Advertisement

Stochastic Modeling of Neuronal Responses

  • Barry J. RichmondEmail author
Conference paper

Abstract

The sequence of spikes in a train is considered to be a neuronal code. We have shown that a simple model in which the spikes are stochastically thrown down with the probabilities governed by a rate function over time estimated from the spike density over time. The numbers of spikes is determined by measuring the number of spikes over the interval of interest, usually several hundred milliseconds. By analyzing data from this model, which is instantiated by order statistics, neuronal spike trains can be decoded instant-by-instant as they unfold over time.

Keywords

Neural code Spike trains Monkey cortex Single neurons 

Notes

Acknowledgments

This research was supported by the Intramural Program of the National Institute of Mental, U.S. National Institutes of Health.

Bibliography

  1. 1.
    Optican, L.M., Richmond, B.J.: Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis. J. Neurophysiol. 57 (1987) 162–178.PubMedGoogle Scholar
  2. 2.
    Oram, M.W., Wiener, M.C., Lestienne, R., Richmond, B.J.: Stochastic nature of precisely timed spike patterns in visual system neuronal responses. J. Neurophysiol. 81 (1999) 3021–3033.PubMedGoogle Scholar
  3. 3.
    Richmond, B.J., Optican, L.M.: Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. II. Quantification of response waveform. J. Neurophysiol. 57 (1987) 147–161.PubMedGoogle Scholar
  4. 4.
    Richmond, B.J., Oram, M.W., Wiener, M.C.: Response features determining spike times. Neural. Plast. 6 (1999) 133–145.CrossRefPubMedGoogle Scholar
  5. 5.
    Richmond, B.J., Optican, L.M., Podell, M., Spitzer, H.: Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. I. Response characteristics. J. Neurophysiol. 57 (1987) 132–146.Google Scholar
  6. 6.
    Wiener, M.C., Richmond, B.J.: Using response models to estimate channel capacity for neuronal classification of stationary visual stimuli using temporal coding. J. Neurophysiol. 82 (1999) 2861–2875.PubMedGoogle Scholar
  7. 7.
    Wiener, M.C., Richmond, B.J.: Model based decoding of spike trains. Biosystems. 67 (2002) 295–300.CrossRefPubMedGoogle Scholar
  8. 8.
    Wiener, M.C., Richmond, B.J.: Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model. J. Neurosci. 23 (2003) 2394–2406.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Section on Neural Coding and Computation, Laboratory of Neuropsychology, Department of Health and Human ServicesUS National Institute of Mental HealthBethesdaUSA

Personalised recommendations