Analysis and numerical solver for excitatory-inhibitory networks with delay and refractory periods
ESAIM: Mathematical Modelling and Numerical Analysis , Tome 52 (2018) no. 5, pp. 1733-1761.

The network of noisy leaky integrate and fire (NNLIF) model is one of the simplest self-contained mean-field models considered to describe the behavior of neural networks. Even so, in studying its mathematical properties some simplifications are required [Cáceres and Perthame, J. Theor. Biol. 350 (2014) 81–89; Cáceres and Schneider, Kinet. Relat. Model. 10 (2017) 587–612; Cáceres, Carrillo and Perthame, J. Math. Neurosci. 1 (2011) 7] which disregard crucial phenomena. In this work we deal with the general NNLIF model without simplifications. It involves a network with two populations (excitatory and inhibitory), with transmission delays between the neurons and where the neurons remain in a refractory state for a certain time. In this paper we study the number of steady states in terms of the model parameters, the long time behaviour via the entropy method and Poincaré’s inequality, blow-up phenomena, and the importance of transmission delays between excitatory neurons to prevent blow-up and to give rise to synchronous solutions. Besides analytical results, we present a numerical solver, based on high order flux-splitting WENO schemes and an explicit third order TVD Runge-Kutta method, in order to describe the wide range of phenomena exhibited by the network: blow-up, asynchronous/synchronous solutions and instability/stability of the steady states. The solver also allows us to observe the time evolution of the firing rates, refractory states and the probability distributions of the excitatory and inhibitory populations.

DOI : 10.1051/m2an/2018014
Classification : 35K60, 35Q92, 82C31, 82C32, 92B20
Mots-clés : Neural networks, Leaky integrate and fire models, noise, blow-up, steady states, entropy, long time behavior, refractory states, transmission delay
Cáceres, María J. 1 ; Schneider, Ricarda 1

1
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     title = {Analysis and numerical solver for excitatory-inhibitory networks with delay and refractory periods},
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     pages = {1733--1761},
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Cáceres, María J.; Schneider, Ricarda. Analysis and numerical solver for excitatory-inhibitory networks with delay and refractory periods. ESAIM: Mathematical Modelling and Numerical Analysis , Tome 52 (2018) no. 5, pp. 1733-1761. doi : 10.1051/m2an/2018014. http://www.numdam.org/articles/10.1051/m2an/2018014/

[1] L.F. Abbott and C. Van Vreeswijk, Asynchronous states in networks of pulse-coupled oscillators. Phys. Rev. E 48 (1993) 1483–1490 | DOI

[2] J. Acebrón, A. Bulsara and W.J. Rappel, Noisy Fitzhugh-Nagumo model: from single elements to globally coupled networks. Phys. Rev. E 69 (2004) 026202 | DOI | MR

[3] L. Albantakis and G. Deco, The encoding of alternatives in multiple-choice decision making. Proc. Natl. Acad. . USA 106 (2009) 10308–10313 | DOI

[4] F. Apfaltrer, C. Ly and D. Tranchina, Population density methods for stochastic neurons with realistic synaptic kinetics: firing rate dynamics and fast computational methods. Netw. Comput. Neural Syst. 17 (2006) 373–418 | DOI

[5] G. Barna, T. Grobler and P. Erdi, Statistical model of the hippocampal CA3 region, II. The population framework: model of rhythmic activity in CA3 slice. Biol. Cybern. 79 (1998) 309–321 | Zbl

[6] R. Brette and W. Gerstner, Adaptive exponential integrate-and-fire model as an effective description of neural activity. J. Neurophysiol. 94 (2005) 3637–3642 | DOI

[7] N. Brunel, Dynamics of sparsely connected networks of excitatory and inhibitory spiking networks. J. Comput. Neurosci. 8 (2000) 183–208 | DOI | Zbl

[8] N. Brunel and V. Hakim, Fast global oscillations in networks of integrate-and-fire neurons with long firing rates. Neural Comput. 11 (1999) 1621–1671 | DOI

[9] N. Brunel and X.J. Wang, What determines the frequency of fast network oscillations with irregular neural discharge? I. Synaptic dynamics and excitation-inhibition balance. J. Neurophysiol. 90 (2003) 415–430 | DOI

[10] M.J. Cáceres and B. Perthame, Beyond blow-up in excitatory integrate and fire neuronal networks: refractory period and spontaneous activity. J. Theory Biol. 350 (2014) 81–89 | DOI | MR | Zbl

[11] M.J. Cáceres and R. Schneider, Blow-up, steady states and long time behaviour of excitatory-inhibitory nonlinear neuron models. Kinet. Relat. Model. 10 (2017) 587–612 | DOI | MR | Zbl

[12] M.J. Cáceres, J.A. Carrillo and B. Perthame, Analysis of nonlinear noisy integrate & fire neuron models: blow-up and steady states. J. Math. Neurosci. 1 (2011) 7 | DOI | MR | Zbl

[13] M.J. Cáceres, J.A. Carrillo and L. Tao, A numerical solver for a nonlinear Fokker-Planck equation representation of neuronal network dynamics. J. Comput. Phys. 230 (2011) 1084–1099 | DOI | MR | Zbl

[14] D. Cai, L. Tao and D.W. Mclaughlin, An embedded network approach for scale-up of fluctuation-driven systems with preservation of spike information. PNAS 101 (2004) 14288–14293 | DOI

[15] D. Cai, L. Tao, M. Shelley and D.W. Mclaughlin, An effective kinetic representation of fluctuation-driven neuronal networks with application to simple and complex cells in visual cortex. Proc. Natl. Acad. Sci. USA 101 (2004) 7757–7762 | DOI

[16] J.A. Carrillo and F. Vecil, Nonoscillatory interpolation methods applied to Vlasov-based models. SIAM J. Sci. Comput. 29 (2007) 1179–1206 | DOI | MR | Zbl

[17] J.A. Carrillo, I.M. Gamba, A. Majorana and C.-W. Shu, A WENO-solver for the transients of Boltzmann-Poisson system for semiconductor devices: performance and comparisons with Monte Carlo methods. J. Comput. Phys. 184 (2003) 498–525 | DOI | MR | Zbl

[18] J.A. Carrillo, I.M. Gamba, A. Majorana and C.-W. Shu, 2D semiconductor device simulations by Weno-Boltzmann schemes: efficiency, boundary conditions and comparison to Monte Carlo methods. J. Comput. Phys. 214 (2006) 55–80 | DOI | MR | Zbl

[19] J.A. Carrillo, M.D.M. González, M.P. Gualdani and M.E. Schonbek, Classical solutions for a nonlinear Fokker-Planck equation arising in computational neuroscience. Commun. Partial Differ. Equ. 38 (2013) 385–409 | DOI | MR | Zbl

[20] J. Carrillo, B. Perthame, D. Salort and D. Smets, Qualitative properties of solutions for the noisy integrate & fire model in computational neuroscience. Nonlinearity 25 (2015) 3365–3388 | DOI | MR | Zbl

[21] T. Chawanya, A. Aoyagi, T. Nishikawa, K. Okuda and Y. Kuramoto, A model for feature linking via collective oscillations in the primary visual cortex. Biol. Cybern. 68 (1993) 483–490 | DOI | Zbl

[22] J. Chevallier, Mean-Field Limit of Generalized Hawkes Processes. Preprint (2015) | arXiv | MR

[23] J. Chevallier, M.J. Cáceres, M. Doumic and P. Reynaud-Bouret, Microscopic approach of a time elapsed neural model. Math. Model. Methods Appl. Sci. 25 (2015) 2669–2719 | DOI | MR | Zbl

[24] F. Delarue, J. Inglis, S. Rubenthaler and E. Tanré, Global solvability of a networked integrate-and-fire model of McKean-Vlasov type. Ann. Appl. Probab. 25 (2015) 2096–2133 | DOI | MR | Zbl

[25] F. Delarue, J. Inglis, S. Rubenthaler and E. Tanré, Particle systems with a singular mean-field self-excitation. Application to neuronal networks. Stoch. Process. Appl. 125 (2015) 2451–2492 | DOI | MR | Zbl

[26] G. Dumont and P. Gabriel, The Mean-Field Equation of a Leaky Integrate-and-Fire Neural Network: Measure Solutions and Steady States. Preprint (2017) | arXiv | MR

[27] G. Dumont and J. Henry, Population density models of integrate-and-fire neurons with jumps: well-posedness. J. Math. Biol. 67 (2012) 453–481 | DOI | MR | Zbl

[28] G. Dumont and J. Henry, Synchronization of an excitatory integrate-and-fire neural network. Bull. Math. Biol. 75 (2013) 629–648 | DOI | MR | Zbl

[29] G. Dumont, J. Henry and C.O. Tarniceriu, Noisy threshold in neuronal models: connections with the noisy leaky integrate-and-fire model. J. Math. Biol. 73 (2016) 1413–1436 | DOI | MR | Zbl

[30] G. Dumont, J. Henry and C.O. Tarniceriu, Theoretical connections between mathematical neuronal models corresponding to different expressions of noise. J. Theor. Biol. 406 (2016) 31–41 | DOI | MR | Zbl

[31] G. Dumont, J. Henry and C.O. Tarniceriu, A Theoretical Connection Between the Noisy Leaky Integrate-and-Fire and Escape Rate Models: The Non-Autonomous Case. Preprint (2017) | arXiv

[32] R. Fitzhugh, Impulses and physiological states in theoretical models of nerve membrane. Biophys. J. 1 (1961) 445–466 | DOI

[33] N. Fourcaud and N. Brunel, Dynamics of the firing probability of noisy integrate-and-fire neurons. Neural Comput. 14 (2002) 2057–2110 | DOI | Zbl

[34] W. Gerstner, Population dynamics of spiking neurons: fast transients, asynchronous states, and locking. Neural Comput. 12 (2000) 43–89 | DOI

[35] W. Gerstner, Integrate-and-fire neurons and networks, in The Handbook of Brain Theory and Neural Networks, Vol. 2. (2002) 577–581

[36] W. Gerstner and W. Kistler, Spiking Neuron Models. Cambridge University Press, Cambridge (2002) | DOI | MR | Zbl

[37] C.M. Gray and W. Singer, Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl. Acad. Sci. USA86 (1989) 1698–1702 | DOI

[38] T. Guillamon, An introduction to the mathematics of neural activity. Butl. Soc. Catalana Mat. 19 (2004) 25–45 | MR

[39] E. Haskell, D. Nykamp and D. Tranchina, Population density methods for large-scale modeling of neuronal networks with realistic synaptic kinetics: cutting the dimension down to size. Netw. Compt. Neural. Syst. 12 (2001) 141–174 | DOI | Zbl

[40] J.A. Henrie and R. Shapley, LFP power spectra in V1 cortex: the graded effect of stimulus contrast. J. Neurophysiol. 94 (2005) 479–490

[41] E.M. Izhikevich and G.M. Edelman, Large-scale model of mammalian thalamocortical systems. Proc. Natl. Acad. Sci. USA 105 (2008) 3593–3598

[42] G.-S. Jiang and C.-W. Shu, Efficient implementation of weighted ENO schemes. J. Comput. Phys. 126 (1996) 202–228 | MR | Zbl

[43] B. Knight, Dynamics of encoding in a populaton neurons. J. Gen. Physiol. 59 (1972) 734–766

[44] A. Koulakov, S. Raghavachari, A. Kepecs and J. Lisman, Model for a robust neural integrator. Nat. Neurosci. 5 (2002) 775–782

[45] R.J. Leveque, Numerical Methods for Conservation Laws, 2nd edn. Lectures in Mathematics. Birkhäuser (1992) | MR | Zbl

[46] J. Marino, J. Schummers, D.C. Lyon, L. Schwabe, O. Beck, P. Wiesing, et al., Invariant computations in local cortical networks with balanced excitation and inhibition. Nat. Neurosci. 8 (2005) 194–201

[47] M. Mattia and P. Del Giudice, Population dynamics of interacting spiking neurons. Phys. Rev. E 66 (2002) 051917 | MR

[48] S. Mischler, C. Quininao and J. Touboul, On a kinetic Fitzhugh–Nagumo model of neuronal network. Commun. Math. Phys. 342 (2016) 1001–1042 | MR | Zbl

[49] K. Newhall, G. Kovačič, P. Kramer, A.V. Rangan and D. Cai, Cascade-induced synchrony in stochastically driven neuronal networks. Phys. Rev. E 82 (2010) 041903 | MR

[50] K. Newhall, G. Kovačič, P. Kramer, D. Zhou, A.V. Rangan and D. Cai, Dynamics of current-based, poisson driven, integrate-and-fire neuronal networks. Commun. Math. Sci. 8 (2010) 541–600 | DOI | MR | Zbl

[51] D. Nykamp and D. Tranchina, A population density method that facilitates large-scale modeling of neural networks: analysis and application to orientation tuning. J. Comput. Neurosci. 8 (2000) 19–50 | DOI | Zbl

[52] D. Nykamp and D. Tranchina, A population density method that facilitates large-scale modeling of neural networks: Extension to slow inhibitory synapses. Neural Comput. 13 (2001) 511–546 | DOI | Zbl

[53] A. Omurtag, B.W. Knight and L. Sirovich, On the simulation of large populations of neurons. J. Comput. Neurosci. 8 (2000) 51–63 | DOI | Zbl

[54] K. Pakdaman, B. Perthame and D. Salort, Dynamics of a structured neuron population. Nonlinearity 23 (2010) 55–75 | DOI | MR | Zbl

[55] K. Pakdaman, B. Perthame and D. Salort, Relaxation and self-sustained oscillations in the time elapsed neuron network model. SIAM J. Appl. Math. 73 (2013) 1260–1279 | DOI | MR | Zbl

[56] K. Pakdaman, B. Perthame and D. Salort, Adaptation and fatigue model for neuron networks and large time asymptotics in a nonlinear fragmentation equation. J. Math. Neurosci. 4 (2014) 1–26 | DOI | MR | Zbl

[57] B. Perthame and D. Salort, On a voltage-conductance kinetic system for integrate and fire neural networks. Kinet. Relat. Model. AIMS 6 (2013) 841–864 | DOI | MR | Zbl

[58] A.V. Rangan and D. Cai, Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks. J. Comput. Neurosci. 22 (2007) 81–100 | DOI | MR

[59] A.V. Rangan, D. Cai and D.W. Mclaughlin, Modeling the spatiotemporal cortical activity associated with the line-motion illusion in primary visual cortex. PNAS 102 (2005) 18793–18800 | DOI

[60] A.V. Rangan, D. Cai and D.W. Mclaughlin, Quantifying neuronal network dynamics through coarse-grained event trees. PNAS 105 (2008) 10990–10995 | DOI

[61] A.V. Rangan, G. Kovacic and D. Cai, Kinetic theory for neuronal networks with fast and slow excitatory conductances driven bythe same spike train. Phys. Rev. E 77 (2008) 1–13 | DOI | MR

[62] A. Renart, N. Brunel and X.-J. Wang, Mean-field theory of irregularly spiking neuronal populations and working memory in recurrent cortical networks, in Computational Neuroscience: A Comprehensive Approach, edited by J. Feng. CRC Mathematical Biology and Medicine Series. Chapman & Hall (2004). | MR

[63] H. Risken, The Fokker-Planck Equation: Methods of Solution and Approximations, 2nd edn. Vol. 18 of Springer Series in Synergetics. Springer-Verlag, Berlin (1989) | MR | Zbl

[64] P. Robert and J. Touboul, On the dynamics of random neuronal networks. J. Stat. Phys. 165 (2016) 545–584 | DOI | MR | Zbl

[65] C. Rossant, D.F.M. Goodman, B. Fontaine, J. Platkiewicz, A.K. Magnusson and R. Brette, Fitting neuron models to spike trains. Front. Neurosci. 5 (2011) 1–8 | DOI

[66] M. Shelley and L. Tao, Efficient and accurate time-stepping schemes for integrate-and-fire neuronal networks. J. Comput. Neurosci. 11 (2001) 111–119 | DOI

[67] C.-W. Shu, Essentially non-oscillatory and weighted esentially non-oscillatory schemes for hyperbolic conservation laws, in Advanced Numerical Approximation of Nonlinear Hyperbolic Equations, Vol. 1697, edited by B. Cockburn, C. Johnson, C.-W. Shu, E. Tadmor and A. Quarteroni. Springer (1998) 325–432 | DOI | MR | Zbl

[68] C.-W. Shu and S. Osher, Efficient implementation of essentially non-oscillatory shock-capturing schemes. J. Comput. Phys. 77 (1988) 439–471 | DOI | MR | Zbl

[69] D.C. Somers, S.B. Nelson and M. Sur, An emergent model of orientation selectivity in cat visual cortical simple cells. J. Neurosci. 15 (1995) 5448–5465 | DOI

[70] L. Tao, M. Shelley, D. Mclaughlin and R. Shapley, An egalitarian network model for the emergence of simple and complex cells in visual cortex. Proc. Natl. Acad. Sci. USA 101 (2004) 366–371 | DOI

[71] J. Touboul, Bifurcation analysis of a general class of nonlinear integrate-and-fire neurons. SIAM J. Appl. Math. 68 (2008) 1045–1079 | DOI | MR | Zbl

[72] J. Touboul, Importance of the cutoff value in the quadratic adaptive integrate-and-fire model. Neural Comput. 21 (2009) 2114–2122 | DOI | MR | Zbl

[73] J. Touboul, Limits and dynamics of stochastic neuronal networks with random heterogeneous delays. J. Stat. Phys. 149 (2012) 569–597 | DOI | MR | Zbl

[74] J. Touboul, Propagation of chaos in neural fields. Ann. Appl. Probab. 24 (2014) 1298–1328 | DOI | MR | Zbl

[75] J. Touboul, Spatially extended networks with singular multi-scale connectivity patterns. J. Stat. Phys. 156 (2014) 546–573 | DOI | MR | Zbl

[76] A. Treves, Mean field analysis of neuronal spike dynamics. Network 4 (1993) 259–284 | DOI | MR | Zbl

[77] T. Troyer, A. Krukowski, N. Priebe and K. Miller, Contrast invariant orientation tuning in cat visual cortex with feedforward tuning and correlation based intracortical connectivity. J. Neurosci. 18 (1998) 5908–5927 | DOI

[78] H. Tuckwell, Introduction to Theoretical Neurobiology. Cambridge University Press, Cambridge (1988) | MR | Zbl

[79] X. Wang,Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19 (1999) 9587–9603 | DOI

[80] W. Wilbur and J. Rinzel, A theoretical basis for large coefficient of variation and bimodality in neuronal interspike interval distributions. J. Theor. Biol. 105 (1983) 345–368 | DOI

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