Skip to main content
Top
Published in: Journal of Computational Neuroscience 1/2011

01-08-2011

Non-weak inhibition and phase resetting at negative values of phase in cells with fast-slow dynamics at hyperpolarized potentials

Authors: Myongkeun Oh, Victor Matveev

Published in: Journal of Computational Neuroscience | Issue 1/2011

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Phase response is a powerful concept in the analysis of both weakly and non-weakly perturbed oscillators such as regularly spiking neurons, and is applicable if the oscillator returns to its limit cycle trajectory between successive perturbations. When the latter condition is violated, a formal application of the phase return map may yield phase values outside of its definition domain; in particular, strong synaptic inhibition may result in negative values of phase. The effect of a second perturbation arriving close to the first one is undetermined in this case. However, here we show that for a Morris–Lecar model of a spiking cell with strong time scale separation, extending the phase response function definition domain to an additional negative value branch allows to retain the accuracy of the phase response approach in the face of such strong inhibitory coupling. We use the resulting extended phase response function to accurately describe the response of a Morris–Lecar oscillator to consecutive non-weak synaptic inputs. This method is particularly useful when analyzing the dynamics of three or more non-weakly coupled cells, whereby more than one synaptic perturbation arrives per oscillation cycle into each cell. The method of perturbation prediction based on the negative-phase extension of the phase response function may be applicable to other excitable cell models characterized by slow voltage dynamics at hyperpolarized potentials.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
go back to reference Achuthan, S., & Canavier, C. C. (2009). Phase-resetting curves determine synchronization, phase locking, and clustering in networks of neural oscillators. Journal of Neuroscience, 29, 5218–5233.PubMedCrossRef Achuthan, S., & Canavier, C. C. (2009). Phase-resetting curves determine synchronization, phase locking, and clustering in networks of neural oscillators. Journal of Neuroscience, 29, 5218–5233.PubMedCrossRef
go back to reference Acker, C. D., Kopell, N., & White, J. A. (2003). Synchronization of strongly coupled excitatory neurons: Relating network behavior to biophysics. Journal of Computational Neuroscience, 15, 71–90.PubMedCrossRef Acker, C. D., Kopell, N., & White, J. A. (2003). Synchronization of strongly coupled excitatory neurons: Relating network behavior to biophysics. Journal of Computational Neuroscience, 15, 71–90.PubMedCrossRef
go back to reference Bressloff, P. C., & Coombes, S. (2000). Dynamics of strongly-coupled spiking neurons. Neural Computation, 12, 91–129.PubMedCrossRef Bressloff, P. C., & Coombes, S. (2000). Dynamics of strongly-coupled spiking neurons. Neural Computation, 12, 91–129.PubMedCrossRef
go back to reference Canavier, C. C., Butera, R. J., Dror, R. O., Baxter, D. A., Clark, J. W., & Byrne, J. H. (1997). Phase response characteristics of model neurons determine which patterns are expressed in a ring circuit model of gait generation. Biological Cybernetics, 77, 367–380.PubMedCrossRef Canavier, C. C., Butera, R. J., Dror, R. O., Baxter, D. A., Clark, J. W., & Byrne, J. H. (1997). Phase response characteristics of model neurons determine which patterns are expressed in a ring circuit model of gait generation. Biological Cybernetics, 77, 367–380.PubMedCrossRef
go back to reference Canavier, C. C., Baxter, D. A., Clark, J. W., & Byrne, J. H. (1999). Control of multistability in ring circuits of oscillators. Biological Cybernetics, 80, 87–102.PubMedCrossRef Canavier, C. C., Baxter, D. A., Clark, J. W., & Byrne, J. H. (1999). Control of multistability in ring circuits of oscillators. Biological Cybernetics, 80, 87–102.PubMedCrossRef
go back to reference Canavier, C. C., Kazanci, F. G., & Prinz, A. A. (2009). Phase resetting curves allow for simple and accurate prediction of robust N.:1 phase locking for strongly coupled neural oscillators. Biophysical Journal, 97, 59–73.PubMedCrossRef Canavier, C. C., Kazanci, F. G., & Prinz, A. A. (2009). Phase resetting curves allow for simple and accurate prediction of robust N.:1 phase locking for strongly coupled neural oscillators. Biophysical Journal, 97, 59–73.PubMedCrossRef
go back to reference Canavier, C. C., & Achuthan, S. (2010). Pulse-coupled oscillators and the phase resetting curve. Mathematical Biosciences, 226, 77–96.PubMedCrossRef Canavier, C. C., & Achuthan, S. (2010). Pulse-coupled oscillators and the phase resetting curve. Mathematical Biosciences, 226, 77–96.PubMedCrossRef
go back to reference Dror, R. O., Canavier, C. C., Butera, R. J., Clark, J. W., & Byrne, J. H. (1999). A mathematical critereon based on the phase response curves for stability in a ring of coupled oscillators. Biological Cybernetics, 80, 11–23.PubMedCrossRef Dror, R. O., Canavier, C. C., Butera, R. J., Clark, J. W., & Byrne, J. H. (1999). A mathematical critereon based on the phase response curves for stability in a ring of coupled oscillators. Biological Cybernetics, 80, 11–23.PubMedCrossRef
go back to reference Ermentrout, G. B. (1996). Type I. membranes, phase resetting curves, and synchrony. Neural Computation, 8, 979–1001.PubMedCrossRef Ermentrout, G. B. (1996). Type I. membranes, phase resetting curves, and synchrony. Neural Computation, 8, 979–1001.PubMedCrossRef
go back to reference Ermentrout, G. B., & Kopell, N. (1984). Frequency plateaus in a chain of weakly coupled oscillators. SIAM Journal on Mathematical Analysis, 15, 215–237.CrossRef Ermentrout, G. B., & Kopell, N. (1984). Frequency plateaus in a chain of weakly coupled oscillators. SIAM Journal on Mathematical Analysis, 15, 215–237.CrossRef
go back to reference Ermentrout, G. B., & Kopell, N. (1986). Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM Journal on Mathematical Analysis, 46, 233–253.CrossRef Ermentrout, G. B., & Kopell, N. (1986). Parabolic bursting in an excitable system coupled with a slow oscillation. SIAM Journal on Mathematical Analysis, 46, 233–253.CrossRef
go back to reference Ermentrout, G. B., & Kopell, N. (1990). Oscillator death in systems of coupled neural oscillators. SIAM Journal on Mathematical Analysis, 50, 125–146.CrossRef Ermentrout, G. B., & Kopell, N. (1990). Oscillator death in systems of coupled neural oscillators. SIAM Journal on Mathematical Analysis, 50, 125–146.CrossRef
go back to reference Ermentrout, G. B., & Kopell, N. (1991). Multiple pulse interactions and averaging in systems of coupled neural oscillators. Journal of Mathematical Biology, 29, 195–217.CrossRef Ermentrout, G. B., & Kopell, N. (1991). Multiple pulse interactions and averaging in systems of coupled neural oscillators. Journal of Mathematical Biology, 29, 195–217.CrossRef
go back to reference Golubitsky, M., Josic, K., & Shea-Brown, E. (2006). Winding numbers and average frequencies in phase oscillator networks. Journal of Nonlinear Science, 16, 201–231.CrossRef Golubitsky, M., Josic, K., & Shea-Brown, E. (2006). Winding numbers and average frequencies in phase oscillator networks. Journal of Nonlinear Science, 16, 201–231.CrossRef
go back to reference Guckenheimer, J. (1975). Isochrons and Phaseless Sets. Journal of Mathematical Biology, 1, 259–273.CrossRef Guckenheimer, J. (1975). Isochrons and Phaseless Sets. Journal of Mathematical Biology, 1, 259–273.CrossRef
go back to reference Gutkin, B. S., Ermentrout, G. B., & Reyes, A. D. (2005). Phase-response curves give the responses of neurons to transient inputs. Journal of Neurophysiology, 94, 1623–1635.PubMedCrossRef Gutkin, B. S., Ermentrout, G. B., & Reyes, A. D. (2005). Phase-response curves give the responses of neurons to transient inputs. Journal of Neurophysiology, 94, 1623–1635.PubMedCrossRef
go back to reference Hansel, D., & Mato, G. (2003). Asynchronous states and the emergence of synchrony in large networks of interacting excitatory and inhibitory neurons. Neural Computation, 15, 1–56.PubMedCrossRef Hansel, D., & Mato, G. (2003). Asynchronous states and the emergence of synchrony in large networks of interacting excitatory and inhibitory neurons. Neural Computation, 15, 1–56.PubMedCrossRef
go back to reference Hansel, D., Mato, G., & Meunier, C. (1995). Synchrony in excitatory neural networks. Neural Computation, 7, 307–337.PubMedCrossRef Hansel, D., Mato, G., & Meunier, C. (1995). Synchrony in excitatory neural networks. Neural Computation, 7, 307–337.PubMedCrossRef
go back to reference Izhikevich, E. M. (2006). Dynamics systems in neuroscience: The geometry of excitability and bursting (Chapter 10). Synchronization. Cambridge: MIT. Izhikevich, E. M. (2006). Dynamics systems in neuroscience: The geometry of excitability and bursting (Chapter 10). Synchronization. Cambridge: MIT.
go back to reference Izhikevich, E. M., & Kuramoto, Y. (2006). Weakly coupled oscillators (Vol. 5, p. 448). Elsevier: Encyclopedia of Mathematical Physics. Izhikevich, E. M., & Kuramoto, Y. (2006). Weakly coupled oscillators (Vol. 5, p. 448). Elsevier: Encyclopedia of Mathematical Physics.
go back to reference Kuramoto, Y. (1984). Chemical oscillations, waves, and turbulence. Berlin: Springer. Kuramoto, Y. (1984). Chemical oscillations, waves, and turbulence. Berlin: Springer.
go back to reference Latham, P. E., Richmond, B. J., Nelson, P. G., & Nirenberg, S. (2000). Intrinsic dynamics in neuronal networks. I. theory. Journal of Neurophysiology, 83, 808–827.PubMed Latham, P. E., Richmond, B. J., Nelson, P. G., & Nirenberg, S. (2000). Intrinsic dynamics in neuronal networks. I. theory. Journal of Neurophysiology, 83, 808–827.PubMed
go back to reference Maran, S. K., & Canavier, C. C. (2008). Using phase resetting to predict 1:1 and 2:2 locking in two neuron networks in which firing order is not always preserved. Journal of Computational Neuroscience, 24, 37–55.PubMedCrossRef Maran, S. K., & Canavier, C. C. (2008). Using phase resetting to predict 1:1 and 2:2 locking in two neuron networks in which firing order is not always preserved. Journal of Computational Neuroscience, 24, 37–55.PubMedCrossRef
go back to reference Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35, 193–213.PubMedCrossRef Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35, 193–213.PubMedCrossRef
go back to reference Netoff, T. I., Banks, M. I., Dorval, A. D., Acker, C. D., Haas, J. S., Kopell, N., et al. (2005). Synchronization in hybrid neuronal networks of the Hippocampal formation. Journal of Neurophysiology, 93, 1197–1208.PubMedCrossRef Netoff, T. I., Banks, M. I., Dorval, A. D., Acker, C. D., Haas, J. S., Kopell, N., et al. (2005). Synchronization in hybrid neuronal networks of the Hippocampal formation. Journal of Neurophysiology, 93, 1197–1208.PubMedCrossRef
go back to reference Oh, M., & Matveev, V. (2009). Loss of phase-locking in non-weakly coupled inhibitory networks of type-I. model neurons. Journal of Computational Neuroscience, 26(2), 303–320.PubMedCrossRef Oh, M., & Matveev, V. (2009). Loss of phase-locking in non-weakly coupled inhibitory networks of type-I. model neurons. Journal of Computational Neuroscience, 26(2), 303–320.PubMedCrossRef
go back to reference Oprisan, S. A., & Canavier, C. C. (2001). Stability analysis of rings of pulse-coupled oscillators: The effect of phase resetting in the second cycle after the pulse is important at synchrony and for long pulses. Journal of Differential Equations and Dynamical Systems, 9, 243–258. Oprisan, S. A., & Canavier, C. C. (2001). Stability analysis of rings of pulse-coupled oscillators: The effect of phase resetting in the second cycle after the pulse is important at synchrony and for long pulses. Journal of Differential Equations and Dynamical Systems, 9, 243–258.
go back to reference Oprisan, S. A., Prinz, A. A., & Canavier, C. C. (2004). Phase resetting and phase locking in hybrid circuits of one model and one biological neuron. Biophysical Journal, 87, 2283–2298.PubMedCrossRef Oprisan, S. A., Prinz, A. A., & Canavier, C. C. (2004). Phase resetting and phase locking in hybrid circuits of one model and one biological neuron. Biophysical Journal, 87, 2283–2298.PubMedCrossRef
go back to reference Pfeuty, B., Mato, G., Golomb, D., & Hansel, D. (2003). Electrical synapses and synchrony: The role of intrinsic currents. Journal of Neuroscience, 23, 6280–6294.PubMed Pfeuty, B., Mato, G., Golomb, D., & Hansel, D. (2003). Electrical synapses and synchrony: The role of intrinsic currents. Journal of Neuroscience, 23, 6280–6294.PubMed
go back to reference Rinzel, J., & Ermentrout, B. (1998). Analysis of neural excitability and oscillations. In C. Koch & I. Segev (Eds.). Methods in neuronal modeling: From ions to networks (2nd ed.). Cambridge: MIT. Rinzel, J., & Ermentrout, B. (1998). Analysis of neural excitability and oscillations. In C. Koch & I. Segev (Eds.). Methods in neuronal modeling: From ions to networks (2nd ed.). Cambridge: MIT.
go back to reference van Vreeswijk, C., Abbott, L. F., & Ermentrout, B. (1994). When inhibition not excitation synchronizes neural firing. Journal of Computational Neuroscience, 1, 313–321.PubMedCrossRef van Vreeswijk, C., Abbott, L. F., & Ermentrout, B. (1994). When inhibition not excitation synchronizes neural firing. Journal of Computational Neuroscience, 1, 313–321.PubMedCrossRef
go back to reference Winfree, A. T. (1974). Patterns of phase compromise in biological cycles. Journal of Mathematical Biology, 1, 73–95.CrossRef Winfree, A. T. (1974). Patterns of phase compromise in biological cycles. Journal of Mathematical Biology, 1, 73–95.CrossRef
go back to reference Winfree, A. T. (2001). The geometry of biological time (2nd edn). New York: Springer. Winfree, A. T. (2001). The geometry of biological time (2nd edn). New York: Springer.
go back to reference Wang, X. J., & Buzsáki, G. (1996). Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. Journal of Neuroscience, 16, 6402–6413.PubMed Wang, X. J., & Buzsáki, G. (1996). Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. Journal of Neuroscience, 16, 6402–6413.PubMed
Metadata
Title
Non-weak inhibition and phase resetting at negative values of phase in cells with fast-slow dynamics at hyperpolarized potentials
Authors
Myongkeun Oh
Victor Matveev
Publication date
01-08-2011
Publisher
Springer US
Published in
Journal of Computational Neuroscience / Issue 1/2011
Print ISSN: 0929-5313
Electronic ISSN: 1573-6873
DOI
https://doi.org/10.1007/s10827-010-0292-x

Other articles of this Issue 1/2011

Journal of Computational Neuroscience 1/2011 Go to the issue

Premium Partner