Advertisement

Stimulus-Response Curves in Sensory Neurons: How to Find the Stimulus Measurable with the Highest Precision

  • Petr Lansky
  • Ondřej Pokora
  • Jean-Pierre Rospars
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)

Abstract

To study sensory neurons, the neuron response is plotted versus stimulus level. The aim of the present contribution is to determine how well two different levels of the incoming stimulation can be distinguished on the basis of their evoked responses. Two generic models of response function are presented and studied under the influence of noise. We show in these noisy cases that the most suitable signal, from the point of view of its identification, is not unique. To obtain the best identification we propose to use measures based on Fisher information. For these measures, we show that the most identifiable signal may differ from that derived when the noise is neglected.

Keywords

Transfer Function Sensory Neuron Stimulus Intensity Optimality Criterion Fisher Information 
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.
    Amari, S., Nakahara, H.: Difficulty of singularity in population coding. Neural Comput. 17, 839–858 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Bethge, M., Rotermund, D., Pawelzik, K.: Optimal short-term population coding: When Fisher information fails. Neural Comput. 14, 2317–2351 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Brunel, N., Nadal, J.-P.: Mutual information, Fisher information, and population coding. Neural Comput. 10, 1731–1757 (1998)CrossRefGoogle Scholar
  4. 4.
    Cramer, H.: Mathematical Methods of Statistics. Princeton University Press, Princeton (1946)zbMATHGoogle Scholar
  5. 5.
    Dayan, P., Abbott, L.F.: Theoretical neuroscience. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  6. 6.
    Freund, J.A., Schimansky-Geier, L., Beisner, B., et al.: Behavioral stochastic resonance: How the noise from a Daphnia swarm enhances individual prey capture by juvenile paddlefish. J. Theor. Biol. 214, 71–83 (2002)CrossRefGoogle Scholar
  7. 7.
    Getz, W.M., Lansky, P.: Ligand concentration coding and optimal Michaelis-Menten parameters in multivalent and heterogeneous receptor membranes. Chemical Senses 26, 95–104 (2001)CrossRefGoogle Scholar
  8. 8.
    Gerstner, W., Kistler, W.: Spiking neuron models. Cambridge Univ. Press, Cambridge (2002)zbMATHGoogle Scholar
  9. 9.
    Green, D.M., Swets, J.A.: Signal detection theory and psychophysics. Wiley, New York (1966)Google Scholar
  10. 10.
    Greenwood, P.E., Ward, L.M., Wefelmeyer, W.: Statistical analysis of stochastic resonance in a simple setting. Phys. Rev. E, Part B 60, 4687–4695 (1999)CrossRefGoogle Scholar
  11. 11.
    Greenwood, P.E., Ward, L.M., Russel, D.F., Neiman, A., Moss, F.: Stochastic resonance enhances the electrosensory information available to paddlefish for prey capture. Phys. Rev. Lett. 84, 4773–4776 (2000)CrossRefGoogle Scholar
  12. 12.
    Greenwood, P.E., Lansky, P.: Optimum signal in a simple neuronal model with signal-dependent noise. Biol. Cybern. 92, 199–205 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Greenwood, P.E., Lansky, P.: Information content in threshold data with non-Gaussian noise. Fluct. Noise Letters, L79–L89 (2007)Google Scholar
  14. 14.
    Krivan, V., Lansky, P., Rospars, J.-P.: Coding of periodic pulse stimulations in chemoreceptors. BioSystems 67, 121–128 (2002)CrossRefGoogle Scholar
  15. 15.
    Jenison, R.L.: Decoding first-spike latency: A likelihood approach. Neurocomput. 38, 239–248 (2001)CrossRefGoogle Scholar
  16. 16.
    Johnson, D.H., Ray, W.: Optimal stimulus coding by neural populations using rate codes. J. Comput. Neurosci. 16, 129–138 (2004)CrossRefGoogle Scholar
  17. 17.
    Lam, H.S., Lampard, D.G.: Modeling of drug receptor interaction with birth and death processes. J. Math. Biol. 12, 153–172 (1981)zbMATHMathSciNetGoogle Scholar
  18. 18.
    Lansky, P., Getz, W.M.: Sensitivity and coding range in olfactory sensory neuron. Role of heterogeneity of receptors. Bull. Math. Biol. 63, 885–908 (2001)CrossRefGoogle Scholar
  19. 19.
    Lansky, P., Greenwood, P.E.: Optimal signal estimation in neuronal models. Neural Comput. 17, 2240–2257 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Lansky, P., Greenwood, P.E.: Optimal signal in sensory neurons under extended rate coding concept. BioSystems 89, 10–15 (2007)CrossRefGoogle Scholar
  21. 21.
    Lansky, P., Rodriguez, R., Sacerdote, L.: Mean instantaneous firing frequency is always higher than the firing rate. Neural Comput. 16, 477–489 (2004)CrossRefGoogle Scholar
  22. 22.
    Lansky, P., Rospars, J.-P.: Coding of odor intensity. BioSystems 31, 15–38 (1993)CrossRefGoogle Scholar
  23. 23.
    Lansky, P., Sacerdote, L., Zucca, L.: Optimum signal in a diffusion leaky integrate-and-fire neuronal model. Math. Biosci. 207, 261–274 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    McKeegan, D.E.F.: Spontaneous and odour evoked activity in single avian olfactory bulb neurones. Brain Res. 929, 48–58 (2002)CrossRefGoogle Scholar
  25. 25.
    Nizami, L.: Estimating auditory neuronal dynamic range using a fitted function. Hearing Res. 167, 13–27 (2002)CrossRefGoogle Scholar
  26. 26.
    Rospars, J.-P., Lansky, P., Duchamp-Viret, P., Duchamp, A.: Spiking frequency vs. odorant concentration in olfactory receptor neurons. BioSystems 58, 133–141 (2000)CrossRefGoogle Scholar
  27. 27.
    Rospars, J.-P., Krivan, V., Lansky, P.: Perireceptor and receptor events in olfaction. Comparison of concentration and flux detectors: a modeling study. Chemical Senses 25, 293–311 (2000)CrossRefGoogle Scholar
  28. 28.
    Rospars, J.-P., Lansky, P., Duchamp-Viret, P., Duchamp, A.: Relation between stimulus intensity and response in frog olfactory receptor neurons in vivo. European J. Neurosci. 18, 1135–1154 (2003)CrossRefGoogle Scholar
  29. 29.
    Stemmler, M.: A single spike suffices: the simplest form of stochastic resonance in model neurons. Network 7, 687–716 (1996)zbMATHCrossRefGoogle Scholar
  30. 30.
    Wiener, M.C., Richmond, B.J.: Decoding spike trains instant by instant using order statistics and the mixture-of-Poisson model. J. Neurosci. 23, 2394–2406 (2003)Google Scholar
  31. 31.
    Wilke, S.D., Eurich, C.W.: Representational accuracy of stochastic neural populations. Neural Comput. 14, 155–189 (2002)zbMATHCrossRefGoogle Scholar
  32. 32.
    Wu, S., Amari, S., Nakahara, H.: Information processing in a neuron ensemble with the multiplicative correlation structure. Neural Networks 17, 205–214 (2004)zbMATHCrossRefGoogle Scholar
  33. 33.
    Zhang, K.C., Sejnowski, T.J.: Neuronal tuning: To sharpen or broaden? Neural Comput. 11, 75–84 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Petr Lansky
    • 1
  • Ondřej Pokora
    • 1
    • 2
  • Jean-Pierre Rospars
    • 3
  1. 1.Institute of Physiology, Academy of Sciences of Czech Republic, Videnska 1083, 142 20 Prague 4Czech Republic
  2. 2.Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Janackovo namesti 2a, 602 00 BrnoCzech Republic
  3. 3.UMR 1272 UMPC–INRA–AgroParisTech “Physiologie de l’Insecte Signalisation et Communication”, INRA, 78026 Versailles CedexFrance

Personalised recommendations