Skip to main content
Log in

Assessing the Encoding of Stimulus Attributes with Rapid Sequences of Stimulus Events

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

Abstract

In a preceding paper (M. Eger and R. Eckhorn, J. Comput. Neurosci., 2002) we have published a three step method for the quantification of transinformation in multi-input and -output neuronal systems. Here we present an extension that applies to rapid series of transient stimuli and thus, fills the gap between the discrete and continuous stimulation paradigm. While the three step method potentially captures all stimulus aspects, the present approach quantifies the discriminability of selected attributes of discrete stimuli and thus, assesses their encoding. Based on simulated and recorded data we investigate the performance of the implemented algorithm. Our approach is illustrated by analyses of neuronal population activity from the visual cortex of the cat, evoked by electrical stimuli of the retina.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bialek W, Rieke F, de Ruyter van Steveninck RR, Warland D (1991) Reading a neural code. Science 252: 1854–1857.

    Google Scholar 

  • Borst A, Theunissen FE (1999) Information theory and neural coding. Nat. Neurosci. 2: 947–957.

    Google Scholar 

  • Buracas GT, Albright TD (1999) Gauging sensory representation in the brain. Trends Neurosci. 22: 303–309.

    Google Scholar 

  • Buracas GT, Zador AM, DeWeese MR, Albright TD (1998) Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex. Neuron 20: 959–969.

    Google Scholar 

  • deCharms RC, Zador A(2000) Neural representation and the cortical code. Annu. Rev. Neurosci. 23: 613–647.

    Google Scholar 

  • DeWeese MR, Meister M (1999) How to measure the information from one symbol. Network 10: 325–340.

    Google Scholar 

  • Eckhorn R, Pöpel B (1974) Rigorous and extended application of information theory to the afferent visual system of the cat. I. Basic concepts. Kybernetik 16: 191–200.

    Google Scholar 

  • Eckhorn R, Pöpel B (1981) Responses of cat retinal ganglion cells to the random motion of a spot stimulus. Vision Res. 21: 435–443.

    Google Scholar 

  • Eckhorn R, Querfurth H (1985) Information transmission by isolated frog muscle spindle. Biol. Cybern. 52: 165–176.

    Google Scholar 

  • Efron B (1994) The Jackknife, the Bootstrap and Other Resampling Plans. 6th edn. Society for Industrial and Applied Mathematics, Philadelphia.

    Google Scholar 

  • Efron B, Tibshirani R (1991) Statistical data analysis in the computer age. Science 253: 390–395.

    Google Scholar 

  • Eger M (2001) Information Theoretical methods for the functional adjustment of retina implant parameters. PhD Thesis, Faculty of Electrical Engineering and Information Technology, University of Paderborn, Paderborn, Germany.

    Google Scholar 

  • Eger M, Eckhorn R (2002) A model-based approach for the analysis of neuronal information transmission in multi-input and-output systems. J. Comput. Neurosci. 12: 175–200.

    Google Scholar 

  • Gawne TJ, McClurkin JW, Richmond BJ, Optican LM (1991) Lateral geniculate neurons in behaving primates. III. Response predictions of a channel model with multiple spatial-to-temporal filters. J. Neurophysiol. 66: 809–823.

    Google Scholar 

  • Gershon ED, Wiener MC, Latham PE, Richmond BJ (1998) Coding strategies in monkey V1 and inferior temporal cortices. J. Neurophysiol. 79: 1135–1144.

    Google Scholar 

  • Glaser EM, Ruchkin DS (1976) Principles of Neurobiological Signal Analysis. 1st edn. Academic Press, New York.

    Google Scholar 

  • Golomb D, Hertz JA, Panzeri S, Treves A, Richmond BJ (1997) How well can we estimate the information carried in neuronal responses from limited samples? Neural Comput. 9: 649–665.

    Google Scholar 

  • Haag J, Borst A (1997) Encoding of visual motion information and reliability in spiking and graded potential neurons. J. Neurosci. 17: 4809–4819.

    Google Scholar 

  • Hesse L, Schanze T, Wilms M, Eger M(2000) Implantation of retina stimulation electrodes and recording of electrical stimulation responses in the visual cortex of the cat. Graefe's Arch. Clin. Exp. Ophthalmol. 238: 840–845.

    Google Scholar 

  • Kjaer TW, Hertz JA, Richmond BJ (1994) Decoding cortical neuronal signals: Network models, information estimation and spatial tuning. J. Comput. Neurosci. 1: 109–139.

    Google Scholar 

  • Loader C (1999) Local Regression and Likelihood. Statistics and Computing. 1st edn. Springer, Berlin.

    Google Scholar 

  • Loader CR (1997) Locfit: An introduction. Statistical Computing and Graphics Newsletters.

  • MacKay DM, McCulloch WS (1952) The limiting information capacity of a neuronal link. Bulletin of Mathematical Biophysics 14: 127–135.

    Google Scholar 

  • Optican LM, Richmond BJ (1987) Temporal encoding of twodimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis. J. Neurophysiol 57: 162–178.

    Google Scholar 

  • Panzeri S, Treves A (1996) Analytical estimates of limited sampling bias in different information measures. Network 7: 87–107.

    Google Scholar 

  • Reich DS, Mechler F, Victor JD (2000) Formal and attribute-specific information in primary visual cortex. J. Neurophysiol. 85: 305–318.

    Google Scholar 

  • Richmond BJ (1998) The Relationship Between Neuronal Codes and Cortical Organization. 1st edn. Wiley, New York.

    Google Scholar 

  • Richmond BJ, Optican LM (1987) Temporal encoding of twodimensional patterns by single units in primate inferior temporal cortex. II. Quantification of response waveform. J. Neurophysiol. 57: 147–161.

    Google Scholar 

  • Richmond BJ, Optican LM, Podell M, Spitzer H (1987) Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. I. Response characteristics. J. Neurophysiol 57: 132–146.

    Google Scholar 

  • Rieke F, Warland DK, de Ruyter van Steveninck RR, Bialek W (1998) Spikes: Exploring the Neural Code. 1st edn. The MIT Press, Cambridge.

    Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst. Tech. J. 27: 379–423, 623-656.

    Google Scholar 

  • Stein RB (1967) The information capacity of nerve cells using a frequency code. Biophys. J. 7: 797–825.

    Google Scholar 

  • Theunissen F, Miller JP Temporal encoding in nervous systems: A rigorous definition. J. Comput. Neurosci. 2: 149–162.

  • Troy JB, Robson JG (1992) Steady discharges of X and Y retinal ganglion cells of cat under photopic illuminance. Vis. Neurosci. 9: 535–553.

    Google Scholar 

  • Victor JD (1999) Temporal aspects of neuronal coding in the retina and lateral geniculate. Network 10:R1–R66.

    Google Scholar 

  • Warland DK, Reinagel P, Meister M(1997) Decoding visual information from a population of retinal ganglion cells. J. Neurophysiol. 78: 2336–2350.

    Google Scholar 

  • Werner G, Mountcastle VB (1965) Neural activity in mechanoreceptive cutaneous afferents: Stimulus-response relations,Weber functions, and information transmission. J. Neurophysiol. 28: 359–397.

    Google Scholar 

  • Wiener MC, Richmond BJ (1998) Using response models to study coding strategies in monkey visual cortex. Biosystems 48: 279–286.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eger, M., Eckhorn, R. Assessing the Encoding of Stimulus Attributes with Rapid Sequences of Stimulus Events. J Comput Neurosci 13, 207–216 (2002). https://doi.org/10.1023/A:1020214331659

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1020214331659

Navigation