Advertisement

Correlation coding in stochastic neural networks

  • Raphael Ritz
  • Terrence J. Sejnowski
Part I: Coding and Learning in Biology
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

Stimulus-dependent changes have been observed in the correlations between the spike trains of simultaneously-recorded pairs of neurons from the auditory cortex of marmosets even when there was no change in the average firing rates. A simple neural model can reproduce most of the characteristics of these experimental observations based on model neurons having leaky integration and fire-and-reset spikes and with Poisson-distributed, balanced input. The source of the synchrony in the model was common sensory input. The outputs of neurons in the model appear noisy (almost Poisson owing to the stochastic nature of the input signal, but there is nevertheless a strong central peak in the correlation of the output spike trains. The experimental data and this simple model clearly demonstrate how even a noisy-looking spike train can convey basic information about a sensory stimulus in the relative spike timing between neurons.

Keywords

Firing Rate Auditory Cortex Spike Train Common Input Firing Threshold 
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. Abeles, H. Bergman, E. Margalit, and E. Vaadia. Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J. of Neurophysiology, 70:1629–1638, 1993.Google Scholar
  2. 2.
    Y. Dan, J. J. Atick, and R. C. Reid. Efficent coding of natural scenes in the lateral geniculate nucleus: Experimental test of a computational theory. J. Neurosci., 16(10):3351–3362, 1996.Google Scholar
  3. 3.
    R. C. deCharms and M. M. Merzenich. Primary cortical representation of sounds by the coordination of action-potential timing. Nature, 381:610–613, 1996.Google Scholar
  4. 4.
    G. L. Gerstein and B. Mandelbrot. Random walk models for the spike activity of a single neuron. Biophysic. J., 4:41–68, 1964.Google Scholar
  5. 5.
    W. Gerstner, R. Ritz, and J. L. van Hemmen. Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns. Biol. Cybern., 69:503–515, 1993.Google Scholar
  6. 6.
    J. J. Hopfield. Pattern recognition computation using action potential timing for stimulus representation. Nature, 376:33–36, 1995.Google Scholar
  7. 7.
    Z. F. Mainen and T. J. Sejnowski. Reliability of spike timing in neocortical neurons. Science, 268:1503–1506, 1995.Google Scholar
  8. 8.
    M. Meister, L. Lagnado, and D. A. Baylor. Concerted signaling by retinal ganglion cells. Science, 270:1207–1210, 1995.Google Scholar
  9. 9.
    M. N. Shadlen and W. T. Newsome. Noise, neural codes and cortical organization. Curr. Opin. Neurobiol., 4:569–579, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Raphael Ritz
    • 1
  • Terrence J. Sejnowski
    • 1
  1. 1.Computational Neurobiology LaboratoryThe Salk Institute for Biological StudiesLa JollaUSA

Personalised recommendations