Skip to main content
SpringerLink
Log in

Neural Model Approach to the Basic Law of Psychophysics

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Stevens’ law, which is one of the well-known psychophysical laws, suggests that the perceived intensity R of a biological system is proportional to the power of the stimulus strength I, R ∝ I n. In order to realize a self-sustainable system that adapts to changes of the environment, it is important to understand the neural mechanism behind this law. Here, we propose a new neural scheme based on the shunting short-term memory (STM) model with the physiological properties of the nervous system, and examine the relation between the neural system and Stevens’ law through computer simulations of the firing rate f with respect to the stimulus strength I. The simulations showed that the feedback-inputting connectivity plays an important role in reproducing the n > 1 and n < 1 cases of Stevens’ law.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Stevens SS (1975) Psychophysics. Wiley

  2. Pluvinage V, Green DG (1990) Evidence for a power law intensity code in the coupled cones of the turtle. Vision Res 30: 673–682

    Article  Google Scholar 

  3. Yasui S (1992) On the square root intensity coding at the level of cone photoreceptors. Vision Res 32: 199–202

    Article  Google Scholar 

  4. Copelli M, Kinouchi O (2005) Intensity coding in two-dimensional excitable neural networks. Physica 349: 431–442

    Article  Google Scholar 

  5. Tal D, Schwartz E (1997) Computing with the leaky integrate-and-fire neuron: logarithmic computation and multiplication. Neural Comput 9: 305–318

    Article  MATH  Google Scholar 

  6. Grossberg S (1988) Nonlinear neural networks: principles, mechanisms and architectures. Neural Networks 1: 17–61

    Article  Google Scholar 

  7. Harvey RL (1994) Neural network principles. Prentice–Hall

  8. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117: 500–544

    Google Scholar 

  9. Gerstner W, Kistler W (2002) Spiking neuron models. Cambridge University Press

  10. Purves D et al (2001) Neuroscience, 2nd edn. Sinauer Associates

Download references

Author information

Authors and Affiliations

  1. Department of Management Information Science, Fukui Institute of Technology, Gakuen, Fukui, 910-8505, Japan

    Teruya Yamanishi, Masashi Nosaka & Kazumasa Ohkuma

  2. Graduate School of Applied Informatics, University of Hyogo, Chuo-ku, Kobe, 650-0044, Japan

    Haruhiko Nishimura

Authors
  1. Teruya Yamanishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masashi Nosaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haruhiko Nishimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kazumasa Ohkuma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Teruya Yamanishi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yamanishi, T., Nosaka, M., Nishimura, H. et al. Neural Model Approach to the Basic Law of Psychophysics. Neural Process Lett 27, 115–123 (2008). https://doi.org/10.1007/s11063-007-9063-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-007-9063-8

Keywords

Search

Navigation