Skip to main content
Log in

Geometry and dynamics of activity-dependent homeostatic regulation in neurons

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

Abstract

To maintain activity in a functional range, neurons constantly adjust membrane excitability to changing intra- and extracellular conditions. Such activity-dependent homeostatic regulation (ADHR) is critical for normal processing of the nervous system and avoiding pathological conditions. Here, we posed a homeostatic regulation problem for the classical Morris-Lecar (ML) model. The problem was motivated by the phenomenon of the functional recovery of stomatogastric neurons in crustaceans in the absence of neuromodulation. In our study, the regulation of the ionic conductances in the ML model depended on the calcium current or the intracellular calcium concentration. We found an asymptotic solution to the problem under the assumption of slow regulation. The solution provides a full account of the regulation in the case of correlated or anticorrelated changes of the maximal conductances of the calcium and potassium currents. In particular, the solution shows how the target and parameters of the regulation determine which perturbations of the conductances can be compensated by the ADHR. In some cases, the sets of compensated initial perturbations are not convex. On the basis of our analysis we formulated specific questions for subsequent experimental and theoretical studies of ADHR.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Abbreviations

ADHR:

activity-dependent homeostatic regulation

ML:

model Morris-Lecar model

STG:

stomatogastric

References

  • Arnold, V. I., Afrajmovich, V. S., Il’yashenko, Y. S., & Shil’nikov, L. P. (1994). Bifurcation theory and catastrophe theory. Berlin: Springer.

    Google Scholar 

  • Barber, C. B., Dobkin, D. P., & Huhdanpaa, H. (1996). The quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software, 22(4), 469–483.

    Article  Google Scholar 

  • Cressman, J. R., Jr., Ullah, G., Ziburkus, J., Schiff, S. J., & Barreto, E. (2009). The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states: I. Single neuron dynamics. Journal of Computational Neuroscience, 26(2), 159–170.

    Article  PubMed  Google Scholar 

  • Cymbalyuk, G. S., Gaudry, Q., Masino, M. A., & Calabrese, R. L. (2002). Bursting in leech heart interneurons: cell-autonomous and network-based mechanisms. Journal of Neuroscience, 22(24), 10580–10592.

    CAS  PubMed  Google Scholar 

  • Davis, G. W. (2006). Homeostatic control of neural activity: from phenomenology to molecular design. Annual Review of Neuroscience, 29, 307–323.

    Article  CAS  PubMed  Google Scholar 

  • Davis, G. W., & Bezprozvanny, I. (2001). Maintaining the stability of neural function: a homeostatic hypothesis. Annual Review of Physiology, 63, 847–869.

    Article  CAS  PubMed  Google Scholar 

  • El-Samad, H., Goff, J. P., & Khammash, M. (2002). Calcium homeostasis and parturient hypocalcemia: an integral feedback perspective. Journal of Theoretical Biology, 214(1), 17–29.

    Article  CAS  PubMed  Google Scholar 

  • El-Samad, H., Kurata, H., Doyle, J. C., Gross, C. A., & Khammash, M. (2005). Surviving heat shock: control strategies for robustness and performance. Proceedings of the National Academy of Sciences of the United States of America, 102(8), 2736–2741.

    Article  CAS  PubMed  Google Scholar 

  • Ermentrout, E. (2002). Simulating, analyzing, and animating dynamical systems: A guide to XPPAUT for researchers and students. Philadelphia: SIAM.

    Google Scholar 

  • Fellin, T., Pascual, O., & Haydon, P. G. (2006). Astrocytes coordinate synaptic networks: balanced excitation and inhibition. Physiology (Bethesda), 21, 208–215.

    CAS  Google Scholar 

  • Fernandez, F. R., Engbers, J. D., & Turner, R. W. (2007). Firing dynamics of cerebellar purkinje cells. Journal of Neurophysiology, 98(1), 278–294.

    Article  PubMed  Google Scholar 

  • Fields, R. D., Lee, P. R., & Cohen, J. E. (2005). Temporal integration of intracellular Сa2+ signaling networks in regulating gene expression by action potentials. Cell Calcium, 37(5), 433–442.

    Article  CAS  PubMed  Google Scholar 

  • Fradkov, A. L., & Pogromsky, A. Y. (1998). Introduction to control of oscillations and chaos. Singapore: World Scientific.

    Book  Google Scholar 

  • French, L. B., Lanning, C. C., & Harris-Warrick, R. M. (2002). The localization of two voltage-gated calcium channels in the pyloric network of the lobster stomatogastric ganglion. Neuroscience, 112(1), 217–232.

    Article  CAS  PubMed  Google Scholar 

  • Goaillard, J. M., & Marder, E. (2006). Dynamic clamp analyses of cardiac, endocrine, and neural function. Physiology (Bethesda), 21, 197–207.

    Google Scholar 

  • Golowasch, J., Casey, M., Abbott, L. F., & Marder, E. (1999). Network stability from activity-dependent regulation of neuronal conductances. Neural Computation, 11(5), 1079–1096.

    Article  CAS  PubMed  Google Scholar 

  • Guckenheimer, J., & Holmes, P. (1983). Nonlinear oscillations, dynamical systems, and bifurcations of vector fields. New York: Springer.

    Google Scholar 

  • Gunay, G., & Prinz, A.A. (2009). Model calcium sensors for network homeostasis: Sensor and readout parameter analysis from a database of model neuronal networks. Journal of Neuroscience (in press).

  • Izhikevich, E. (2007). Dynamical systems in neuroscience. Cambridge: MIT.

    Google Scholar 

  • Khorkova, O., & Golowasch, J. (2007). Neuromodulators, not activity, control coordinated expression of ionic currents. Journal of Neuroscience, 27(32), 8709–8718.

    Article  CAS  PubMed  Google Scholar 

  • Kunjilwar, K. K., Fishman, H. M., Englot, D. J., O’Neil, R. G., & Walters, E. T. (2009). Long-lasting hyperexcitability induced by depolarization in the absence of detectable Ca2+ signals. Journal of Neurophysiology, 101(3), 1351–1360.

    Article  CAS  PubMed  Google Scholar 

  • Kuznetsov, Y., Levitin, V., & Skovoroda, A. (1996). Continuation of stationary solutions to evolution problems in content. In. Amsterdam: Centrum/Voor Wiskunde en Informatica.

  • LeMasson, G., Marder, E., & Abbott, L. F. (1993). Activity-dependent regulation of conductances in model neurons. Science, 259(5103), 1915–1917.

    Article  CAS  PubMed  Google Scholar 

  • Liu, Z., Golowasch, J., Marder, E., & Abbott, L. F. (1998). A model neuron with activity-dependent conductances regulated by multiple calcium sensors. Journal of Neuroscience, 18(7), 2309–2320.

    CAS  PubMed  Google Scholar 

  • Luther, J. A., Robie, A. A., Yarotsky, J., Reina, C., Marder, E., & Golowasch, J. (2003). Episodic bouts of activity accompany recovery of rhythmic output by a neuromodulator- and activity-deprived adult neural network. Journal of Neurophysiology, 90(4), 2720–2730.

    Article  PubMed  Google Scholar 

  • MacLean, J. N., Zhang, Y., Johnson, B. R., & Harris-Warrick, R. M. (2003). Activity-independent homeostasis in rhythmically active neurons. Neuron, 37(1), 109–120.

    Article  CAS  PubMed  Google Scholar 

  • MacLean, J. N., Zhang, Y., Goeritz, M. L., Casey, R., Oliva, R., Guckenheimer, J., et al. (2005). Activity-independent coregulation of ia and ih in rhythmically active neurons. Journal of Neurophysiology, 94(5), 3601–3617.

    Article  PubMed  Google Scholar 

  • Marder, E., & Prinz, A. A. (2002). Modeling stability in neuron and network function: The role of activity in homeostasis. Bioessays, 24(12), 1145–1154.

    Article  CAS  PubMed  Google Scholar 

  • Marder, E., & Goaillard, J. M. (2006). Variability, compensation and homeostasis in neuron and network function. Nature Reviews Neuroscience, 7(7), 563–574.

    Article  CAS  PubMed  Google Scholar 

  • Marder, E., Abbott, L. F., Turrigiano, G. G., Liu, Z., & Golowasch, J. (1996). Memory from the dynamics of intrinsic membrane currents. Proceedings of the National Academy of Sciences of the United States of America, 93(24), 13481–13486.

    Article  CAS  PubMed  Google Scholar 

  • McAnelly, M. L., & Zakon, H. H. (2000). Coregulation of voltage-dependent kinetics of Na(+) and N(+) currents in electric organ. Journal of Neuroscience, 20(9), 3408–3414.

    CAS  PubMed  Google Scholar 

  • Miller, J. P., & Selverston, A. I. (1982). Mechanisms underlying pattern generation in lobster stomatogastric ganglion as determined by selective inactivation of identified neurons. II. Oscillatory properties of pyloric neurons. Journal of Neurophysiology, 48(6), 1378–1391.

    CAS  PubMed  Google Scholar 

  • Mizrahi, A., Dickinson, P. S., Kloppenburg, P., Fenelon, V., Baro, D. J., Harris-Warrick, R. M., et al. (2001). Long-term maintenance of channel distribution in a central pattern generator neuron by neuromodulatory inputs revealed by decentralization in organ culture. Journal of Neuroscience, 21(18), 7331–7339.

    CAS  PubMed  Google Scholar 

  • Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35(1), 193–213.

    Article  CAS  PubMed  Google Scholar 

  • Nelson, S. B., & Turrigiano, G. G. (2008). Strength through diversity. Neuron, 60(3), 477–482.

    Article  CAS  PubMed  Google Scholar 

  • Olypher, A. V., & Calabrese, R. L. (2007). Using constraints on neuronal activity to reveal compensatory changes in neuronal parameters. Journal of Neurophysiology, 98(6), 3749–3758.

    Article  PubMed  Google Scholar 

  • Olypher, A. V., & Prinz, A. A. (2008). Restrictions on intrinsic neuronal properties following from models of homeostatic regulation of neuronal activity. Abstract Viewer/Itinerary Planner. Washington, DC: Society for Neuroscience Abstracts online. Program No. 376.13.

  • Olypher, A. V., & Calabrese, R. L. (2009). How does maintenance of network activity depend on endogenous dynamics of isolated neurons? Neural Computation, 21(6), 1665–1682.

    Article  PubMed  Google Scholar 

  • Olypher, A. V., Cymbalyuk, G., & Calabrese, R. L. (2006). Hybrid systems analysis of the control of burst duration by low-voltage-activated calcium current in leech heart interneurons. Journal of Neurophysiology, 96(6), 2857–2867.

    Article  PubMed  Google Scholar 

  • Petersen, O. H., & Verkhratsky, A. (2007). Endoplasmic reticulum calcium tunnels integrate signalling in polarised cells. Cell Calcium, 42(4–5), 373–378.

    Article  CAS  PubMed  Google Scholar 

  • Prinz, A. A., Billimoria, C. P., & Marder, E. (2003). Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. Journal of Neurophysiology, 90(6), 3998–4015.

    Article  PubMed  Google Scholar 

  • Prinz, A. A., Bucher, D., & Marder, E. (2004a). Similar network activity from disparate circuit parameters. Nature Neuroscience, 7(12), 1345–1352.

    Article  CAS  PubMed  Google Scholar 

  • Prinz, A. A., Abbott, L. F., & Marder, E. (2004b). The dynamic clamp comes of age. Trends in Neuroscience, 27(4), 218–224.

    Article  CAS  Google Scholar 

  • Prinz, A. A., Smolinski, T. G., Soto-Trevino, C., & F.Nadim (2008). Conductance coregulations in a 2-compartment model of the anterior burster (ab) neuron in the lobster pyloric pacemaker kernel. Abstract Viewer/Itinerary Planner. Washington, DC: Society for Neuroscience Abstracts online. Program No. 376.9.

  • Rinzel, J., & Ermentrout, G. (1998). Analysis of neural excitability and oscillations. In C. Koch & I. Segev (Eds.), Methods in Neuronal Modeling: From Ions to Networks (pp. 251–291). Cambridge: MIT.

    Google Scholar 

  • Schulz, D. J., Goaillard, J. M., & Marder, E. E. (2007). Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression. Proceedings of the National Academy of Sciences of the United States of America, 104(32), 13187–13191.

    Article  PubMed  Google Scholar 

  • Sorensen, M., DeWeerth, S., Cymbalyuk, G., & Calabrese, R. L. (2004). Using a hybrid neural system to reveal regulation of neuronal network activity by an intrinsic current. Journal of Neuroscience, 24(23), 5427–5438.

    Article  CAS  PubMed  Google Scholar 

  • Stellwagen, D., & Malenka, R. C. (2006). Synaptic scaling mediated by glial tnf-alpha. Nature, 440(7087), 1054–1059.

    Article  CAS  PubMed  Google Scholar 

  • Taylor, A. L., Hickey, T. J., Prinz, A. A., & Marder, E. (2006). Structure and visualization of high-dimensional conductance spaces. Journal of Neurophysiology, 96(2), 891–905.

    Article  PubMed  Google Scholar 

  • Thoby-Brisson, M., & Simmers, J. (2002). Long-term neuromodulatory regulation of a motor pattern-generating network: Maintenance of synaptic efficacy and oscillatory properties. Journal of Neurophysiology, 88(6), 2942–2953.

    Article  PubMed  Google Scholar 

  • Turrigiano, G. G. (1999). Homeostatic plasticity in neuronal networks: the more things change, the more they stay the same. Trends in Neuroscience, 22(5), 221–227.

    Article  CAS  Google Scholar 

  • Turrigiano, G. G., & Nelson, S. B. (2004). Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience, 5(2), 97–107.

    Article  CAS  PubMed  Google Scholar 

  • Ullah, G., Cressman, J. R., Jr., Barreto, E., & Schiff, S. J. (2009). The influence of sodium and potassium dynamics on excitability, seizures, and the stability of persistent states. II. Network and glial dynamics. Journal of Computational Neuroscience, 26(2), 171–183.

    Article  PubMed  Google Scholar 

  • Zhang, Y., & Golowasch, J. (2007). Modeling recovery of rhythmic activity: hypothesis for the role of a calcium pump. Neurocomputing, 70(10–12), 1657–1662.

    Article  PubMed  Google Scholar 

  • Zhang, Y., Khorkova, O., Rodriguez, R., & Golowasch, J. (2008). Activity and neuromodulatory input contribute to the recovery of rhythmic output after decentralization in a central pattern generator. Journal of Neurophysiology, 101(1), 372–386.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank L. Abbott, R. Calabrese, C. Gunay, and A. Hudson for helpful discussions, and the reviewers for inspiring and helpful comments. Supported by NIH Grant 1 R01 NS054911-01A1 from NINDS.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrey V. Olypher.

Additional information

Action Editor: J. Rinzel

Rights and permissions

Reprints and permissions

About this article

Cite this article

Olypher, A.V., Prinz, A.A. Geometry and dynamics of activity-dependent homeostatic regulation in neurons. J Comput Neurosci 28, 361–374 (2010). https://doi.org/10.1007/s10827-010-0213-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-010-0213-z

Keywords

Navigation