Analytic approximations of statistical quantities and response of noisy oscillators

Abstract Nonlinear oscillators have been utilized in many contexts because they encompass a large class of phenomena. For a reduced phase oscillator model with weak noise forcing that is necessarily multiplicative, we provide analytic formulas for the stationary statistical quantities of the random period. This is an important quantity which we term ‘response’ (i.e., the spike times, instantaneous frequency in neuroscience, the cycle time in chemical reactions, etc.) that is often analytically intractable in noisy oscillator systems. The analytic formulas are accurate in the weak noise limit so that one does not have to numerically solve a time-varying Fokker–Planck equation. The steady-state and dynamic responses are also analyzed with deterministic forcing. A second order analytic formula is derived for the steady-state response, whereas the dynamic response with time-varying forcing is more complicated. We focus on the specific case where the forcing is sinusoidal and accurately capture the frequency response with an analytic approximation that is obtained with a rescaling of the equation. By utilizing various techniques in the weak noise regime, this work leads to a better understanding of how the random period of nonlinear oscillators are affected by multiplicative noise and external forcing. Comparisons of the asymptotic formulas with a full oscillator system confirm the qualitative accurateness of the theory.

[1]  Xiaoqin Wang,et al.  Temporal and rate representations of time-varying signals in the auditory cortex of awake primates , 2001, Nature Neuroscience.

[2]  H. Risken The Fokker-Planck equation : methods of solution and applications , 1985 .

[3]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. I. The single spike train. , 1967, Biophysical journal.

[4]  Nicolas Fourcaud-Trocmé,et al.  Correlation-induced Synchronization of Oscillations in Olfactory Bulb Neurons , 2022 .

[5]  Cheng Ly,et al.  Coupling regularizes individual units in noisy populations. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  D. Cox,et al.  The statistical analysis of series of events , 1966 .

[7]  Cheng Ly,et al.  Analysis of Recurrent Networks of Pulse-Coupled Noisy Neural Oscillators , 2010, SIAM J. Appl. Dyn. Syst..

[8]  R. Spigler,et al.  The Kuramoto model: A simple paradigm for synchronization phenomena , 2005 .

[9]  H. Daido,et al.  Intrinsic fluctuations and a phase transition in a class of large populations of interacting oscillators , 1990 .

[10]  F. Jülicher,et al.  Auditory sensitivity provided by self-tuned critical oscillations of hair cells. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[11]  E. Izhikevich,et al.  Weakly connected neural networks , 1997 .

[12]  B. Knight The Relationship between the Firing Rate of a Single Neuron and the Level of Activity in a Population of Neurons , 1972, The Journal of general physiology.

[13]  S. Strogatz,et al.  Stability of incoherence in a population of coupled oscillators , 1991 .

[14]  M. Andermann,et al.  Embodied Information Processing: Vibrissa Mechanics and Texture Features Shape Micromotions in Actively Sensing Rats , 2008, Neuron.

[15]  Bard Ermentrout,et al.  Type I Membranes, Phase Resetting Curves, and Synchrony , 1996, Neural Computation.

[16]  Eugene M. Izhikevich,et al.  Phase Equations for Relaxation Oscillators , 2000, SIAM J. Appl. Math..

[17]  Idan Segev,et al.  Methods in neuronal modeling: From synapses to networks , 1989 .

[18]  Cheng Ly,et al.  Synchronization dynamics of two coupled neural oscillators receiving shared and unshared noisy stimuli , 2009, Journal of Computational Neuroscience.

[19]  Carson C. Chow,et al.  Kinetic theory of coupled oscillators. , 2006, Physical review letters.

[20]  A. Winfree Patterns of phase compromise in biological cycles , 1974 .

[21]  I. Prigogine,et al.  Symmetry Breaking Instabilities in Dissipative Systems. II , 1968 .

[22]  Eugene M. Izhikevich,et al.  Weakly pulse-coupled oscillators, FM interactions, synchronization, and oscillatory associative memory , 1999, IEEE Trans. Neural Networks.

[23]  Gregory A. Clark,et al.  Relative spike timing in stochastic oscillator networks of the Hermissenda eye , 2010, Biological Cybernetics.

[24]  G Bard Ermentrout,et al.  Class-II neurons display a higher degree of stochastic synchronization than class-I neurons. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  G Bard Ermentrout,et al.  Stochastic phase reduction for a general class of noisy limit cycle oscillators. , 2009, Physical review letters.

[26]  J. Teramae,et al.  Robustness of the noise-induced phase synchronization in a general class of limit cycle oscillators. , 2004, Physical review letters.

[27]  G. Ermentrout,et al.  On chains of oscillators forced at one end , 1991 .

[28]  Adi R. Bulsara,et al.  Analytic Expressions for Rate and CV of a Type I Neuron Driven by White Gaussian Noise , 2003, Neural Computation.

[29]  Jürgen Kurths,et al.  Synchronization - A Universal Concept in Nonlinear Sciences , 2001, Cambridge Nonlinear Science Series.

[30]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[31]  G. Ermentrout,et al.  Analysis of neural excitability and oscillations , 1989 .

[32]  M. Schetzen The Volterra and Wiener Theories of Nonlinear Systems , 1980 .

[33]  Bard Ermentrout,et al.  Simulating, analyzing, and animating dynamical systems - a guide to XPPAUT for researchers and students , 2002, Software, environments, tools.

[34]  A. J. Hudspeth,et al.  How the ear's works work , 1989, Nature.

[35]  Philip Holmes,et al.  The Influence of Spike Rate and Stimulus Duration on Noradrenergic Neurons , 2004, Journal of Computational Neuroscience.

[36]  J. Guckenheimer,et al.  Isochrons and phaseless sets , 1975, Journal of mathematical biology.

[37]  I. Prigogine,et al.  On symmetry-breaking instabilities in dissipative systems , 1967 .

[38]  Kresimir Josic,et al.  A finite volume method for stochastic integrate-and-fire models , 2009, Journal of Computational Neuroscience.

[39]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. , 1967, Biophysical journal.

[40]  Bruce W. Knight,et al.  Dynamics of Encoding in Neuron Populations: Some General Mathematical Features , 2000, Neural Computation.

[41]  Lutz Schimansky-Geier,et al.  Noise-induced transport with low randomness. , 2002, Physical review letters.

[42]  Hansel,et al.  Clustering in globally coupled phase oscillators. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[43]  S. Strogatz,et al.  Synchronization of pulse-coupled biological oscillators , 1990 .

[44]  L Glass,et al.  Phase locking, period doubling bifurcations and chaos in a mathematical model of a periodically driven oscillator: A theory for the entrainment of biological oscillators and the generation of cardiac dysrhythmias , 1982, Journal of mathematical biology.

[45]  David Golomb,et al.  The Combined Effects of Inhibitory and Electrical Synapses in Synchrony , 2005, Neural Computation.

[46]  Eric Shea-Brown,et al.  On the Phase Reduction and Response Dynamics of Neural Oscillator Populations , 2004, Neural Computation.

[47]  Yoji Kawamura,et al.  Noise-induced synchronization and clustering in ensembles of uncoupled limit-cycle oscillators. , 2007, Physical review letters.

[48]  T. Poggio,et al.  The Volterra Representation and the Wiener Expansion: Validity and Pitfalls , 1977 .

[49]  Johan Grasman,et al.  Asymptotic Methods for the Fokker-Planck Equation and the Exit Problem in Applications , 1999 .

[50]  Carson C. Chow,et al.  Correlations, fluctuations, and stability of a finite-size network of coupled oscillators. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[51]  C. Gardiner Handbook of Stochastic Methods , 1983 .

[52]  Duane Q. Nykamp,et al.  A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientation Tuning , 2004, Journal of Computational Neuroscience.

[53]  Henry C. Tuckwell,et al.  Analytical and Simulation Results for Stochastic Fitzhugh-Nagumo Neurons and Neural Networks , 1998, Journal of Computational Neuroscience.

[54]  L. L. Bonilla,et al.  Time-periodic phases in populations of nonlinearly coupled oscillators with bimodal frequency distributions , 1998 .

[55]  J. Crawford,et al.  Amplitude expansions for instabilities in populations of globally-coupled oscillators , 1993, patt-sol/9310005.

[56]  Yoshiki Kuramoto,et al.  Chemical Oscillations, Waves, and Turbulence , 1984, Springer Series in Synergetics.

[57]  Kenichi Arai,et al.  Phase reduction of stochastic limit cycle oscillators. , 2007, Physical review letters.

[58]  Jason Ritt,et al.  Evaluation of entrainment of a nonlinear neural oscillator to white noise. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[59]  Renato Spigler,et al.  Nonlinear stability of incoherence and collective synchronization in a population of coupled oscillators , 1992 .

[60]  H C Tuckwell,et al.  Noisy spiking neurons and networks: useful approximations for firing probabilities and global behavior. , 1998, Bio Systems.

[61]  B. Grothe,et al.  Temporal processing in sensory systems , 2000, Current Opinion in Neurobiology.

[62]  Olivier Faugeras,et al.  The spikes trains probability distributions: A stochastic calculus approach , 2007, Journal of Physiology-Paris.