Advertisement

A model of clipped hebbian learning in a neocortical pyramidal cell

  • Bruce Graham
  • David Willshaw
Part I: Coding and Learning in Biology
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

A detailed compartmental model of a cortical pyramidal cell is used to determine the effect of the spatial distribution of synapses across a dendritic tree on the pattern recognition capability of the neuron. By setting synaptic strengths according to the clipped Hebbian learning rule used in the associative net neural network model, the cell is able to recognise input patterns, but with a one to two order of magnitude decrease in performance compared to the computing units in the network model. Performance of the cell is optimised by particular forms of input signal, but is not altered by different pattern recognition criteria.

Keywords

Pyramidal Cell Spike Train Input Pattern Associative Memory Output Unit 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. Bennett, W. Gibson, and J. Robinson. Dynamics of the CA3 pyramidal neuron autoassociative memory network in the hippocampus. Phil. Trans. Roy. Soc. Lond. B, 343:167–187, 1994.Google Scholar
  2. 2.
    O. Bernander. Synaptic Integration and Its Control in Neocortical Pyramidal Cells. PhD thesis, California Institute of Technology, 1993.Google Scholar
  3. 3.
    O. Bernander, C. Koch, and R. Douglas. Amplification and linearization of distal synaptic input to cortical pyramidal cells. J. Neurophys., 72:2743–2753, 1994.Google Scholar
  4. 4.
    T. Brown, E. Kairiss, and C. Keenan. Hebbian synapses: biophysical mechanisms and algorithms. Ann. Rev. Neurosci., 13:475–511, 1990.Google Scholar
  5. 5.
    T. Brown, Z. Mainen, A. Zador, and B. Claiborne. Self-organization of Hebbian synapses in hippocampal neurons. In R. Lippmann, J. Moody, and D. Touretzky, editors, Neural Information Processing Systems 3, pages 39–45, San Mateo, California, 1991. Morgan Kaufmann.Google Scholar
  6. 6.
    P. Dayan and D. Willshaw. Optimising synaptic learning rules in linear associative memories. Biol. Cybern., 65:253–265, 1991.Google Scholar
  7. 7.
    R. Douglas, K. Martin, and D. Whitteridge. An intracellular analysis of the visual responses of neurones in cat visual cortex. J. Physiol., 440:659–696, 1991.Google Scholar
  8. 8.
    B. Graham and D. Willshaw. Capacity and information efficiency of the associative net. Network, 8:35–54, 1997.Google Scholar
  9. 9.
    M. Hines. A program for simulation of nerve equations with branching geometries. Int. J. Biomed. Comput., 24:55–68, 1989.Google Scholar
  10. 10.
    D. Johnston, J. Magee, C. Colbert, and B. Christie. Active properties of neuronal dendrites. Ann. Rev. Neurosci., 19:165–186, 1996.Google Scholar
  11. 11.
    D. Marr. Simple memory: a theory for archicortex. Phil. Trans. Roy. Soc. Lond. B, 262:23–81, 1971.Google Scholar
  12. 12.
    B. Mel. Synaptic integration in an excitable dendritic tree. J. Neurophys., 70:1086–1101, 1993.Google Scholar
  13. 13.
    A. Treves and E. Rolls. Computational analysis of the role of the hippocampus in memory. Hippocampus, 4:374–391, 1994.Google Scholar
  14. 14.
    D. Willshaw. Models of distributed associative memory. PhD thesis, University of Edinburgh, 1971.Google Scholar
  15. 15.
    D. Willshaw, O. Buneman, and H. Longuet-Higgins. Non-holographic associative memory. Nature, 222:960–962, 1969.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Bruce Graham
    • 1
  • David Willshaw
    • 1
  1. 1.Centre for Cognitive ScienceUniversity of EdinburghScotlandUK

Personalised recommendations