selected according to their resonance with the input percepts. Predictions of the model are discussed.

Full text

PDF
2727

Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. Changeux J. P. The acetylcholine receptor: an "allosteric" membrane protein. Harvey Lect. 1979 1980;75:85–254. [PubMed] [Google Scholar]
  2. Fukushima K. A model of associative memory in the brain. Kybernetik. 1973 Feb;12(2):58–63. doi: 10.1007/BF00272461. [DOI] [PubMed] [Google Scholar]
  3. Hawkins R. D., Kandel E. R. Is there a cell-biological alphabet for simple forms of learning? Psychol Rev. 1984 Jul;91(3):375–391. [PubMed] [Google Scholar]
  4. Heidmann T., Changeux J. P. Un modèle moléculaire de régulation d'efficacité au niveau postsynaptique d'une synapse chimique. C R Seances Acad Sci III. 1982 Dec 6;295(12):665–670. [PubMed] [Google Scholar]
  5. Hopfield J. J. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A. 1982 Apr;79(8):2554–2558. doi: 10.1073/pnas.79.8.2554. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Ito M., Sakurai M., Tongroach P. Climbing fibre induced depression of both mossy fibre responsiveness and glutamate sensitivity of cerebellar Purkinje cells. J Physiol. 1982 Mar;324:113–134. doi: 10.1113/jphysiol.1982.sp014103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Kawamoto A. H., Anderson J. A. A neural network model of multistable perception. Acta Psychol (Amst) 1985 May;59(1):35–65. doi: 10.1016/0001-6918(85)90041-1. [DOI] [PubMed] [Google Scholar]
  8. Koch C., Poggio T., Torre V. Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing. Proc Natl Acad Sci U S A. 1983 May;80(9):2799–2802. doi: 10.1073/pnas.80.9.2799. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Konishi M. Birdsong: from behavior to neuron. Annu Rev Neurosci. 1985;8:125–170. doi: 10.1146/annurev.ne.08.030185.001013. [DOI] [PubMed] [Google Scholar]
  10. Margoliash D. Acoustic parameters underlying the responses of song-specific neurons in the white-crowned sparrow. J Neurosci. 1983 May;3(5):1039–1057. doi: 10.1523/JNEUROSCI.03-05-01039.1983. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Marler P., Peters S. Developmental overproduction and selective attrition: new processes in the epigenesis of birdsong. Dev Psychobiol. 1982 Jul;15(4):369–378. doi: 10.1002/dev.420150409. [DOI] [PubMed] [Google Scholar]
  12. Marslen-Wilson W., Tyler L. K. The temporal structure of spoken language understanding. Cognition. 1980 Mar;8(1):1–71. doi: 10.1016/0010-0277(80)90015-3. [DOI] [PubMed] [Google Scholar]
  13. McClelland J. L., Elman J. L. The TRACE model of speech perception. Cogn Psychol. 1986 Jan;18(1):1–86. doi: 10.1016/0010-0285(86)90015-0. [DOI] [PubMed] [Google Scholar]
  14. Nakayama K. Biological image motion processing: a review. Vision Res. 1985;25(5):625–660. doi: 10.1016/0042-6989(85)90171-3. [DOI] [PubMed] [Google Scholar]
  15. Stent G. S., Kristan W. B., Jr, Friesen W. O., Ort C. A., Poon M., Calabrese R. L. Neuronal generation of the leech swimming movement. Science. 1978 Jun 23;200(4348):1348–1357. doi: 10.1126/science.663615. [DOI] [PubMed] [Google Scholar]
  16. Toulouse G., Dehaene S., Changeux J. P. Spin glass model of learning by selection. Proc Natl Acad Sci U S A. 1986 Mar;83(6):1695–1698. doi: 10.1073/pnas.83.6.1695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Willwacher G. Storage of temporal pattern sequence in a network. Biol Cybern. 1982;43(2):115–126. doi: 10.1007/BF00336974. [DOI] [PubMed] [Google Scholar]

Articles from Proceedings of the National Academy of Sciences of the United States of America are provided here courtesy of National Academy of Sciences

RESOURCES