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Equivalency of spike-frequency and h-current-based adaptation in a Wilson-Cowan field model

Ronja Strömsdörfer1,2,* and Klaus Obermayer1,2,3

  • *Contact author: stroemsdoerfer@tu-berlin.de

Phys. Rev. Research 8, 023348 – Published 26 June, 2026

DOI: https://doi.org/10.1103/3sd6-d2w9

Abstract

During slow-wave sleep, the brain produces traveling waves of slow oscillations (SOs; ≤2Hz), characterized by the propagation of alternating high- and low-activity states. They play a crucial role in memory consolidation and are frequently investigated, empirically and with the support of in silico studies. The question of internal mechanisms that modulate traveling waves of SOs is still unanswered even though it is established that it is an adaptation mechanism that mediates them. One of the main mechanisms investigated is spike-frequency adaptation, a hyperpolarizing feedback current that is activated during periods of high activity. An alternative mechanism that has recently been suggested is based on hyperpolarization-activated (h-)currents, which are positive feedback currents that are activated in low-activity states. Both adaptation mechanisms were shown to feature SO-like dynamics in neuronal populations, and the inclusion of a spatial domain seems to enhance observable differences in their effects. To investigate the effects of both adaptation mechanisms on the neural dynamics in detail, we examine a spatially extended, adaptive Wilson-Cowan model of interacting excitatory and inhibitory populations of neurons with local spatial coupling. The excitatory populations are equipped with either one or the other adaptation mechanism. We describe both mechanisms using the same dynamical equation and include the inverse mode of action by changing the signs of the adaptation strength and the gain of activation function. We then show that the dynamical systems including the two feedback currents are mathematically equivalent under a compensatory external input, which depends on the adaptation strength and which in turn leads to a shift in state space of the otherwise equivalent bifurcation structure. A detailed analysis of state space shows that strong enough adaptation is required to induce traveling waves of SO-like dynamics, while only stationary or homogeneous activity patterns emerge if adaptation is weak. Additionally, adaptation modulates the properties of the spatiotemporal activity patterns, such as temporal frequencies, spatial frequencies, and the speed of the traveling waves, all of which increase with increasing strength. Though being dynamically equivalent, our results also explain why location-dependent variations in feedback strength cause differences in the propagation of traveling waves between both adaptation mechanisms.

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References (85)

  1. M. V. Sanchez-Vives, M. Massimini, and M. Mattia, Shaping the default activity pattern of the cortical network, Neuron 94, 993 (2017).
  2. M. V. Sanchez-Vives, Origin and dynamics of cortical slow oscillations, Curr. Opin. Physiol. 15, 217 (2020).
  3. M. Steriade, A. Nunez, and F. Amzica, A novel slow (≤1 Hz) oscillation of neocortical neurons in vivo: Depolarizing and hyperpolarizing components, J. Neurosci. 13, 3252 (1993).
  4. Note, however, that cortical down-states, which correspond to so-called K complexes, have already been described in the very first sleep electroencephalography studies in the 1930s [5].
  5. A. L. Loomis, E. N. Harvey, and G. Hobart, Further observations on the potential rhythms of the cerebral cortex during sleep, Science 82, 198 (1935).
  6. S. Brodt, M. Inostroza, N. Niethard, and J. Born, Sleep—A brain-state serving systems memory consolidation, Neuron 111, 1050 (2023).
  7. J. G. Klinzing, N. Niethard, and J. Born, Mechanisms of systems memory consolidation during sleep, Nat. Neurosci. 22, 1598 (2019).
  8. B. Rasch and J. Born, About sleep's role in memory, Physiol. Rev. 93, 681 (2013).
  9. M. Massimini, R. Huber, F. Ferrarelli, S. Hill, and G. Tononi, The sleep slow oscillation as a traveling wave, J. Neurosci. 24, 6862 (2004).
  10. E. S. L. Faber and P. Sah, Calcium-activated potassium channels: Multiple contributions to neuronal function, Neuroscientist 9, 181 (2003).
  11. G. E. Ha and E. Cheong, Spike frequency adaptation in neurons of the central nervous system, Exp. Neurobiol. 26, 179 (2017).
  12. D. A. McCormick, Neurotransmitter actions in the thalamus and cerebral cortex and their role in neuromodulation of thalamocortical activity, Prog. Neurobiol. 39, 337 (1992).
  13. B. E. Jones, Arousal systems, Front. Biosci. 8, s438 (2003).
  14. T.-A. E. Nghiem, N. Tort-Colet, T. Górski, U. Ferrari, S. Moghimyfiroozabad, J. S. Goldman, B. Teleńczuk, C. Capone, T. Bal, M. di Volo, and A. Destexhe, Cholinergic switch between two types of slow waves in cerebral cortex, Cereb. Cortex 30, 3451 (2020).
  15. D. Jercog, A. Roxin, P. Barthó, A. Luczak, A. Compte, and J. de la Rocha, Up-down cortical dynamics reflect state transitions in a bistable network, eLife 6, e22425 (2017).
  16. C. L. Combe and S. Gasparini, Ih from synapses to networks: HCN channel functions and modulation in neurons, Prog. Biophys. Mol. Biol. 166, 119 (2021).
  17. Y. Shu, A. Hasenstaub, and D. A. McCormick, The h-current controls cortical recurrent network activity through modulation of dendrosomatic communication, bioRxiv (2023).
  18. A. Destexhe and A. Babloyantz, A model of the inward current Ih and its possible role in thalamocortical oscillations, NeuroReport 4, 223 (1993).
  19. A. Destexhe, D. A. McCormick, and T. J. Sejnowski, A model for 8–10 Hz spindling in interconnected thalamic relay and reticularis neurons, Biophys. J. 65, 2473 (1993).
  20. S. Hill and G. Tononi, Modeling sleep and wakefulness in the thalamocortical system, J. Neurophysiol. 93, 1671 (2005).
  21. S. Y. Abbas, S. W. Ying, and P. A. Goldstein, Compartmental distribution of hyperpolarization-activated cyclic-nucleotide-gated channel 2 and hyperpolarization-activated cyclic-nucleotide-gated channel 4 in thalamic reticular and thalamocortical relay neurons, Neuroscience 141, 1811 (2006).
  22. D. Mehrotra, D. Levenstein, A. J. Duszkiewicz, S. S. Carrasco, S. A. Booker, A. Kwiatkowska, and A. Peyrache, Hyperpolarization-activated currents drive neuronal activation sequences in sleep, Curr. Biol. 34, 3043 (2024).
  23. L. Dalla Porta, A. Barbero-Castillo, J. Sanchez-Sanchez, N. Cancino, and M. V. Sanchez-Vives, H-current modulation of cortical up and down states, J. Physiol. 603, 2409 (2025).
  24. P. C. Bressloff, Spatiotemporal dynamics of continuum neural fields, J. Phys. A: Math. Theor. 45, 033001 (2012).
  25. B. Ermentrout, The analysis of synaptically generated traveling waves, J. Comput. Neurosci. 5, 191 (1998).
  26. G. Ermentrout and D. Terman, Mathematical Foundations of Neuroscience, Interdisciplinary Applied Mathematics (Springer, New York, 2010).
  27. S. Budzinskiy, A. Beuter, and V. Volpert, Nonlinear analysis of periodic waves in a neural field model, Chaos 30, 083144 (2020).
  28. R. Erazo-Toscano and R. Osan, Synaptic propagation in neuronal networks with finite-support space-dependent coupling, Phys. Rev. E 107, 034403 (2023).
  29. O. E. Omel'chenko and C. R. Laing, Activity patterns in ring networks of quadratic integrate-and-fire neurons with synaptic and gap junction coupling, Phys. Rev. E 110, 034411 (2024).
  30. S. Coombes, Waves, bumps, and patterns in neural field theories, Biol. Cybern. 93, 91 (2005).
  31. M. R. Qubbaj and V. K. Jirsa, Neural field dynamics under variation of local and global connectivity and finite transmission speed, Physica D 238, 2331 (2009).
  32. J. D. Harris and B. Ermentrout, Traveling waves in a spatially-distributed Wilson–Cowan model of cortex: From fronts to pulses, Physica D 369, 30 (2018).
  33. J. Wyller, P. Blomquist, and G. T. Einevoll, Turing instability and pattern formation in a two-population neuronal network model, Physica D 225, 75 (2007).
  34. H. R. Wilson and J. D. Cowan, Excitatory and inhibitory interactions in localized populations of model neurons, Biophys. J. 12, 1 (1972).
  35. C. Dimulescu, R. Strömsdörfer, A. Flöel, and K. Obermayer, On the robustness of the emergent spatiotemporal dynamics in biophysically realistic and phenomenological whole-brain models at multiple network resolutions, Front. Network Physiol. 5, 1589566 (2025).
  36. D. Levenstein, G. Buzsáki, and J. Rinzel, NREM sleep in the rodent neocortex and hippocampus reflects excitable dynamics, Nat. Commun. 10, 2478 (2019).
  37. M. Torao-Angosto, A. Manasanch, M. Mattia, and M. V. Sanchez-Vives, Up and down states during slow oscillations in slow-wave sleep and different levels of anesthesia, Front. Syst. Neurosci. 15, 609645 (2021).
  38. P. C. Bressloff, Metastable states and quasicycles in a stochastic Wilson-Cowan model of neuronal population dynamics, Phys. Rev. E 82, 051903 (2010).
  39. T. Trabocchi, R. Burioni, L. de Arcangelis, and D. Fanelli, Generalized Wilson-Cowan model with short term synaptic plasticity, Chaos, Solitons Fract. 208, 118286 (2026).
  40. R. Marino, L. Buffoni, L. Chicchi, F. D. Patti, D. Febbe, L. Giambagli, and D. Fanelli, Learning in Wilson-Cowan model for metapopulation, Neural Comput. 37, 701 (2025).
  41. H. R. Wilson and J. D. Cowan, A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue, Kybernetik 13, 55 (1973).
  42. D. J. Pinto and G. B. Ermentrout, Spatially structured activity in synaptically coupled neuronal networks: I. Traveling fronts and pulses, SIAM J. Appl. Math. 62, 206 (2001).
  43. S. Amari, Dynamics of pattern formation in lateral-inhibition type neural fields, Biol. Cybern. 27, 77 (1977).
  44. S. E. Folias and P. C. Bressloff, Breathing pulses in an excitatory neural network, SIAM J. Appl. Dyn. Syst. 3, 378 (2004).
  45. Z. P. Kilpatrick and P. C. Bressloff, Effects of synaptic depression and adaptation on spatiotemporal dynamics of an excitatory neuronal network, Physica D 239, 547 (2010).
  46. R. Curtu and B. Ermentrout, Pattern formation in a network of excitatory and inhibitory cells with adaptation, SIAM J. Appl. Dyn. Syst. 3, 191 (2004).
  47. M. Augustin, J. Ladenbauer, and K. Obermayer, How adaptation shapes spike rate oscillations in recurrent neuronal networks, Front. Comput. Neurosci. 7, 76 (2013).
  48. C. Cakan and K. Obermayer, Biophysically grounded mean-field models of neural populations under electrical stimulation, PLoS Comput. Biol. 16, e1007822 (2020).
  49. C. Cakan, C. Dimulescu, L. Khakimova, D. Obst, A. Flöel, and K. Obermayer, Spatiotemporal patterns of adaptation-induced slow oscillations in a whole-brain model of slow-wave sleep, Front. Comput. Neurosci. 15, 800101 (2022).
  50. J. Ladenbauer, M. Augustin, and K. Obermayer, How adaptation currents change threshold, gain, and variability of neuronal spiking, J. Neurophysiol. 111, 939 (2014).
  51. M. Mattia and M. V. Sanchez-Vives, Exploring the spectrum of dynamical regimes and timescales in spontaneous cortical activity, Cognit. Neurodynam. 6, 239 (2012).
  52. F. C. Roth and H. Hu, An axon-specific expression of HCN channels catalyzes fast action potential signaling in GABAergic interneurons, Nat. Commun. 11, 2248 (2020).
  53. HCN channels form the biophysical substrate of h-current-based adaptation.
  54. S. Rich, T. A. Valiante, and J. Lefebvre, H- and m-channel overexpression promotes seizure-like events by impairing the ability of inhibitory neurons to process correlated inputs, PLoS Comput. Biol. 21, e1013199 (2025).
  55. M. D'Andola, B. Rebollo, A. G. Casali, J. F. Weinert, A. Pigorini, R. Villa, M. Massimini, and M. V. Sanchez-Vives, Bistability, causality, and complexity in cortical networks: An in vitro perturbational study, Cereb. Cortex 28, 2233 (2018).
  56. M. Dasilva, A. Camassa, A. Navarro-Guzman, A. Pazienti, L. Perez-Mendez, G. Zamora-López, M. Mattia, and M. V. Sanchez-Vives, Modulation of cortical slow oscillations and complexity across anesthesia levels, NeuroImage 224, 117415 (2021).
  57. M. V. Sanchez-Vives and D. A. McCormick, Cellular and network mechanisms of rhythmic recurrent activity in neocortex, Nat. Neurosci. 3, 1027 (2000).
  58. Z. P. Kilpatrick, Wilson-Cowan model, in Encyclopedia of Computational Neuroscience, edited by D. Jaeger and R. Jung (Springer, New York, NY, 2013), pp. 1–5.
  59. L. Papadopoulos, C. W. Lynn, D. Battaglia, and D. S. Bassett, Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state, PLoS Comput. Biol. 16, e1008144 (2020).
  60. T. Friedlander and N. Brenner, Adaptive response by state-dependent inactivation, Proc. Natl. Acad. Sci. USA 106, 22558 (2009).
  61. I. Timofeev, M. Bazhenov, T. Sejnowski, and M. Steriade, Cortical hyperpolarization-activated depolarizing current takes part in the generation of focal paroxysmal activities, Proc. Natl. Acad. Sci. USA 99, 9533 (2002).
  62. H.-Y. Chu and X. Zhen, Hyperpolarization-activated, cyclic nucleotide-gated (HCN) channels in the regulation of midbrain dopamine systems, Acta Pharmacol. Sin. 31, 1036 (2010).
  63. C. Ganguly, S. S. Bezugam, E. Abs, M. Payvand, S. Dey, and M. Suri, Spike frequency adaptation: Bridging neural models and neuromorphic applications, Commun. Eng. 3, 22 (2024).
  64. D. A. McCormick and H. C. Pape, Properties of a hyperpolarization-activated cation current and its role in rhythmic oscillation in thalamic relay neurones, J. Physiol. 431, 291 (1990).
  65. Reference [33], however, assumes that the fixed point values ũe and ũi are equal.
  66. Reference  [32] investigates the stability of homogeneous equilibria for the two-population model (i.e., b=0).
  67. Á. Byrne, D. Avitabile, and S. Coombes, Next-generation neural field model: The evolution of synchrony within patterns and waves, Phys. Rev. E 99, 012313 (2019).
  68. H. G. E. Meijer and S. Coombes, Travelling waves in a neural field model with refractoriness, J. Math. Biol. 68, 1249 (2014).
  69. States marked in yellow in the regions of Turing-unstable up- and down-states in bi are shown in Figs. 5 and 6 may also support regular spatiotemporal patterns if another initialization would have been chosen.
  70. L. M. Giocomo and M. E. Hasselmo, Time constants of h current in layer II stellate cells differ along the dorsal to ventral axis of medial entorhinal cortex, J. Neurosci. 28, 9414 (2008).
  71. A. Bhattacharjee and L. K. Kaczmarek, For K+ channels, Na+ is the new Ca2+, Trends Neurosci. 28, 422 (2005).
  72. D. A. Brown and P. R. Adams, Muscarinic suppression of a novel voltage-sensitive K+ current in a vertebrate neurone, Nature (London) 283, 673 (1980).
  73. P. C. Schwindt, W. J. Spain, and W. E. Crill, Long-lasting reduction of excitability by a sodium-dependent potassium current in cat neocortical neurons, J. Neurophysiol. 61, 233 (1989).
  74. D. A. Brown and W. H. Griffith, Calcium-activated outward current in voltage-clamped hippocampal neurones of the guinea-pig, J. Physiol. 337, 287 (1983).
  75. C. M. Upchurch, C. L. Combe, C. J. Knowlton, V. G. Rousseau, S. Gasparini, and C. C. Canavier, Long-term inactivation of sodium channels as a mechanism of adaptation in CA1 pyramidal neurons, J. Neurosci. 42, 3768 (2022).
  76. C.-H. Lee and R. MacKinnon, Structures of the human HCN1 hyperpolarization-activated channel, Cell 168, 111 (2017).
  77. A. Mironenko, U. Zachariae, B. L. de Groot, and W. Kopec, The persistent question of potassium channel permeation mechanisms, J. Mol. Biol. 433, 167002 (2021).
  78. H.-C. Pape, Queer current and pacemaker: The hyperpolarization-activated cation current in neurons, Annu. Rev. Physiol. 58, 299 (1996).
  79. M. Biel, C. Wahl-Schott, S. Michalakis, and X. Zong, Hyperpolarization-activated cation channels: From genes to function, Physiol. Rev. 89, 847 (2009).
  80. R. C. Budzinski, T. T. Nguyen, G. B. Benigno, J. Đoàn, J. Mináč, T. J. Sejnowski, and L. E. Muller, Analytical prediction of specific spatiotemporal patterns in nonlinear oscillator networks with distance-dependent time delays, Phys. Rev. Res. 5, 013159 (2023).
  81. J. A. Roberts, L. L. Gollo, R. G. Abeysuriya, G. Roberts, P. B. Mitchell, M. W. Woolrich, and M. Breakspear, Metastable brain waves, Nat. Commun. 10, 1056 (2019).
  82. M. Martin and M. G. Pedersen, Modelling and analysis of cAMP-induced mixed-mode oscillations in cortical neurons: Critical roles of HCN and M-type potassium channels, PLoS Comput. Biol. 20, e1011559 (2024).
  83. https://github.com/ronja-roevardotter/equivalent-adaptation-wilson-cowan-field.git.
  84. R. Strömsdörfer, Framework for reproducibility, version 1.1, Zenodo (2026), https://zenodo.org/records/19455050.
  85. D. Rubino, K. A. Robbins, and N. G. Hatsopoulos, Propagating waves mediate information transfer in the motor cortex, Nat. Neurosci. 9, 1549 (2006).

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