[go: up one dir, main page]

Skip to main content
Log in

From integrator to resonator neurons: a multiple-timescale scenario

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

Neuronal excitability manifests itself through a number of key markers of the dynamics and it allows to classify neurons into different groups with identifiable voltage responses to input currents. In particular, two main types of excitability can be defined based on experimental observations, and their underlying mathematical models can be distinguished through separate bifurcation scenarios. Related to these two main types of excitable neural membranes, and associated models, is the distinction between integrator and resonator neurons. One important difference between integrator and resonator neurons, and their associated model representations, is the presence in resonators, as opposed to integrators, of subthreshold oscillations following spikes. Switches between one neural category and the other can be observed and/or created experimentally, and reproduced in models mostly through changes of the bifurcation structure. In the present work, we propose a new scenario of switch between integrator and resonator neurons based upon multiple-timescale dynamics and the possibility to force an integrator neuron with a specific time-dependent slowly varying current. The key dynamical object organising this switch is a so-called folded-saddle singularity. We also showcase the reverse switch via a folded-node singularity and propose an experimental protocol to test our theoretical predictions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data availability

The authors declare that there are no data associated with this work.

References

  1. Izhikevich, E.M.: Neural excitability, spiking and bursting. Int. J. Bifurc. Chaos 10(06), 1171–1266 (2000). https://doi.org/10.1142/S0218127400000840

    Article  MathSciNet  MATH  Google Scholar 

  2. Izhikevich, E.M.: Resonate-and-fire neurons. Neural Netw. 14(6–7), 883–894 (2001). https://doi.org/10.1016/S0893-6080(01)00078-8

    Article  Google Scholar 

  3. Prescott, S., Ratté, S., Koninck, Y., Sejnowski, T.: Pyramidal neurons switch from integrators in vitro to resonators under in vivo-like conditions. J. Neurophysiol. 100(6), 3030–42 (2008). https://doi.org/10.1152/jn.90634.2008

    Article  Google Scholar 

  4. Rinzel, J., Ermentrout, G.B.: Analysis of neural excitability and oscillations. In: Koch, C., Segev, I. (eds.) Methods in Neuronal Modeling, vol. 2, 2nd edn., pp. 251–292. MIT Press, Cambridge (1998)

    Google Scholar 

  5. Benoît, E., Callot, J.-L., Diener, F., Diener, M.: Chasse au canard. Collect. Math. 32(1–2), 37–119 (1981)

    MathSciNet  MATH  Google Scholar 

  6. Izhikevich, E.M.: Dynamical Systems in Neuroscience. MIT Press, Cambridge (2007)

    Google Scholar 

  7. Desroches, M., Krupa, M., Rodrigues, S.: Inflection, canards and excitability threshold in neuronal models. J. Math. Biol. 67(4), 989–1017 (2013). https://doi.org/10.1007/s00285-012-0576-z

    Article  MathSciNet  MATH  Google Scholar 

  8. Wechselberger, M., Mitry, J., Rinzel, J.: Canard theory and excitability. In: Kloeden, P., Pötzsche, C. (eds.) Nonautonomous Dynamical Systems in the Life Sciences. Lecture Notes in Mathematics, vol. 2102, pp. 89–132. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03080-7_3

  9. Kirst, C., Ammer, J., Felmy, F., Herz, A., Stemmler, M.: Fundamental structure and modulation of neuronal excitability: synaptic control of coding, resonance, and network synchronization. BioRxiv 022475 (2015). https://doi.org/10.1101/022475

  10. Fellous, J.-M., Houweling, A., Modi, R., Rao, R., Tiesinga, P., Sejnowski, T.: Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons. J. Neurophysiol. 85(4), 1782–1787 (2001). https://doi.org/10.1152/jn.2001.85.4.1782

    Article  Google Scholar 

  11. Yi, G., Wang, J., Wei, X., Tsang, K.-M., Chan, W.-L., Deng, B., Han, C.-X.: Exploring how extracellular electric field modulates neuron activity through dynamical analysis of a two-compartment neuron model. J. Comput. Neurosci. 36, 383–399 (2013). https://doi.org/10.1007/s10827-013-0479-z

    Article  MathSciNet  Google Scholar 

  12. Feldmann, J., Youngblood, N., Wright, C.D., Bhaskaran, H., Pernice, W.H.: All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569(7755), 208–214 (2019). https://doi.org/10.1038/s41586-019-1157-8

    Article  Google Scholar 

  13. Roach, J., Eniwaye, B., Booth, V., Sander, L., Zochowski, M.: Acetylcholine mediates dynamic switching between information coding schemes in neuronal networks. Front. Syst. Neurosci. 13, 64 (2019). https://doi.org/10.3389/fnsys.2019.00064

    Article  Google Scholar 

  14. Al-Darabsah, I., Campbell, S.: M-current induced Bogdanov–Takens bifurcation and switching of neuron excitability class. J. Math. Neurosci. 11(1), 1–26 (2021). https://doi.org/10.1186/s13408-021-00103-5

    Article  MathSciNet  MATH  Google Scholar 

  15. Guantes, R., Polavieja, G.: Variability in noise-driven integrator neurons. Phys. Rev. E 71(1), 011911 (2005). https://doi.org/10.1103/PhysRevE.71.011911

    Article  MathSciNet  Google Scholar 

  16. Zhao, Z., Gu, H.-G.: Transitions between classes of neuronal excitability and bifurcations induced by autapse. Sci. Rep. 7(1), 6760 (2017). https://doi.org/10.1038/s41598-017-07051-9

    Article  Google Scholar 

  17. Hutcheon, B., Yarom, Y.: Resonance, oscillation and the intrinsic frequency preferences of neurons. Trends Neurosci. 23(5), 216–222 (2000). https://doi.org/10.1016/S0166-2236(00)01547-2

    Article  Google Scholar 

  18. Macherey, O., Carlyon, R., van Wieringen, A., Wouters, J.: A dual-process integrator–resonator model of the electrically stimulated human auditory nerve. J. Assoc. Res. Otolaryngol. 8, 84–104 (2007). https://doi.org/10.1007/s10162-006-0066-3

    Article  Google Scholar 

  19. Muresan, R., Savin, C.: Resonance or integration? Self-sustained dynamics and excitability of neural microcircuits. J. Neurophysiol. 97(3), 1911–1930 (2007). https://doi.org/10.1152/jn.01043.2006

    Article  Google Scholar 

  20. Desroches, M., Guckenheimer, J., Krauskopf, B., Kuehn, C., Osinga, H.M., Wechselberger, M.: Mixed-mode oscillations with multiple time scales. SIAM Rev. 54(2), 211–288 (2012). https://doi.org/10.1137/100791233

    Article  MathSciNet  MATH  Google Scholar 

  21. Desroches, M., Krupa, M., Rodrigues, S.: Spike-adding in parabolic bursters: the role of folded-saddle canards. Physica D 331, 58–70 (2016). https://doi.org/10.1016/j.physd.2016.05.011

    Article  MathSciNet  MATH  Google Scholar 

  22. Mitry, J., Wechselberger, M.: Folded saddles and faux canards. SIAM J. Appl. Dyn. Syst. 16(1), 546–596 (2017). https://doi.org/10.1137/15M1045065

    Article  MathSciNet  MATH  Google Scholar 

  23. Desroches, M., Guillamon, A., Ponce, E., Prohens, R., Rodrigues, S., Teruel, A.E.: Canards, folded nodes, and mixed-mode oscillations in piecewise-linear slow-fast systems. SIAM Rev. 58(4), 653–691 (2016). https://doi.org/10.1137/15M1014528

    Article  MathSciNet  MATH  Google Scholar 

  24. Krinskiĭ, V., Kokoz, I.M.: Analysis of the equations of excitable membranes. I. Reduction of the Hodgkins–Huxley equations to a 2d order system. Biofizika 18(3), 506–511 (1973)

    Google Scholar 

  25. Rinzel, J.: On repetitive activity in nerve. Fed. Proc. 37(14), 2793–2802 (1978)

    Google Scholar 

  26. Moehlis, J.: Canards for a reduction of the Hodgkin–Huxley equations. J. Math. Biol. 52, 141–153 (2006). https://doi.org/10.1007/s00285-005-0347-1

    Article  MathSciNet  MATH  Google Scholar 

  27. Ermentrout, B.: Type I membranes, phase resetting curves, and synchrony. Neural Comput. 8(5), 979–1001 (1996). https://doi.org/10.1162/neco.1996.8.5.979

    Article  Google Scholar 

  28. Fenichel, N.: Geometric singular perturbation theory for ordinary differential equations. J. Differ. Equ. 31(1), 53–98 (1979). https://doi.org/10.1016/0022-0396(79)90152-9

    Article  MathSciNet  MATH  Google Scholar 

  29. Desroches, M., Kirk, V.: Spike-adding in a canonical three-time-scale model: superslow explosion and folded-saddle canards. SIAM J. Appl. Dyn. Syst. 17(3), 1989–2017 (2018). https://doi.org/10.1137/17M1143411

    Article  MathSciNet  MATH  Google Scholar 

  30. Rinzel, J.: A formal classification of bursting mechanisms in excitable systems. In: International Congress of Mathematicians. Berkeley, California, USA, 3–11 Aug 1986, vol. II, pp. 1578–1593. American Mathematical Society, Providence (1987)

  31. Amir, R., Michaelis, M., Devor, M.: Burst discharge in primary sensory neurons: triggered by subthreshold oscillations, maintained by depolarizing afterpotentials. J. Neurosci. 22, 1187–1198 (2002). https://doi.org/10.1523/JNEUROSCI.22-03-01187.2002

    Article  Google Scholar 

  32. Ori, H., Hazan, H., Marder, E., Marom, S.: Dynamic clamp constructed phase diagram for the Hodgkin and Huxley model of excitability. Proc. Natl. Acad. Sci. USA 117(7), 3575–3582 (2020). https://doi.org/10.1073/pnas.1916514117

    Article  Google Scholar 

  33. Sharp, A.A., O’Neil, M.B., Abbott, L.F., Marder, E.: Dynamic clamp: computer-generated conductances in real neurons. J. Neurophysiol. 69(3), 992–995 (1993). https://doi.org/10.1152/jn.1993.69.3.992

    Article  Google Scholar 

  34. Wechselberger, M.: Existence and bifurcation of canards in \(\mathbb{R} ^{3}\) in the case of a folded node. SIAM J. Appl. Dyn. Syst. 4(1), 101–139 (2005). https://doi.org/10.1137/030601995

    Article  MathSciNet  MATH  Google Scholar 

  35. Haiduc, R.: Horseshoes in the forced van der Pol system. Nonlinearity 22(1), 213–237 (2008). https://doi.org/10.1088/0951-7715/22/1/011

    Article  MathSciNet  MATH  Google Scholar 

  36. Schiff, S.J., Jerger, K., Duong, D.H., Chang, T., Spano, M.L., Ditto, W.L.: Controlling chaos in the brain. Nature 370(6491), 615–620 (1994). https://doi.org/10.1038/370615a0

    Article  Google Scholar 

  37. Faure, P., Korn, H.: Is there chaos in the brain? I. Concepts of nonlinear dynamics and methods of investigation. C. R. l’Acad. Sci. Ser. III Sci. Vie 324(9), 773–793 (2001). https://doi.org/10.1016/S0764-4469(01)01377-4

    Article  Google Scholar 

  38. Korn, H., Faure, P.: Is there chaos in the brain? II. Experimental evidence and related models. C. R. Biol. 326(9), 787–840 (2003). https://doi.org/10.1016/j.crvi.2003.09.011

    Article  Google Scholar 

  39. Erchova, I., McGonigle, D.J.: Rhythms of the brain: an examination of mixed mode oscillation approaches to the analysis of neurophysiological data. Chaos Interdiscip. J. Nonlinear Sci. 18(1), 015115 (2008). https://doi.org/10.1063/1.2900015

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

SR was supported by Ikerbasque (The Basque Foundation for Science). GG and SR acknowledge support from the Basque Government through the BERC 2022-2025 program and by the Ministry of Science and Innovation: BCAM Severo Ochoa accreditation CEX2021-001142-S / MICIN / AEI / 10.13039/501100011033 and through project RTI2018-093860-B-C21 funded by (AEI/FEDER, UE) and acronym “MathNEURO.” MD and SR acknowledge the support of Inria via the Associated Team “NeuroTransSF.”

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guillaume Girier.

Ethics declarations

Conflict of interest

The authors declare that there are no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Girier, G., Desroches, M. & Rodrigues, S. From integrator to resonator neurons: a multiple-timescale scenario. Nonlinear Dyn 111, 16545–16556 (2023). https://doi.org/10.1007/s11071-023-08687-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-023-08687-1

Keywords