Skip to main content
SpringerLink
Log in

Trial-to-trial variability and its influence on higher-order statistics

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

Firing patterns of neurons are highly variable from trial to trial, even when we record a well-specified neuron exposed to identical stimuli under the same experimental conditions. The trial-to-trial variability of neuronal spike trains may represent some sort of information and provide important indications about neuronal properties. We propose a new method for quantifying the trial-to-trial variability of spike trains, and investigate how the characteristics of noisy neural network models affect the proposed measure.

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. Softky WR, Koch C (1993) The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J Neurosci 13:334–350

    Google Scholar 

  2. Shadlen MN, Newsome WT (1998) The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci 18:3870–3896

    Google Scholar 

  3. Masuda N, Aihara K (2003) Ergodicity of spike trains: when does trial averaging make sense? Neural Comput 15:1341–1372

    Article  MATH  Google Scholar 

  4. Salinas E, Sejnowski TJ (2001) Correlated neuronal activity and the flow of neural information. Nature Rev Neurosci 2:539–551

    Article  Google Scholar 

  5. Benucci A, Verschure PF, König P (2003) Existence of high-order correlations in cortical activity. Phys Rev E 68(4):041905

    Article  Google Scholar 

  6. Tuckwell HC (1988) Introduction to theoretical neurobiology, vol 2. Cambridge University Press, Cambridge

    Google Scholar 

  7. Hunter JD, Milton JG, Thomas PJ, et al (1998) Resonance effect for neural spike time reliability. J Neurophysiol 80:1427–1438

    Google Scholar 

  8. Fujiwara K, Fujiwara H, Tsukada M, et al (2007) Reproducing bursting interspike interval statistics of the gustatory cortex. Biosystems 90:442–448

    Article  Google Scholar 

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

    MATH  Google Scholar 

  10. Hansel D, Mato G, Meunier C (1995) Synchrony in excitatory neural networks. Neural Comput 7:307–337

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan

    Kantaro Fujiwara & Kazuyuki Aihara

  2. Aihara Complexity Modeling Project, ERATO, Japan Science and Technology Agency (JST), Tokyo, Japan

    Kazuyuki Aihara

Authors
  1. Kantaro Fujiwara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kazuyuki Aihara
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kantaro Fujiwara.

Additional information

This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008

About this article

Cite this article

Fujiwara, K., Aihara, K. Trial-to-trial variability and its influence on higher-order statistics. Artif Life Robotics 13, 470–473 (2009). https://doi.org/10.1007/s10015-008-0598-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-008-0598-1

Key words

Search

Navigation