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Chaotic Dynamics in Neural Systems

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Encyclopedia of Complexity and Systems Science

Introduction

Several basic mechanisms of chaotic dynamics in phenomenological and biologically plausible models of individual neurons are discussed. We show that chaos occurs at the transition boundaries between generic activity types in neurons such as tonic spiking, bursting, and quiescence, where the system can also become bi-stable. The bifurcations underlying these transitions give rise to period-doubling cascades, various homoclinic and saddle phenomena, torus breakdown, and chaotic mixed-mode oscillations in such neuronal systems.

Neurons exhibit various activity regimes and state transitions that reflect their intrinsic ionic channel behaviors and modulatory states. The fundamental types of neuronal activity can be broadly defined as quiescence, subthreshold, and tonic spiking oscillations, as well as bursting composed of alternating periods of spiking activity and quiescence. A single neuron can endogenously demonstrate various bursting patterns, varying in response to the...

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Bibliography

  • Abraham RH (1985) Chaostrophes, intermittency, and noise, in Chaos, fractals, and dynamics, Conf. Univ. Guelph/Can. 1981 and 1983. Lect Notes Pure Appl Math 98:3–22

    Google Scholar 

  • Alacam D, Shilnikov A (2015) Making a swim central pattern generator out of latent parabolic bursters. J Bifurcat Chaos 25(7):1540003

    Article  MathSciNet  MATH  Google Scholar 

  • Andronov AA, Vitt AA, Khaikin SE (1966) Theory of oscillations. International series of monographs in physics. Pergamon Press, Oxford

    MATH  Google Scholar 

  • Arnold VI, Afrajmovich VS, Ilyashenko YS, Shil’nikov LP (1994) Bifurcation theory, dynamical systems v. encyclopedia of mathematical sciences. Springer. https://www.springer.com/gp/book/9783540181736

  • Barrio R, Shilnikov A (2011) Parameter-sweeping techniques for temporal dynamics of neuronal systems: case study of hindmarsh-rose model. J Math Neurosci 1(1):6

    Article  MathSciNet  MATH  Google Scholar 

  • Barrio R, Angeles Martínez M, Serrano S, Shilnikov A (2014) Macro-and micro-chaotic structures in the Hindmarsh-Rose model of bursting neurons. J Chaos 24(2):023128

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Baxter DA, Lechner HA, Canavier CC, Butera RJ, Franceschi AA, Clark JW, Byrne JH (1999) Coexisting stable oscillatory states in single cell and multicellular neuronal oscillators. In: Levine DS, Brown VR, Shirey VT (eds) Oscillations in neural systems. Lawrence Erlbaum Associates, Mahwah, pp 51–78

    Google Scholar 

  • Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (2000) Spiking-bursting activity in the thalamic reticular nucleus initiates sequences of spindle oscillations in thalamic networks. J Neurophysiol 84:1076–1087

    Article  Google Scholar 

  • Beim Graben P, Hutt A (2013) Detecting metastable states of dynamical systems by recurrence-based symbolic dynamics. Phys Rev Lett 110:154101

    Article  ADS  Google Scholar 

  • Beim Graben P, Hutt A (2015) Detecting event-related recurrences by symbolic analysis: applications to human language processing. Philos Trans Royal Soc A 373:20140089

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Beim Graben P, Sellers KK, FrÃűhlich F, Hutt A (2016) Optimal estimation of recurrence structures from time series. Europhys Lett 114(3):38003

    Article  ADS  Google Scholar 

  • Belykh I, Shilnikov A (2008) When weak inhibition synchronizes strongly desynchronizing networks of bursting neurons. Phys Rev Lett 101(7):078102

    Article  ADS  Google Scholar 

  • Belykh VN, Belykh IV, Colding-Joregensen M, Mosekilde E (2000) Homoclinic bifurcations leading to bursting oscillations in cell models. Eur Phys J 3:205

    Google Scholar 

  • Bem T, Rinzel J (2004) Short duty cycle distabilizes a half-center oscillator, by gap junctions can restabilize the anti-phase pattern. J Neurophysiol 91:693–703

    Article  Google Scholar 

  • Bertram R (1993) A computational study of the effects of serotonin on a molluscan Burster neuron. Biol Cybern 69:257–267

    Article  MATH  Google Scholar 

  • Bertram R, Butte MJ, Kiemel T, Sherman A (1995) Topological and phenomenological classication of bursting oscillations. Bull Math Biol 57(3):413–439

    Article  MATH  Google Scholar 

  • Best J, Borisyuk A, Rubin J, Terman D, Wechselberger M (2005) The dynamic range of bursting in a model respiratory pacemaker network. SIAM J Appl Dyn Syst 4(4):1107–1139

    Article  MathSciNet  MATH  Google Scholar 

  • Briggman KL, Kristan WB (2008) Multifunctional pattern-generating circuits. Annu Rev Neurosci 31:271–294

    Article  Google Scholar 

  • Briggman KL, Kristan WB Jr (2006) Imaging dedicated and multifunctional neural circuits generating distinct behaviors. J Neurosci 26(42):10925–10923

    Article  Google Scholar 

  • Butera R (1998) Multirhythmic bursting. J Chaos 8:274–282

    Article  ADS  MATH  Google Scholar 

  • Canavier CC, Baxter DA, Clark L, Byrne J (1993) Nonlinear dynamics in a model neuron provide a novel mechanism for transient synaptic inputs to produce long-term alterations of postsynaptic activity. J Neurophysiol 69:2252

    Article  Google Scholar 

  • Canavier CC, Baxter DA, Clark JW, Byrne JH (1999) Control of multistability in ring circuits of oscillators. Biol Cybern 80:87–102

    Article  MATH  Google Scholar 

  • Catacuzzeno L, Fioretti B, Franciolini F (2003) Voltage-gated outward K-currents in frog saccular hair cells. J Neurophysiol 90(6):3688–3701

    Article  Google Scholar 

  • Catacuzzeno L, Fioretti B, Perin P, Franciolini F (2004) Spontaneous low-frequency voltage oscillations in frog saccular hair cells. J Physiol 561:685–701

    Article  Google Scholar 

  • Channell P, Cymbalyuk G, Shilnikov AL (2007a) Origin of bursting through homoclinic spike adding in a neuron model. Phys Rev Lett 98:134101

    Article  ADS  Google Scholar 

  • Channell P, Cymbalyuk G, Shilnikov AL (2007b) Applications of the Poincare mapping technique to analysis of neuronal dynamics. Neurocomputing 70:10–12

    Article  Google Scholar 

  • Channell P, Fuwape I, Neiman AB, Shilnikov AL (2009) Variability of bursting patterns in a neuron model in the presence of noise. J Comp Neurosci 27(3):527

    Article  MathSciNet  Google Scholar 

  • Chay TR (1985) Chaos in a three-variable model of an excitable cell. Phys D 16(2):233–242

    Article  MATH  Google Scholar 

  • Cymbalyuk GS, Calabrese RL (2001) A model of slow plateau-like oscillations based upon the fast Na+ current in a window mode. Neurocomputing 38:159–166

    Article  Google Scholar 

  • Cymbalyuk G, Shilnikov AL (2005) Co-existent tonic spiking modes in a leech neuron model. J Comp Neurosci 18(3):255–263

    Article  Google Scholar 

  • Cymbalyuk GS, Nikolaev EV, Borisyuk RM (1994) In-phase and anti-phase self-oscillations in a model of two electrically coupled pacemakers. Biol Cybern 71:153160

    Article  MATH  Google Scholar 

  • Cymbalyuk GS, Gaudry Q, Masino MA, Calabrese RL (2002) Bursting in leech heart interneurons: cell autonomous and network based mechanisms. J Neurosci 22:10580–10592

    Article  Google Scholar 

  • Deng B (1999) Glucose-induced period-doubling cascade in the electrical activity of pancreatic β-cells. Math Biol 38(1):28

    Article  MathSciNet  Google Scholar 

  • Deng B, Hines G (2002) Food chain chaos due to Shilnikov’s orbit. J Chaos 12(3):533–538

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • DeShazer DJ, Garcia-Ojalv J, Roy R (2003) Bursting dynamics of a fiber laser with an injected signal. Phys Rev E 67(3):036602

    Article  ADS  Google Scholar 

  • Devaney R (1992) A first course in chaotic dynamical systems. Westview Press. https://www.taylorfrancis.com/books/9780429503481

  • Doiron B, Laing C, Longtin A (2002) Ghostbursting: a novel neuronal burst mechanism. J Comp Neurosci 12:5

    Article  Google Scholar 

  • Elson RC, Selverston AI, Abarbanel HDI, Rabinovich MI (2002) Dynamic control of irregular bursting in an identified neuron of an oscillatory circuit. J Neurophysiol 88:1166

    Article  Google Scholar 

  • Ermentrout B (1993) Type I membranes, phase resetting curves, and synchrony, neural computation 8, 979–1001, 1996. Phys D 62(1–4):338–346

    MathSciNet  Google Scholar 

  • Fan YS, Holden AV (1995) Bifurcations bursting, chaos and crises in the Rose-Hindmarsh model for neuronal activity. Chaos Solitons Fractals 3:439–449

    Article  ADS  MATH  Google Scholar 

  • Fenichel N (1979) Geometric singular perturbation theory for ordinary differential equations. J Diff Eqns 31:53–98

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Feudel U, Neiman A, Pei X, Wojtenek W, Braun H, Huber M, Moss F (2000) Homoclinic bifurcation in a Hodgkin-Huxley model of thermally sensitive neurons. J Chaos 10(1):231–239

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Frohlich F, Bazhenov M (2006) Coexistence of tonic firing and bursting in cortical neurons. Phys Rev E 74(3):031922–031929

    Article  ADS  Google Scholar 

  • ftp://ftp.cwi.nl/pub/CONTENT

  • Gavrilov NK, Shilnikov LP (1972) On three-dimensional dynamical systems close to systems with a structurally unstable homoclinic curve. I Math USSR-Sb 17(4):467–484

    Article  Google Scholar 

  • Gavrilov N, Shilnikov A (2000) Example of a blue sky catastrophe, in Methods of qualitative theory of differential equations and related topics. Dedicated to the memory of E.A. Leontovich-Andronova. Am Math Soc Trans II Ser 200:99–105

    MATH  Google Scholar 

  • Getting PA (1989) Emerging principles governing the operation of neural networks. Annu Rev Neurosci 12:185–204

    Article  Google Scholar 

  • Glass L (2005) Multistable spatiotemporal patterns of cardiac activity. Proc Natl Acad Sci U S A 102:10409

    Article  ADS  Google Scholar 

  • Glendenning P, Hall T (1996) Zeros of the kneading invariant and topological entropy for Lorenz maps. Nonlinearity 9:999–1014

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Golomb D, Rinzel J (1993) Clustering in globally coupled inhibitory neurons. Phys Rev E 48:4810

    Article  ADS  MATH  Google Scholar 

  • Griffiths RE, Pernarowski MC (1917–1948) Return map characterizations for a model of bursting with two slow variables. SIAM J Appl Math 66(6):2006

    MathSciNet  Google Scholar 

  • Guckenheimer J (1996) Towards a global theory of singularly perturbed systems. Progr Nonlinear Diff Eqns Appl 19:214–225

    MathSciNet  MATH  Google Scholar 

  • Hill A, Lu J, Masino M, Olsen O, Calabrese RL (2001) A model of a segmental oscillator in the leech heartbeat neuronal network. J Comput Neurosci 10:281–302

    Article  Google Scholar 

  • Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117(4):500–544

    Article  Google Scholar 

  • Holden AV, Fan YS (1992) From simple to simple bursting oscillatory behavior via intermittent chaos in the Rose-Hindmarsh model for neuronal activity. Chaos Solutions Fractals 2(3):221–0236

    Article  ADS  MATH  Google Scholar 

  • Hounsgaard J, Kiehn O (1989) Serotonin-induced bistability of turtle motoneurones caused by a nifedipine-sensitive calcium plateau potential. J Physiol 414:265

    Article  Google Scholar 

  • Hudspeth A, Lewis R (1988) Kinetic analysis of voltage- and ion-dependent conductances in saccular hair cells of the bull-frog, Rana catesbeiana. J Physiol 400:237–274

    Article  Google Scholar 

  • Hutt A, Beim Graben P (2017) Sequences by metastable attractors: interweaving dynamical systems and experimental data. Front Appl Math Stat 3:11

    Article  Google Scholar 

  • Izhikevich EM (2000) Neural excitability, spiking and bursting. Int J Bifurc Chaos 10(6):1171–1266

    Article  MathSciNet  MATH  Google Scholar 

  • Izhikevich EM (2007) Dynamical systems in neuroscience. The geometry of excitability and bursting. MIT Press, Cambridge

    Google Scholar 

  • Jalil S, Allen D, Youker J, Shilnikov A (2013) Toward robust phase-locking in melibe swim central pattern generator models. J Chaos 23(4):046105

    Article  ADS  MathSciNet  Google Scholar 

  • Jones CKRT, Kopell N (1994) Tracking invariant manifolds with differential forms in singularly perturbed systems. J Diff Eqns 108:64–88

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Ju H, Neiman A, Shilnikov A (2018) Bottom-up approach to torus bifurcation in neuron models. J Chaos 28:106317

    Article  ADS  MathSciNet  Google Scholar 

  • Katz PS (2008) Tritonia. Scholarpedia 2(6):3504

    Article  ADS  Google Scholar 

  • Kopell N (1988) Toward a theory of modeling central pattern generators. In: Cohen AH, Rossignol S, Grillner S (eds) Neural control of rhythmic movements in vertebrates. Wiley, New York, p 23

    Google Scholar 

  • Kopell N, Ermentrout GB (2002) Mechanisms of phase-locking and frequency control. In: Fiedler B (ed) Handbook of dynamical systems, vol 2. Elsevier, Amsterdam, pp 3–54

    MATH  Google Scholar 

  • Kopell N, Ermentrout GB (2004) Chemical and electrical synapses perform complementary roles in the synchronization of interneuronal networks. Proc Natl Acad Sci U S A 101:15482

    Article  ADS  Google Scholar 

  • Kramer MA, Traub RD, Kopell NJ (2008) New dynamics in cerebellar Purkinje cells: torus canards. Phys Rev Lett 101(6):068103

    Article  ADS  Google Scholar 

  • Kristan WB, Katz P (2006) Form and function in systems neuroscience. Curr Biol 16:R828–R831

    Article  Google Scholar 

  • Kristan WB, Calabrese RL, Friesen WO (2005) Neuronal control of leech behavior. Prog Neurobiol 76(5):279–327

    Article  Google Scholar 

  • Kuznetsov YA (1998) Elements of applied bifurcation theory. Applied mathematical sciences, vol 112, 2nd edn. New York, Springer

    MATH  Google Scholar 

  • Laing CR, Doiron B, Longtin A, Noonan L, Turner RW, Maler L (2003) Type I burst excitability. J Comput Neurosci 14:329

    Article  Google Scholar 

  • Lechner H, Baxter D, Clark C, Byrne J (1996) Bistability and its regulation by serotonin in the endogenously bursting neuron R15 in Aplysia. J Neurophysiol 75:957

    Article  Google Scholar 

  • Lukyanov V, Shilnikov L (1978) On some bifurcations of dynamical systems with homoclinic structures. Soviet Math Dokl 19(6):1314–1318

    Google Scholar 

  • Marder E, Calabrese RL (1996) Principles of rhythmic motor pattern generation. Physiol Rev 76:687–713

    Article  Google Scholar 

  • Marder E, Kopell N, Sigvardt K (1998) How compuation aids in understanding biological networks. In: Stein PSG, Selverston A, Grillner S (eds) Neurons, networks, and motor behavior. MIT Press, Cambridge, pp 139–150

    Google Scholar 

  • Medvedev GM (2005) Reduction of a model of an excitable cell to a one-dimensional map. Phys D 202(1–2):87–106

    MathSciNet  Google Scholar 

  • Milnor J, Thurston W (1988) On iterated maps of the interval. In: Dynamical systems, Lecture notes in mathematics, vol 1342. Springer, Berlin, p 465563

    Google Scholar 

  • Mira C (1987) Chaotic dynamics from the one-dimensional endomorphism to the two-dimensional diffeomorphism. World Scientific, Singapore

    Book  MATH  Google Scholar 

  • Mira C, Shilnikov AL (2005) Slow and fast dynamics generated by non-invertible plane maps. J Bifurc Chaos 15(11):3509–3534

    Article  MATH  Google Scholar 

  • Mischenko EF, Rozov NK (1980) Differential equations with small parameters and relaxation oscillations. Plenum Press, New York

    Book  Google Scholar 

  • Mischenko EF, Kolesov YS, Kolesov AY, Rozov NK (1994) Asymptotic methods in singularly perturbed systems. Monographs in contemporary mathematics. Consultants Bureau, New York

    Book  Google Scholar 

  • Neiman AB, Dierkes K, Lindner B, Han L, Shilnikov AL (2011) Spontaneous voltage oscillations and response dynamics of a Hodgkin-Huxley type model of sensory hair cells. J Math Neurosci 1(1):11

    Article  MathSciNet  MATH  Google Scholar 

  • Pontryagin LS, Rodygin LV (1960) Periodic solution of a system of ordinary differential equations with a small parameter in the terms containing derivatives. Sov Math Dokl 1:611–661

    MathSciNet  MATH  Google Scholar 

  • Pusuluri K, Shilnikov A (2018) Homoclinic chaos and its organization in a nonlinear optics model. Phys Rev E 98(4):040202

    Article  ADS  Google Scholar 

  • Pusuluri K, Shilnikov A (2019) Symbolic representation of neuronal dynamics. In: Advances on nonlinear dynamics of electronic systems. World Scientific, Singapore, pp 97–102

    Chapter  Google Scholar 

  • Pusuluri K, Pikovsky A, Shilnikov A (2017) Unraveling the chaos-land and its organization in the Rabinovich system. In: Advances in dynamics, patterns, cognition. Springer, pp 41–60. https://doi.org/10.1007/978-3-319-53673-6_4

    Chapter  Google Scholar 

  • Rabinovich M, Varona P, Silverston AL, Abarbanel HD (2006) Dynamics principles in neuroscience. Rev Mod Phys 78(4):1213–1265

    Article  ADS  Google Scholar 

  • Rinaldi S, Muratori S (1992) Slow-fast limit cycles in predator-prey models. Ecol Model 61:287

    Article  MATH  Google Scholar 

  • Rinzel J (1985) Bursting oscillations in an excitable membrane model, in ordinary and partial differential equations. Lect Notes Math 1151:304

    Article  MathSciNet  MATH  Google Scholar 

  • Rinzel J, Ermentrout B (1989) Analysis of neural excitability and oscillations. In: Koch C, Segev I (eds) Methods of neural modeling: from synapses to networks. MIT Press, Cambridge, pp 135–169

    Google Scholar 

  • Rowat PF, Elson RC (2004) State-dependent effects of Na-channel noise on neuronal burst generation. J Comp Neurosci 16:87–0112

    Article  Google Scholar 

  • Rubin J, Terman D (2002a) Synchronized activity and loss of synchrony among heterogeneous conditional oscillators. SIAM J Appl Dyn Sys 1:146

    Article  MathSciNet  MATH  Google Scholar 

  • Rubin J, Terman D (2002b) Geometric singular perturbation analysis of neuronal dynamics. In: Fiedler B (ed) Handbook of dynamical systems, vol 2. Elsevier, Amsterdam, pp 93–146

    MATH  Google Scholar 

  • Rubin J, Terman D (2004) High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model. J Comput Neurosci 16:211

    Article  Google Scholar 

  • Rutherford M, Roberts W (2009) Spikes and membrane potential oscillations in hair cells generate periodic afferent activity in the frog sacculus. J Neurosci 29(32):10025–10037

    Article  Google Scholar 

  • Sharkovsky AN, Kolyada SF, Sivak AG, Fedorenko VV (1997) Dynamics of one dimensional maps. Mathematics and its applications, vol 407. Kluwer, Dordrecht

    Book  MATH  Google Scholar 

  • Sherman A (1994) Anti-phase, asymmetric, and aperiodic oscillations in excitable cells I. Coupled bursters. Bull Math Biol 56:811–835

    MATH  Google Scholar 

  • Shilnikov A (2012) Complete dynamical analysis of a neuron model. J Nonlinear Dyn 68(3):305–328

    Article  MathSciNet  MATH  Google Scholar 

  • Shilnikov A, Cymbaluyk G (2004) Homoclinic saddle-node orbit bifurcations en a route between tonic spiking and bursting in neuron models, invited paper. Regul Chaot Dyn 3(9):281–297

    Article  ADS  Google Scholar 

  • Shilnikov A, Cymbalyuk G (2005) Transition between tonic-spiking and bursting in a neuron model via the blue-sky catastrophe. Phys Rev Lett 94:048101

    Article  ADS  Google Scholar 

  • Shilnikov AL, Kolomiets ML (2008) Methods of the qualitative theory for the Hindmarsh-Rose model: a case study – a tutorial. Int J Bifurc Chaos 18(7):1–32

    MathSciNet  MATH  Google Scholar 

  • Shilnikov AL, Rulkov NF (2003) Origin of chaos in a two-dimensional map modeling spiking-bursting neural activity. J Bifurc Chaos 13(11):3325–3340

    Article  MathSciNet  MATH  Google Scholar 

  • Shilnikov AL, Rulkov NF (2004) Subthreshold oscillations in a map-based neuron model. Phys Lett A 328:177–184

    Article  ADS  MATH  Google Scholar 

  • Shilnikov LP, Turaev DV (1997) On simple bifurcations leading to hyperbolic attractors. Comput Math Appl 34:441–457

    MathSciNet  Google Scholar 

  • Shilnikov L, Turaev D (2000) A new simple bifurcation of a periodic orbit of blue sky catastrophe type, in methods of qualitative theory of differential equations and related topics. AMS Trans Ser II 200:165–188

    MATH  Google Scholar 

  • Shilnikov LP, Shilnikov AL, Turaev DV, Chua L (1998/2001) Methods of qualitative theory in nonlinear dynamics. Volumes I and II. World Scientific, Singapore

    MATH  Google Scholar 

  • Shilnikov AL, Shilnikov LP, Turaev DV (2004) Mathematical aspects of classical synchronization theory: a tutorial. J Bifurc Chaos 14(7):2143–2160

    Article  MATH  Google Scholar 

  • Shilnikov A, Calabrese R, Cymbalyuk G (2005a) Mechanism of bi-stability: tonic spiking and bursting in a neuron model. Phys Rev E 71(1):205

    Google Scholar 

  • Shilnikov A, Shilnikov L, Turaev D (2005b) Blue sky catastrophe in singularly perturbed systems. Moscow Math J 5(1):205–218

    Article  MathSciNet  MATH  Google Scholar 

  • Shilnikov AL, Gordon R, Belykh I (2008) Polyrhythmic synchronization in bursting network motifs. J Chaos 18:037120

    Article  ADS  Google Scholar 

  • Shilnikov LP, Shilnikov AL, Turaev DV (2014) Showcase of blue sky catastrophes. J Bifurc Chaos 24(8):1440003

    Article  MathSciNet  MATH  Google Scholar 

  • Shochat E, Rom-Kedar V (2008) Novel strategies for granulocyte colony-stimulating factor treatment of severe prolonged neutropenia suggested by mathematical modeling. Clin Cancer Res 14:6354–6363

    Article  Google Scholar 

  • Somers D, Kopell N (1993) Rapid synchronization through fast threshold modulation. Biol Cybern 68:393

    Article  Google Scholar 

  • Steriade M, Jones EG, Llinás RR (1990) Thalamic oscillations and signaling. Wiley, New York

    Google Scholar 

  • Steriade M, McCormick DA, Sejnowski TJ (1993) Thalamocortical oscillations in the sleeping and aroused brain. Science 262:679–685

    Article  ADS  Google Scholar 

  • Terman D (1991) Chaotic spikes arising from a model of bursting in excitable membranes. SIAM J Appl Math 51(5):1418–1450

    Article  MathSciNet  MATH  Google Scholar 

  • Terman D (1992) The transition from bursting to continuous spiking in an excitable membrane model. J Nonlinear Sci 2:133–182

    Article  ADS  MathSciNet  MATH  Google Scholar 

  • Terman D, Kopell N, Bose A (1998) Dynamics of two mutually coupled slow inhibitory neurons. Phys D 117:241

    Article  MathSciNet  MATH  Google Scholar 

  • Tikhonov AN (1948) On the dependence of solutions of differential equations from a small parameter. Mat Sb 22(64):193–204

    Google Scholar 

  • Timofeev I, Bazhenov M, Sejnowski T, Steriade M (2002) Cortical hyperpolarization-activateddepolarizing current takes part in the generation of focal paroxysmal activities. Proc Natl Acad Sci USA 99(14):9533–9537

    Article  ADS  Google Scholar 

  • Tobin A-E, Calabrese RL (2006) Endogenous and half-center bursting in morphologically-inspired models of leech heart interneurons. J Neurophysiol 96:2089–2109

    Article  Google Scholar 

  • Turaev DV, Shilnikov LP (1995) Blue sky catastrophes. Dokl Math 51:404–407

    MATH  Google Scholar 

  • Turrigiano G, Marder E, Abbott L (1996) Cellular short-term memory from a slow potassium conductance. J Neurophysiol 75:963–966

    Article  Google Scholar 

  • Wang XJ (1993) Genesis of bursting oscillations in the Hindmarsh-Rose model and homoclinicity to a chaotic saddle. Phys D 62:263–274

    Article  MathSciNet  MATH  Google Scholar 

  • Wang X-J, Rinzel J (1992) Alternating and synchronous rhythms in reciprocally inhibitory model neurons. Neural Comput 4:84

    Article  Google Scholar 

  • Wang XJ, Rinzel J (1995) Oscillatory and bursting properties of neurons. In: Arbib M (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge, pp 686–691

    Google Scholar 

  • Wojcik J, Shilnikov A (2011) Voltage interval mappings for activity transitions in neuron models for elliptic bursters. Phys D 240(14–15):1164–1180

    Article  MATH  Google Scholar 

  • Wojcik J, Schwabedal J, Clewley R, Shilnikov AL (2014) Key bifurcations of bursting polyrhythms in 3-cell central pattern generators. PLoS One 9(4):e92918

    Article  ADS  Google Scholar 

  • Yang Z, Qishao L, Li L (2006) The genesis of period-adding bursting without bursting-chaos in the Chay model. Chaos Solitons Fractals 27(3):689–697

    Article  ADS  MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was funded in part by the NSF grant IOS-1455527 and the RSF grant 14-41-00044 at Lobachevsky University of Nizhny Novgorod. We thank the Brains and Behavior initiative of Georgia State University for providing pilot grant support and the doctoral fellowships of K. Pusuluri and H. Ju. We acknowledge the support of NVIDIA Corporation with the Tesla K40 GPUs used in this study. Finally, we are grateful to all the current and past members of the Shilnikov NeurDS lab for productive discussions.

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Appendix

Appendix

Leech Heart Interneuron Model

The reduced leech heart model is derived using the Hodgkin-Huxley formalism:

$$ {\displaystyle \begin{array}{c}C{V}^{\prime }=-{I}_{\mathrm{Na}}-{I}_{\mathrm{K}2}-{I}_{\mathrm{leak}}+{I}_{\mathrm{app}},\\ {}{\tau}_{\mathrm{Na}}{h}_{\mathrm{Na}}^{\prime }={h}_{\mathrm{Na}}^{\infty }(V)-h,\\ {}{\tau}_{\mathrm{K}2}{m}_{\mathrm{K}2}^{\prime }={m}_{\mathrm{K}2}^{\infty }(V)-{m}_{\mathrm{K}2},\end{array}} $$
(3)

with

$$ {I}_{leak}=8\left(V+0.046\right),\, {I}_{\mathrm{K}2}=30{m}_{\mathrm{k}2}^2\left(V+0.07\right),\, {I}_{\mathrm{Na}}=200{\left[{m}_{\mathrm{Na}}^{\infty }(V)\right]}^3{h}_{\mathrm{Na}}\left(V-0.045\right), $$

and where V is the membrane potential, C = 0.5; hNa is a fast (Ï„Na = 0.0405 sec) activation of INa, and mK2; IL describes the slow (Ï„K2 = 0.25 sec) activation of IK2, Iapp is an applied current. The steady states \( {h}_{\mathrm{Na}}^{\infty }(V) \), \( {m}_{\mathrm{Na}}^{\infty }(V) \), \( {m}_{\mathrm{K}2}^{\infty }(V) \), of the of the gating variables are given by the Boltzmann equations given by

$$ {\displaystyle \begin{array}{c}{h}_{\mathrm{Na}}^{\infty }(V)=\left[1+\exp \left(500(0.0333)+V\right)\right)\Big]{}^{-1},\\ {}{m}_{\mathrm{Na}}^{\infty }(V)=\left[1+\exp \left(-150(0.0305)+V\right)\right)\Big]{}^{-1},\\ {}{m}_{\mathrm{K}2}^{\infty }(V)=\left[1+\exp \left(-83(0.018)+{\mathrm{V}}_{\mathrm{K}2}^{\mathrm{shift}}+V\right)\right)\Big]{}^{-1}.\end{array}} $$
(4)

The bifurcation parameter \( {\mathrm{V}}_{\mathrm{K}2}^{\mathrm{shift}} \) of the model is a deviation from the experimentally determined voltage V1/2 = 0.018 V corresponding to the half-activated potassium channel, i.e., to \( {m}_{\mathrm{k}2}^{\infty }(0.018)=1/2 \). In its range, \( {\mathrm{V}}_{\mathrm{K}2}^{\mathrm{shift}} \) is [−0.025; 0.0018]V the upper boundary corresponds to the hyperpolarized quiescent state of the neuron, whereas the model produces spiking oscillations at the lower end \( {\mathrm{V}}_{\mathrm{K}2}^{\mathrm{shift}} \) values and bursts in between.

Chay Model

The 3D Hodgkin-Huxley type Chay model reads as follows:

$$ {\displaystyle \begin{array}{c}{V}^{\prime }=-{g}_I{m}_{\infty}^3{h}_{\infty}\left(V-{V}_I\right)-{g}_{K,V}{n}_{\infty}^4\left(V-{V}_K\right)-{g}_{K,C}\frac{C}{1+C}\left(V-{V}_K\right)-{g}_L\left(V-{V}_L\right),\\ {}{n}^{\prime }=\left({n}_{\infty}\left[V\right]-n\right)/{\tau}_n\left[V\right],\\ {}{C}^{\prime }=\rho \left\{{m}_{\infty}^3{h}_{\infty}\left({V}_C-V\right)-{k}_CC\right\},\end{array}} $$
(5)

where n represents the gating variable of the voltage-sensitive K+ channel and C represents the intracellular free calcium concentration. See (Chay 1985) for the detailed description.

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Pusuluri, K., Ju, H., Shilnikov, A. (2020). Chaotic Dynamics in Neural Systems. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_738-1

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