Delay-induced oscillations in Wilson and Cowan’s model: an analysis of the subthalamo-pallidal feedback loop in healthy and parkinsonian subjects

The model proposed by Wilson and Cowan (1972) describes the dynamics of two interacting subpopulations of excitatory and inhibitory neurons. It has been used to model neural structures like the olfactory bulb, whisker barrels, and the subthalamo-pallidal system. It is well-known that this system can exhibit an oscillatory behavior that is amplified by the presence of delays. In the absence of delays, the conditions for stability are well-known. The aim of our paper is to clarify these conditions when delays are included in the model. The first ingredient of our methods is a new necessary and sufficient condition for the existence of multiple equilibria. This condition is related to those for local asymptotic stability. In addition, a sufficient condition for global stability is also proposed. The second and main ingredient is a stability analysis of the system in the frequency-domain, based on the Nyquist criterion, that takes the four independent delays into account. The methods proposed in this paper can be applied to analyse the stability of the subthalamo-pallidal feedback loop, a deep brain structure involved in Parkinson’s disease. Our stability conditions are easy to compute and characterize sharply the system’s parameters for which spontaneous oscillations appear.

[1]  David Terman,et al.  Mathematical foundations of neuroscience , 2010 .

[2]  P. N. Paraskevopoulos,et al.  Modern Control Engineering , 2001 .

[3]  B. Ermentrout,et al.  Oscillations in a refractory neural net , 2001, Journal of mathematical biology.

[4]  H. Nyquist,et al.  The Regeneration Theory , 1954, Journal of Fluids Engineering.

[5]  D. Plenz,et al.  A basal ganglia pacemaker formed by the subthalamic nucleus and external globus pallidus , 1999, Nature.

[6]  P. Olver Nonlinear Systems , 2013 .

[7]  J. Cowan,et al.  Temporal oscillations in neuronal nets , 1979, Journal of mathematical biology.

[8]  J. Cowan,et al.  A mathematical theory of visual hallucination patterns , 1979, Biological Cybernetics.

[9]  L. Monteiro,et al.  Analytical results on a Wilson-Cowan neuronal network modified model. , 2002, Journal of theoretical biology.

[10]  J. Hale Theory of Functional Differential Equations , 1977 .

[11]  Hans Zwart,et al.  An Introduction to Infinite-Dimensional Linear Systems Theory , 1995, Texts in Applied Mathematics.

[12]  R. Bogacz,et al.  Improved conditions for the generation of beta oscillations in the subthalamic nucleus-globus pallidus network , 2012, BMC Neuroscience.

[13]  Keqin Gu,et al.  Stability and Stabilization of Systems with Time Delay , 2011, IEEE Control Systems.

[14]  H. Schuster,et al.  A model for neuronal oscillations in the visual cortex , 1990, Biological Cybernetics.

[15]  H. Schuster,et al.  A model for neuronal oscillations in the visual cortex , 1990, Biological Cybernetics.

[16]  Dirk Roose,et al.  Numerical bifurcation analysis of delay differential equations using DDE-BIFTOOL , 2002, TOMS.

[17]  D.L. Elliott,et al.  Feedback systems: Input-output properties , 1976, Proceedings of the IEEE.

[18]  Charles J. Wilson,et al.  A model of reverse spike frequency adaptation and repetitive firing of subthalamic nucleus neurons. , 2004, Journal of neurophysiology.

[19]  John R. Terry,et al.  Conditions for the Generation of Beta Oscillations in the Subthalamic Nucleus–Globus Pallidus Network , 2010, The Journal of Neuroscience.

[20]  Peter Brown,et al.  Oscillations in the Basal Ganglia: The good, the bad, and the unexpected , 2005 .

[21]  Joshua C. Brumberg,et al.  A quantitative population model of whisker barrels: Re-examining the Wilson-Cowan equations , 1996, Journal of Computational Neuroscience.

[22]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[23]  S. Coombes,et al.  Delays in activity-based neural networks , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  P. Holmes,et al.  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields , 1983, Applied Mathematical Sciences.

[25]  Xiaolin Li,et al.  Stability and Bifurcation in a Neural Network Model with Two Delays , 2011 .

[26]  Jeffrey C. Lagarias,et al.  Minimax and Hamiltonian Dynamics of Excitatory-Inhibitory Networks , 1997, NIPS.

[27]  D. Willshaw,et al.  Subthalamic–pallidal interactions are critical in determining normal and abnormal functioning of the basal ganglia , 2002, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[28]  G. P. Szegö,et al.  Stability theory of dynamical systems , 1970 .

[29]  R. Llinás,et al.  Electrophysiology of globus pallidus neurons in vitro. , 1994, Journal of neurophysiology.

[30]  P. Holmes,et al.  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields , 1983, Applied Mathematical Sciences.

[31]  Stefan Rotter,et al.  The Role of Inhibition in Generating and Controlling Parkinson’s Disease Oscillations in the Basal Ganglia , 2011, Front. Syst. Neurosci..

[32]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[33]  Daniel E. Miller,et al.  On the Achievable Delay Margin Using LTI Control for Unstable Plants , 2007, IEEE Transactions on Automatic Control.

[34]  Bruce A. Francis,et al.  Feedback Control Theory , 1992 .

[35]  C. A. Desoer,et al.  IV – LINEAR SYSTEMS , 1975 .

[36]  A. Galip Ulsoy,et al.  Analysis of a System of Linear Delay Differential Equations , 2003 .

[37]  J. Hopfield,et al.  Modeling the olfactory bulb and its neural oscillatory processings , 1989, Biological Cybernetics.

[38]  Bruno A. Olshausen,et al.  Book Review , 2003, Journal of Cognitive Neuroscience.

[39]  D. Hansel,et al.  Competition between Feedback Loops Underlies Normal and Pathological Dynamics in the Basal Ganglia , 2022 .

[40]  Nicolas Brunel,et al.  Dynamics of Networks of Excitatory and Inhibitory Neurons in Response to Time-Dependent Inputs , 2011, Front. Comput. Neurosci..