[en] Phase sensitivity analysis is a powerful method for studying (asymptotically periodic) bursting neuron models. One popular way of capturing phase sensitivity is through the computation of isochrons---subsets of the state space that each converge to the same trajectory on the limit cycle. However, the computation of isochrons is notoriously difficult, especially for bursting neuron models. In [W. E. Sherwood and J. Guckenheimer, SIAM J. Appl. Dyn. Syst., 9 (2010), pp. 659--703], the phase sensitivity of the bursting Hindmarsh--Rose model is studied through the use of singular perturbation theory: cross sections of the isochrons of the full system are approximated by those of fast subsystems. In this paper, we complement the previous study, providing a detailed phase sensitivity analysis of the full (three-dimensional) system, including computations of the full (two-dimensional) isochrons. To our knowledge, this is the first such computation for a bursting neuron model. This was made possible thanks to the numerical method recently proposed in [A. Mauroy and I. Mezić, Chaos, 22 (2012), 033112]---relying on the spectral properties of the so-called Koopman operator---which is complemented with the use of adaptive quadtree and octree grids. The main result of the paper is to highlight the existence of a region of high phase sensitivity called the almost phaseless set and to completely characterize its geometry. In particular, our study reveals the existence of a subset of the almost phaseless set that is not predicted by singular perturbation theory (i.e., by the isochrons of fast subsystems). We also discuss how the almost phaseless set is related to empirically observed phenomena such as addition/deletion of spikes and to extrema of the phase response of the system. Finally, through the same numerical method, we show that an elliptic bursting model is characterized by a very high phase sensitivity and other remarkable properties.
Disciplines :
Mathematics
Author, co-author :
Mauroy, Alexandre ; University of California Santa Barbara > Department of Mechanical Engineering
Rhoads, Blane; University of California Santa Barbara > Department of Mechanical Engineering
Moehlis, Jeff; University of California Santa Barbara > Department of Mechanical Engineering
Mezic, Igor; University of California Santa Barbara > Department of Mechanical Engineering
Language :
English
Title :
Global Isochrons and Phase Sensitivity of Bursting Neurons
R. Bertram, M. J. Butte, T. Kiemel, and A. Sherman, Topological and phenomenological classification of bursting oscillations, Bull. Math. Biol., 57 (1995), pp. 413-439.
A. Borisyuk and J. Rinzel, Understanding neuronal dynamics by geometrical dissection of minimal models, in Methods and Models in Neurophysics, Proceedings of Les Houches Summer School, Elsevier, New York, 2005, pp. 19-72.
E. Brown, J. Moehlis, and P. Holmes, On the phase reduction and response dynamics of neural oscillator populations, Neural Comput., 16 (2004), pp. 673-715.
M. Budisíc, R. Mohr, and I. Mezíc, Applied Koopmanism, Chaos, 22 (2012), 047510.
R. J. Butera, J. Rinzel, and J. C. Smith, Models of respiratory rhythm generation in the pre-Botzinger complex. I. Bursting pacemaker neurons, J. Neurophysiol., 82 (1999), pp. 382-397.
C. C. Canavier, J. W. Clark, and J. H. Byrne, Routes to chaos in a model of a bursting neuron, Biophys. J., 57 (1990), pp. 1245-1251.
O. Castej́on, A. Guillamon, and G. Huguet, Phase-amplitude response functions for transient-state stimuli, J. Math. Neurosci., 3 (2013), pp. 1-26.
T. R. Chay and J. Keizer, Minimal model for membrane oscillations in the pancreatic beta-cell, Biophys. J., 42 (1983), pp. 181-189.
D. C. Cooper, The significance of action potential bursting in the brain reward circuit, Neurochem. Internat., 41 (2002), pp. 333-340.
S. S. Demir, R. J. Butera, A. A. DeFranceschi, J. W. Clark, and J. H. Byrne, Phase sensitivity and entrainment in a modeled bursting neuron, Biophys. J., 72 (1997), pp. 579-594.
G. B. Ermentrout and N. Kopell, Oscillator death in systems of coupled neural oscillators, SIAMJ. Appl. Math., 50 (1990), pp. 125-146.
G. B. Ermentrout and D. H. Terman, Mathematical Foundations of Neuroscience, Springer, New York, 2010.
N. Fenichel, Persistence and smoothness of invariant manifolds for flows, Indiana Univ. Math. J., 21 (1971), pp. 193-226.
N. Fenichel, Asymptotic stability with rate conditions, Indiana Univ. Math. J., 23 (1973), pp. 1109-1137.
A. Franci, G. Drion, and R. Sepulchre, Modeling the modulation of neuronal bursting: A singularity theory approach, SIAM Appl. Dyn. Syst., to appear; http://arXiv.org/abs/1305.7364, 2013.
M. Golubitsky, K. Josic, and T. J. Kaper, An unfolding theory approach to bursting in fast-slow systems, in Global Analysis of Dynamical Systems, Institute of Physics Publishing, London, 2001, pp. 277-308.
W. Govaerts and B. Sautois, Computation of the phase response curve: A direct numerical approach, Neural Comput., 18 (2006), pp. 817-847.
J. Guckenheimer, Isochrons and phaseless sets, J. Math. Biol., 1 (1975), pp. 259-273.
J. Guckenheimer and C. Kuehn, Computing slow manifolds of saddle type, SIAM J. Appl. Dyn. Syst., 8 (2009), pp. 854-879.
A. Guillamon and G. Huguet, A computational and geometric approach to phase resetting curves and surfaces, SIAM J. Appl. Dyn. Syst., 8 (2009), pp. 1005-1042.
C. Hammond, H. Bergman, and P. Brown, Pathological synchronization in Parkinson's disease: Networks, models and treatments, Trends Neurosci., 30 (2007), pp. 357-364.
J. L. Hindmarsh and R. M. Rose, A model of neuronal bursting using three coupled first order differential equations, Proc. Roy. Soc. London Ser. B Biol. Sci., 221 (1984), pp. 87-102.
P. Hitczenko and G. S. Medvedev, Bursting oscillations induced by small noise, SIAM J. Appl. Math., 69 (2009), pp. 1359-1392.
G. Huguet and R. de la Llave, Computation of limit cycles and their isochrons: Fast algorithms and their convergence, SIAM J. Appl. Dyn. Syst., 12 (2013), pp. 1763-1802.
E. M. Izhikevich, Neural excitability, spiking and bursting, Internat. J. Bifur. Chaos Appl. Sci. Engrg., 10 (2000), pp. 1171-1266.
E. M. Izhikevich, Synchronization of elliptic bursters, SIAM Rev., 43 (2001), pp. 315-344.
E. M. Izhikevich, Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting, MIT Press, Cambridge, MA, 2007.
J. Keener and J. Sneyd, Mathematical Physiology, Springer-Verlag, New York, 1998.
Y. Kuramoto, Chemical Oscillations, Waves, and Turbulence, Springer-Verlag, New York, Berlin, 1984.
I. G. Malkin, The Methods of Lyapunov and Poincaŕe in the Theory of Nonlinear Oscillations, Gostekhizdat, Moscow, Leningrad, 1949.
A. Mauroy and I. Mezíc, On the use of Fourier averages to compute the global isochrons of (quasi)periodic dynamics, Chaos, 22 (2012), 033112.
I. Mezíc and A. Banaszuk, Comparison of systems with complex behavior, Phys. D, 197 (2004), pp. 101-133.
V. Novicenko and K. Pyragas, Computation of phase response curves via a direct method adapted to infinitesimal perturbations, Nonlinear Dynamics, 67 (2012), pp. 517-526.
H. M. Osinga and J. Moehlis, Continuation-based computation of global isochrons, SIAM J. Appl. Dyn. Syst., 9 (2010), pp. 1201-1228.
H. M. Osinga, A. Sherman, and K. Tsaneva-Atanasova, Cross-currents between biology and mathematics: The codimension of pseudo-plateau bursting, Discrete Contin. Dyn. Systems, 32 (2012), pp. 2853-2877.
J. Rinzel, A formal classification of bursting mechanisms in excitable systems, in Proceedings of the International Congress of Mathematicians, Springer, New York, 1986, vol. 1, pp. 1578-1593.
J. Rinzel and G. B. Ermentrout, Analysis of neural excitability, in Methods of Neuronal Modeling, C. Koch and I. Segev, eds., MIT Press, Cambridge, MA, 1998, pp. 251-291.
J. Rinzel and Y. S. Lee, Dissection of a model for neuronal parabolic bursting, J. Math. Biol., 25 (1987), pp. 653-675.
J. E. Rubin, C. C. McIntyre, R. S. Turner, and T. Wichmann, Basal ganglia activity patterns in Parkinsonism and computational modeling of their downstream effects, European J. Neurosci., 36 (2012), pp. 2213-2228.
P. Sacre and R. Sepulchre, Sensitivity analysis of oscillator models in the space of phase response curves: Oscillators as open systems, IEEE Control Systems Mag., 2014, to appear; http://arxiv.org/abs/1206.4144.
W. E. Sherwood and J. Guckenheimer, Dissecting the phase response of a model bursting neuron, SIAM J. Appl. Dyn. Syst., 9 (2010), pp. 659-703.
A. Shilnikov and M. Kolomiets, Methods of the qualitative theory for the Hindmarsh-Rose model: A case study. A tutorial, Internat. J. Bifur. Chaos Appl. Sci. Engrg., 18 (2008), pp. 2141-2168.
J. Su, J. Rubin, and D. Terman, Effects of noise on elliptic bursters, Nonlinearity, 17 (2004), pp. 133-157.
T. T́el and M. Gruiz, Chaotic Dynamics: An Introduction Based on Classical Mechanics, Cambridge University Press, Cambridge, UK, 2006.
K. C. A. Wedgwood, K. K. Lin, R. Thul, and S. Coombes, Phase-amplitude descriptions of neural oscillator models, J. Math. Neurosci., 3 (2013), pp. 1-22.
A. Winfree, The Geometry of Biological Time, 2nd ed., Springer-Verlag, New York, Berlin, 2001.
A. T. Winfree, Patterns of phase compromise in biological cycles, J. Math. Biol., 1 (1974), pp. 73-95.