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Sequence of pseudoequilibria describes the long-time behavior of the nonlinear noisy leaky integrate-and-fire model with large delay (2403.00971v3)

Published 1 Mar 2024 in math.AP, cs.NA, and math.NA

Abstract: There is a wide range of mathematical models that describe populations of large numbers of neurons. In this article, we focus on nonlinear noisy leaky integrate-and-fire (NNLIF) models that describe neuronal activity at the level of the membrane potential. We introduce a sequence of states, which we call pseudoequilibria, and give evidence of their defining role in the behavior of the NNLIF system when a significant synaptic delay is considered. The advantage is that these states are determined solely by the system's parameters and are derived from a sequence of firing rates that result from solving a recurrence equation. We propose a strategy to show convergence to an equilibrium for a weakly connected system with large transmission delay, based on following the sequence of pseudoequilibria. Unlike direct entropy dissipation methods, this technique allows us to see how a large delay favors convergence. We present a detailed numerical study to support our results. This study helps us understand, among other phenomena, the appearance of periodic solutions in strongly inhibitory networks.

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References (45)
  1. Noisy Fitzhugh-Nagumo model: From single elements to globally coupled networks. Physical Review E 69, 2 (2004), 026202.
  2. Adaptive exponential integrate-and-fire model as an effective description of neural activity. Journal of neurophysiology 94 (2005), 3637–3642.
  3. Brunel, N. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of computational neuroscience 8, 3 (2000), 183–208.
  4. Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural computation 11, 7 (1999), 1621–1671.
  5. Cáceres, M. J. A review about nonlinear noisy leaky integrate and fire models for neural networks. Rivista di Matematica della Università di Parma 2 (2019), 269–298.
  6. On the asymptotic behavior of the NNLIF neuron model for general connectivity strength. arXiv preprint arXiv:2401.13534 (2024).
  7. Analysis of nonlinear noisy integrate & fire neuron models: blow-up and steady states. The Journal of Mathematical Neuroscience 1, 1 (2011), 7.
  8. A numerical solver for a nonlinear Fokker–Planck equation representation of neuronal network dynamics. Journal of Computational Physics 230, 4 (2011), 1084–1099.
  9. Beyond blow-up in excitatory integrate and fire neuronal networks: refractory period and spontaneous activity. Journal of theoretical biology 350 (2014), 81–89.
  10. An understanding of the physical solutions and the blow-up phenomenon for nonlinear noisy leaky integrate and fire neuronal models. Communications in Computational Physics 30, 3 (2021), 820–850.
  11. Global-in-time solutions and qualitative properties for the NNLIF neuron model with synaptic delay. Communications in Partial Differential Equations 44, 12 (2019), 1358–1386.
  12. Blow-up, steady states and long time behaviour of excitatory-inhibitory nonlinear neuron models. Kinetic & Related Models 10, 3 (2017), 587.
  13. Analysis and numerical solver for excitatory-inhibitory networks with delay and refractory periods. ESAIM: Mathematical Modelling and Numerical Analysis 52, 5 (2018), 1733–1761.
  14. Asymptotic behaviour of neuron population models structured by elapsed-time. Nonlinearity 32, 2 (2019), 464.
  15. Qualitative properties of solutions for the noisy integrate & fire model in computational neuroscience. Nonlinearity 25 (2015), 3365–3388.
  16. Classical solutions for a nonlinear Fokker-Planck equation arising in computational neuroscience. Communications in Partial Differential Equations 38, 3 (2013), 385–409.
  17. Chevallier, J. Mean-field limit of generalized Hawkes processes. Stochastic Processes and their Applications 127, 12 (2017), 3870–3912.
  18. Microscopic approach of a time elapsed neural model. Mathematical Models and Methods in Applied Sciences 25, 14 (2015), 2669–2719.
  19. Essentially non-oscillatory and weighted essentially non-oscillatory schemes for hyperbolic conservation laws. Springer, 1998.
  20. Particle systems with a singular mean-field self-excitation. Application to neuronal networks. Stochastic Processes and their Applications 125, 6 (2015), 2451–2492.
  21. Global solvability of a networked integrate-and-fire model of McKean–Vlasov type. The Annals of Applied Probability 25, 4 (2015), 2096–2133.
  22. Dilating blow-up time: A generalized solution of the NNLIF neuron model and its global well-posedness. arXiv preprint arXiv:2206.06972 (2022).
  23. A synchronization-capturing multi-scale solver to the noisy integrate-and-fire neuron networks. arXiv preprint arXiv:2305.05915 (2023).
  24. The mean-field equation of a leaky integrate-and-fire neural network: measure solutions and steady states. Nonlinearity 33, 12 (2020), 6381.
  25. Population density models of integrate-and-fire neurons with jumps: well-posedness. Journal of Mathematical Biology 67, 3 (2013), 453–481.
  26. Synchronization of an excitatory integrate-and-fire neural network. Bulletin of mathematical biology 75, 4 (2013), 629–648.
  27. Fitzhugh, R. Impulses and physiological states in theoretical models of nerve membrane. Biophysical journal 1, 6 (1961), 445–466.
  28. A structure preserving numerical scheme for Fokker-Planck equations of neuron networks: Numerical analysis and exploration. Journal of Computational Physics 433 (2021), 110195.
  29. Theoretical study of the emergence of periodic solutions for the inhibitory NNLIF neuron model with synaptic delay. Mathematical Neuroscience and Applications, 4 (2022), 1–37.
  30. Lapicque, L. Recherches quantitatives sur l’excitation électrique des nerfs traitée comme une polarisation. J. Physiol. Pathol. Gen 9 (1907), 620–635.
  31. Rigorous justification of the Fokker–Planck equations of neural networks based on an iteration perspective. SIAM Journal on Mathematical Analysis 54, 1 (2022), 1270–1312.
  32. On a kinetic Fitzhugh–Nagumo model of neuronal network. Communications in Mathematical Physics 342, 3 (2016), 1001–1042.
  33. Cascade-induced synchrony in stochastically driven neuronal networks. Physical Review E 82 (2010), 041903.
  34. Dynamics of current-based, poisson driven, integrate-and-fire neuronal networks. Communications in Mathematical Sciences 8 (2010), 541–600.
  35. On the simulation of large populations of neurons. Journal of Computational Neuroscience 8 (2000), 51–63.
  36. Dynamics of a structured neuron population. Nonlinearity 23 (2010), 55–75.
  37. Relaxation and self-sustained oscillations in the time elapsed neuron network model. SIAM Journal on Applied Mathematics 73, 3 (2013), 1260–1279.
  38. Adaptation and fatigue model for neuron networks and large time asymptotics in a nonlinear fragmentation equation. The Journal of Mathematical Neuroscience (JMN) 4, 1 (2014), 1–26.
  39. On a voltage-conductance kinetic system for integrate and fire neural networks. Kinetic and related models, AIMS 6, 4 (2013), 841–864.
  40. Derivation of a voltage density equation from a voltage-conductance kinetic model for networks of integrate-and-fire neurons. Communications in Mathematical Sciences 17, 5 (2019).
  41. Kinetic theory for neuronal networks with fast and slow excitatory conductances driven by the same spike train. Physical Review E 77, 041915 (2008), 1–13.
  42. Towards a further understanding of the dynamics in the excitatory NNLIF neuron model: Blow-up and global existence. Kinetic & Related Models 14, 5 (2021).
  43. Convergence towards equilibrium for a model with partial diffusion. hal-03845918 (2022).
  44. Dynamics of neural populations: Stability and synchrony. Network: Computation in Neural Systems 17 (2006), 3–29.
  45. A spectral method for a Fokker-Planck equation in neuroscience with applications in neural networks with learning rules. arXiv preprint arXiv:2305.00275 (2023).
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