Skip to main content
Log in

Capturing the bursting dynamics of a two-cell inhibitory network using a one-dimensional map

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

Abstract

Out-of-phase bursting is a functionally important behavior displayed by central pattern generators and other neural circuits. Understanding this complex activity requires the knowledge of the interplay between the intrinsic cell properties and the properties of synaptic coupling between the cells. Here we describe a simple method that allows us to investigate the existence and stability of anti-phase bursting solutions in a network of two spiking neurons, each possessing a T-type calcium current and coupled by reciprocal inhibition. We derive a one-dimensional map which fully characterizes the genesis and regulation of anti-phase bursting arising from the interaction of the T-current properties with the properties of synaptic inhibition. This map is the burst length return map formed as the composition of two distinct one-dimensional maps that are each regulated by a different set of model parameters. Although each map is constructed using the properties of a single isolated model neuron, the composition of the two maps accurately captures the behavior of the full network. We analyze the parameter sensitivity of these maps to determine the influence of both the intrinsic cell properties and the synaptic properties on the burst length, and to find the conditions under which multistability of several bursting solutions is achieved. Although the derivation of the map relies on a number of simplifying assumptions, we discuss how the principle features of this dimensional reduction method could be extended to more realistic model networks.

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.

Institutional subscriptions

Similar content being viewed by others

References

  • Bartos, M., Manor, Y., Nadim, F., Marder, E., & Nusbaum, M. P. (1999). Coordination of fast and slow rhythmic neuronal circuits. Journal of Neuroscience, 19, 6650–6660.

    PubMed  CAS  Google Scholar 

  • Bertram, R., & Sherman, A. (2000). Dynamical complexity and temporal plasticity in pancreatic beta-cells. Journal of Biosciences, 25, 197–209.

    PubMed  CAS  Google Scholar 

  • Bose, A., Manor, Y., & Nadim, F. (2001). Bistable oscillations arising from synaptic depression. SIAM Journal on Applied Mathematics, 62, 706–727.

    Article  Google Scholar 

  • Butera, R. J. (1998). Multirhythmic bursting. Chaos, 8, 274–284.

    Article  PubMed  Google Scholar 

  • Canavier, C. C., Baxter, D. A., Clark, J. W., & Byrne, J. H. (1994). Multiple modes of activity in a model neuron suggest a novel mechanism for the effects of neuromodulators. Journal of Neurophysiology, 72, 872–882.

    PubMed  CAS  Google Scholar 

  • Canavier, C. C., Clark, J. W., & Byrne, J. H. (1991). Simulation of the bursting activity of neuron R15 in Aplysia: Role of ionic currents, calcium balance, and modulatory transmitters. Journal of Neurophysiology, 66, 2107–2124.

    PubMed  CAS  Google Scholar 

  • Chay, T. R., & Rinzel, J. (1985). Bursting, beating, and chaos in an excitable membrane model. Biophysical Journal, 47, 357–366.

    PubMed  CAS  Google Scholar 

  • Coombes, C., & Bressloff, P. (Eds.) (2005). Bursting: The genesis of rhythm in the nervous system. London: World Scientific.

    Google Scholar 

  • Destexhe, A., & Sejnowski, T. J. (2003). Interactions between membrane conductances underlying thalamocortical slow-wave oscillations. Physiological Reviews, 83, 1401–1453.

    PubMed  CAS  Google Scholar 

  • Ermentrout, G. B., & Kopell, N. (1998). Fine structure of neural spiking and synchronization in the presence of conduction delays. In Proceedings of the National Academy of Sciences of the United States of America, 95, 1259–1264.

    Article  PubMed  CAS  Google Scholar 

  • Grillner, S., Markram, H., De Schutter, E., Silberberg, G., & LeBeau, F. E. (2005). Microcircuits in action—from CPGs to neocortex. Trends in Neurosciences, 28, 525–33.

    Article  PubMed  CAS  Google Scholar 

  • Hines, M., Morse, T., Carnevale, N., & Shepard, G. (2004). Model DB: A database to support computational neuroscience. Journal of Computational Neuroscience, 17, 7–11.

    Article  PubMed  Google Scholar 

  • Huguenard, J. R. (1996). Low-threshold calcium currents in central nervous system neurons. Annual Review of Physiology, 58, 329–48.

    Article  PubMed  CAS  Google Scholar 

  • Huguenard, J. R., & McCormick, D. A. (1992). Simulation of the currents involved in rhythmic oscillations in thalamic relay neurons. Journal of Neurophysiology, 68, 1373–1383.

    PubMed  CAS  Google Scholar 

  • Izhikevich, E. M., & Hoppensteadt, F. C. (2004). Classification of bursting mappings. International Journal of Bifurcation and Chaos, 14, 3847–3854.

    Article  Google Scholar 

  • Keener, J., & Sneyd, J. (1998). Mathematical physiology (pp. 154–155). New York: Springer-Verlag.

    Google Scholar 

  • Lechner, H. A., Baxter, D. A., Clark, J. W., & Byrne, J. H. (1996). Bistability and its regulation by serotonin in the endogenously bursting neuron R15 in Aplysia. Journal of Neurophysiology, 75, 957–962.

    PubMed  CAS  Google Scholar 

  • Lee, E., & Terman, D. (1999). Uniqueness and stability of periodic bursting solutions. Journal of Difference Equations, 158, 48–78.

    Article  Google Scholar 

  • Lofaro, T., & Kopell, N. (1999). Timing regulation in a network reduced from voltage-gated equations to a one-dimensional map. Journal of Mathematical Biology, 38, 479–533.

    Article  PubMed  CAS  Google Scholar 

  • Llinas, R. R., & Steriade, M. (2006). Bursting of thalamic neurons and states of vigilance. Journal of Neurophysiology, 95, 3297–3308.

    Article  PubMed  Google Scholar 

  • Manor, Y., & Nadim, F. (2001). Synaptic depression mediates bistability in neuronal networks with recurrent inhibitory connectivity. Journal of Neuroscience, 21, 9460–9470.

    PubMed  CAS  Google Scholar 

  • Marder, E., & Calabrese, R. (1996). Principles of rhythmic motor pattern generation. Physiological Reviews, 76, 687–717.

    PubMed  CAS  Google Scholar 

  • Masino, M. A., & Calabrese, R. L. (2002). Period differences between segmental oscillators produce intersegmental phase differences in the leech heartbeat timing network. Journal of Neurophysiology, 87, 1603–1615.

    PubMed  Google Scholar 

  • Medvedev, G. (2005). Reduction of a model of an excitable cell to a one-dimensional map. Physica D, 202, 37–59.

    Article  Google Scholar 

  • Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35, 193–213.

    Article  PubMed  CAS  Google Scholar 

  • Perkel, D. H., & Mulloney, B. (1974). Motor pattern production in reciprocally inhibitory neurons exhibiting postinhibitory rebound. Science, 185, 181–183.

    Article  PubMed  CAS  Google Scholar 

  • Rubin, J., & Terman, D. (2000). Geometric analysis of population rhythms in synaptically coupled neuronal networks. Neural Computation, 12, 597–645.

    Article  PubMed  CAS  Google Scholar 

  • Satterlie, R. (1985). Reciprocal inhibition and postinhibitory rebound produce reverberation in a locomotor pattern generator. Science, 229, 402–404.

    Article  PubMed  Google Scholar 

  • Selverston, A., & Moulins, M. (1986). The Crustacean stomatogastric system : A model for the study of central nervous systems. Berlin Heidelberg New York: Springer.

    Google Scholar 

  • Skinner, F. K., Kopell, N., & Marder, E. (1994). Mechanisms for oscillation and frequency control in reciprocally inhibitory model neural networks. Journal of Computational Neuroscience, 1, 69–87.

    Article  PubMed  CAS  Google Scholar 

  • Sohal, V., & Huguenard, J. (2001). It takes T to tango. Neuron, 31, 35–45.

    Article  Google Scholar 

  • Terman, D. (1994). Chaotic spikes arising from a model of bursting in excitable membranes. SIAM Journal on Applied Mathematics, 51, 1418–1450.

    Article  Google Scholar 

  • Terman, D., Kopell, N., & Bose, A. (1998). Dynamics of two mutually coupled slow inhibitory neurons. Physica D, 117, 241–275.

    Article  Google Scholar 

  • Traub, R. D., Whittington, M. A., Colling, S. B., Buzsaki, G., & Jefferys, J. G. (1996). Analysis of gamma rhythms in the rat hippocampus in vitro and in vivo. Journal of Physiology, 493, 471–484.

    PubMed  CAS  Google Scholar 

  • Van Vreeswijk, C., Abbott, L. F., & Ermentrout, B. (1994). When inhibition not excitation synchronizes neural firing. Journal of Computational Neuroscience, 1, 313–321.

    Article  PubMed  Google Scholar 

  • Wang, X. J., & Buzsaki, G. (1996). Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. Journal of Neuroscience, 16, 6402–6413

    PubMed  CAS  Google Scholar 

  • Wang, X. J., & Rinzel, J. (1992). Alternating and synchronous rhythms in reciprocally inhibitory model neurons. Neural Computation, 4, 84–97.

    Google Scholar 

  • Wang, X. J., & Rinzel, J. (1994). Spindle rhythmicity in the reticularis thalami nucleus: Synchronization among mutually inhibitory neurons. Neuroscience, 53, 899–904.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Victor Matveev.

Additional information

Action Editor: John Rinzel

Rights and permissions

Reprints and permissions

About this article

Cite this article

Matveev, V., Bose, A. & Nadim, F. Capturing the bursting dynamics of a two-cell inhibitory network using a one-dimensional map. J Comput Neurosci 23, 169–187 (2007). https://doi.org/10.1007/s10827-007-0026-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-007-0026-x

Keywords

Navigation