Skip to main content

Advertisement

Log in

On the stationary state of a network of inhibitory spiking neurons

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

Abstract

The background activity of a cortical neural network is modeled by a homogeneous integrate-and-fire network with unreliable inhibitory synapses. For the case of fast synapses, numerical and analytical calculations show that the network relaxes into a stationary state of high attention. The majority of the neurons has a membrane potential just below the threshold; as a consequence the network can react immediately – on the time scale of synaptic transmission- on external pulses. The neurons fire with a low rate and with a broad distribution of interspike intervals. Firing events of the total network are correlated over short time periods. The firing rate increases linearly with external stimuli. In the limit of infinitely large networks, the synaptic noise decreases to zero. Nevertheless, the distribution of interspike intervals remains broad.

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

  • Abbott, L. F., & Van Vreeswijk, C. (1993). Asynchronous states in networks of pulse-coupled oscillators. Physical Review E, 48, 1483–1490.

    Article  Google Scholar 

  • Abeles, M. (1991). Corticonics. Cambridge: Cambridge University Press.

    Google Scholar 

  • Allen, C., & Stevens, C. F. (1994). An evaluation of causes for unreliability of synaptic transmission. Proceedings of the National Academy of Sciences of the United States of America, 91, 10380–10383.

    Article  PubMed  CAS  Google Scholar 

  • Aviel, Y., Horn, D., Abeles, M. (2005). Memory capacity of balanced networks. Neural Computation, 17, 691–713.

    Article  PubMed  Google Scholar 

  • Aviel, Y., Mehring, Abeles, M., & Horn, D. (2003). On embedding synfire chains in a balanced network. Neural Computation, 15(6), 1321–1340.

    Article  PubMed  CAS  Google Scholar 

  • Brunel, N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience, 8, 183–208.

    Article  PubMed  CAS  Google Scholar 

  • Brunel, N., & Hakim, V. (1999). Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Computation, 11, 1621–1671.

    Article  PubMed  CAS  Google Scholar 

  • Ernst, U., Pawelzik, K., & Geisel, T. (1995). Synchronization induced by temporal delays in pulse-coupled oscillators. Physical Review Letters, 74, 1570–1574.

    Article  PubMed  CAS  Google Scholar 

  • Gerstner, W., & Kistler, W. (2002). Spiking Neuron Models. Cambridge: Cambridge University Press.

    Google Scholar 

  • Hansel, D., Mato, G., & Meunier, C. (1993). Clustering and slow switching in globally coupled phase oscillators. Physical Review, E48, 3470–3477.

    Article  PubMed  Google Scholar 

  • Hertz, J. A., Richmond, B. J., & Nilsen, K. (2003). Anomalous response variability in a balanced cortical network. Neurocomputers, 52–54, 787–792.

    Article  Google Scholar 

  • Kestler, J., & Kinzel, W. (2006). Multifractal distribution of spike intervals for two neurons coupled by unreliable pulses. Journal of Physics A, 39, L461–L466.

    Article  Google Scholar 

  • Lisman, J. E. (1997). Bursts as a unit of neural information: Making unreliable synapses reliable. Trends in Neurosciences, 20, 38–43.

    Article  PubMed  CAS  Google Scholar 

  • Mirollo, R. E., & Strogatz, S. H. (1990). SIAM Journal of Applied Mathematics, 50, 1645.

    Article  Google Scholar 

  • Shadlen, M. N., & Newsome, W. T. (1994). Noise, neural codes and cortical organization. Current Opinion in Neurobiology 4(4), 569–579.

    Article  PubMed  CAS  Google Scholar 

  • Strogatz, S. H. (2001). Nonlinear dynamics and chaos. Cambridge: Cambridge University Press.

    Google Scholar 

  • Timme, M., Wolf, F., & Geisel, T. (2002). Coexistence of regular and irregular dynamics in complex networks of pulse-coupled oscillators. Physical Review Letters, 89, 258701.

    Article  PubMed  CAS  Google Scholar 

  • Trevelyan, A. J., & Watkinson, O. (2005). Does inhibition balance excitation in neocortex? Progress in Biophysics and Molecular Biology, 87, 109–143.

    Article  PubMed  Google Scholar 

  • Tuckwell, H. C. (1998). Introduction to theoretical neurobiology. Cambridge: Cambridge University Press.

    Google Scholar 

  • Van Vreeswijk, C. (1996). Partial synchronization in populations of pulse-coupled oscillators. Physical Review E, 54, 5522–5537.

    Article  Google Scholar 

  • Van Vreeswijk, C., & Sompolinsky, H. (1996). Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science, 274, 1724–1726.

    Article  PubMed  Google Scholar 

  • Zillmer, R., Livi, R., Politi, A., & Torcini, A. (2006). Desynchronization in diluted neural networks. Physical Review E, 74, 036203.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wolfgang Kinzel.

Additional information

Action Editor: Misha Tsodyks

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kinzel, W. On the stationary state of a network of inhibitory spiking neurons. J Comput Neurosci 24, 105–112 (2008). https://doi.org/10.1007/s10827-007-0049-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-007-0049-3

Keywords

Navigation