2016, 13(3): 613-629. doi: 10.3934/mbe.2016011

Approximation of the first passage time density of a Wiener process to an exponentially decaying boundary by two-piecewise linear threshold. Application to neuronal spiking activity

1. 

Johannes Kepler University, Altenbergerstraße 69, 4040 Linz, Austria

Received  June 2015 Revised  August 2015 Published  January 2016

The first passage time density of a diffusion process to a time varying threshold is of primary interest in different fields. Here, we consider a Brownian motion in presence of an exponentially decaying threshold to model the neuronal spiking activity. Since analytical expressions of the first passage time density are not available, we propose to approximate the curved boundary by means of a continuous two-piecewise linear threshold. Explicit expressions for the first passage time density towards the new boundary are provided. First, we introduce different approximating linear thresholds. Then, we describe how to choose the optimal one minimizing the distance to the curved boundary, and hence the error in the corresponding passage time density. Theoretical means, variances and coefficients of variation given by our method are compared with empirical quantities from simulated data. Moreover, a further comparison with firing statistics derived under the assumption of a small amplitude of the time-dependent change in the threshold, is also carried out. Finally, maximum likelihood and moment estimators of the parameters of the model are derived and applied on simulated data.
Citation: Massimiliano Tamborrino. Approximation of the first passage time density of a Wiener process to an exponentially decaying boundary by two-piecewise linear threshold. Application to neuronal spiking activity. Mathematical Biosciences & Engineering, 2016, 13 (3) : 613-629. doi: 10.3934/mbe.2016011
References:
[1]

M. Abundo, Some results about boundary crossing for Brownian motion,, Ric. Mat., 50 (2001), 283.   Google Scholar

[2]

L. Alili, P. Patie and J. Pedersen, Representation of the first hitting time density of an Ornstein-Uhlenbeck process,, Stoch. Models, 21 (2005), 967.  doi: 10.1080/15326340500294702.  Google Scholar

[3]

K. Borovkov and A. Novikov, Explicit bounds for approximation rates of boundary crossing probabilities for the Wiener process,, J. Appl. Probab., 42 (2005), 82.  doi: 10.1239/jap/1110381372.  Google Scholar

[4]

A. Buonocore, L. Caputo, E. Pirozzi and M. F. Carfora, A simple algorithm to generate firing times for leaky integrate-and-fire neuronal model,, Math. Biosci. Eng., 11 (2014), 1.   Google Scholar

[5]

A. Buonocore, A. G. Nobile and L. M. Ricciardi, A new integral equation for the evaluation of first-passage-time probability densities,, Adv. in Appl. Probab., 19 (1987), 784.  doi: 10.2307/1427102.  Google Scholar

[6]

R. M. Capocelli and L. M. Ricciardi, On the transformation of diffusion process into the Feller process,, Math. Biosci., 29 (1976), 219.  doi: 10.1016/0025-5564(76)90104-8.  Google Scholar

[7]

M. J. Chacron, A. Longtin and L. Maler, Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli,, J. Neurosci., 21 (2001), 5328.   Google Scholar

[8]

M. J. Chacron, K. Pakdaman and A. Longtin, Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire model with threshold fatigue,, Neural Comput., 15 (2003), 253.  doi: 10.1162/089976603762552915.  Google Scholar

[9]

M. J. Chacron, A. Longtin, M. St-Hilaire and L. Maler, Suprathreshold stochastic firing dynamics with memory in P-type electroreceptors,, Phys. Rev. Lett., 85 (2000), 1576.  doi: 10.1103/PhysRevLett.85.1576.  Google Scholar

[10]

R. S. Chhikara and J. L. Folks, The Inverse Gaussian Distribution: Theory, Methodology, and Applications,, Marcel Dekker, (1989).   Google Scholar

[11]

D. R. Cox and H. D. Miller, The Theory of Stochastic Processes,, CRC Press, (1977).   Google Scholar

[12]

G. L. Gerstein and B. Mandelbrot, Random walk models for the spike activity of a single neuron,, Biophys. J., 4 (1964), 41.  doi: 10.1016/S0006-3495(64)86768-0.  Google Scholar

[13]

M. T. Giraudo and L. Sacerdote, An improved technique for the simulation of first passage times for diffusion processes,, Commun. Stat. Simulat., 28 (1999), 1135.  doi: 10.1080/03610919908813596.  Google Scholar

[14]

M. T. Giraudo, L. Sacerdote and C. Zucca, A Monte Carlo method for the simulation of first passage times of diffusion processes,, Methodol. Comput. App. Probab., 3 (2001), 215.  doi: 10.1023/A:1012261328124.  Google Scholar

[15]

J. Honerkamp, Stochastic Dynamical Systems. Concepts, Numerical Methods, Data Analysis,, Wiley/VCH, (1993).   Google Scholar

[16]

R. Jolivet, A. Roth, F. Schurmann, W. Gerstner and W. Senn, Special issue on quantitative neuron modeling,, Biol. Cybern., 99 (2008), 237.  doi: 10.1007/s00422-008-0274-5.  Google Scholar

[17]

R. Kobayashi, Y. Tsubo and S. Shinomoto, Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold,, Front. Comput. Neurosci., 3 (2009), 1.  doi: 10.3389/neuro.10.009.2009.  Google Scholar

[18]

B. Lindner, Moments of the first passage time under weak external driving,, J. Stat. Phys., 117 (2004), 703.  doi: 10.1007/s10955-004-2269-5.  Google Scholar

[19]

B. Lindner and A. Longtin, Effect of an exponentially decaying threshold on the firing statistics of a stochastic integrate-and-fire neuron,, J. Theor. Biol., 232 (2005), 505.  doi: 10.1016/j.jtbi.2004.08.030.  Google Scholar

[20]

A. Metzler, On the first passage problem for correlated Brownian motion,, Stat. Probabil. Lett., 80 (2010), 277.  doi: 10.1016/j.spl.2009.11.001.  Google Scholar

[21]

A. Molini, P. Talkner, G. G. Katul and A. Porporato, First passage time statistics of Brownian motion with purely time dependent drift and diffusion,, Physica A, 390 (2011), 1841.  doi: 10.1016/j.physa.2011.01.024.  Google Scholar

[22]

A. Novikov, V. Frishling and N. Kordzakhia, Approximations of boundary crossing probabilities for a Brownian motion,, J. Appl. Probab., 36 (1999), 1019.  doi: 10.1239/jap/1032374752.  Google Scholar

[23]

K. Pötzelberger and L. Wang, Boundary crossing probability for Brownian motion,, J. Appl. Probab., 38 (2001), 152.  doi: 10.1239/jap/996986650.  Google Scholar

[24]

R Core Team, R: A Language and Environment for Statistical Computing,, R Foundation for Statistical Computing, (2014).   Google Scholar

[25]

L. M. Ricciardi, On the transformation of diffusion processes into the Wiener process,, J. Math. Anal. Appl., 54 (1976), 185.  doi: 10.1016/0022-247X(76)90244-4.  Google Scholar

[26]

L. M. Ricciardi, Diffusion Processes and Related Topics in Biology,, Lecture notes in Biomathematics, (1977).   Google Scholar

[27]

L. M. Ricciardi, A. Di Crescenzo, V. Giorno and A. Nobile, An outline of theoretical and algorithmic approaches to first passage time problems with applications to biological modeling,, Math. Japonica, 50 (1999), 247.   Google Scholar

[28]

L. Sacerdote and M. T. Giraudo, Stochastic Integrate and Fire Models: A Review on Mathematical Methods and Their Applications,, in Stochastic Biomathematical Models, (2058), 99.  doi: 10.1007/978-3-642-32157-3_5.  Google Scholar

[29]

L. Sacerdote, M. Tamborrino and C. Zucca, First passage times of two-dimensional correlated processes: Analytical results for the Wiener process and a numerical method for diffusion processes,, J. Comput. Appl. Math., 296 (2016), 275.  doi: 10.1016/j.cam.2015.09.033.  Google Scholar

[30]

L. Sacerdote, O. Telve and C. Zucca, Joint densities of first hitting times of a diffusion process through two time dependent boundaries,, Adv. Appl. Probab., 46 (2014), 186.  doi: 10.1239/aap/1396360109.  Google Scholar

[31]

T. H. Scheike, A boundary-crossing results for Brownian motion,, J. Appl. Probab., 29 (1992), 448.  doi: 10.2307/3214581.  Google Scholar

[32]

S. Shinomoto, Y. Sakai and S. Funahashi, The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex,, Neural Comput., 11 (1999), 935.  doi: 10.1162/089976699300016511.  Google Scholar

[33]

T. Taillefumier and M. O. Magnasco, A fast algorithm for the first-passage times of Gauss-Markov processes with Hölder continuous boundaries,, J. Stat. Phys., 140 (2010), 1130.  doi: 10.1007/s10955-010-0033-6.  Google Scholar

[34]

M. Tamborrino, S. Ditlevsen and P. Lansky, Parameter inference from hitting times for perturbed Brownian motion,, Lifetime Data Anal., 21 (2015), 331.  doi: 10.1007/s10985-014-9307-7.  Google Scholar

[35]

H. C. Tuckwell, Recurrent inhibition and afterhyperpolarization: Effects on neuronal discharge,, Biol. Cybernet., 30 (1978), 115.  doi: 10.1007/BF00337325.  Google Scholar

[36]

H. C. Tuckwell, Introduction to Theoretical Neurobiology, Volume 2. Nonlinear and Stochastic Theories,, Cambridge University Press, (1988).   Google Scholar

[37]

H. C. Tuckwell and F. Y. M. Wan, First passage time of Markov processes to moving barriers,, J. Appl. Probab., 21 (1984), 695.  doi: 10.2307/3213688.  Google Scholar

[38]

E. Urdapilleta, Survival probability and first-passage-time statistics of a Wiener process driven by an exponential time-dependent drift,, Phys. Rev. E, 83 (2011).  doi: 10.1103/PhysRevE.83.021102.  Google Scholar

[39]

L. Wang and K. Pötzelberger, Boundary crossing probability for Brownian motion and general boundaries,, J. App. Probab., 34 (1997), 54.  doi: 10.2307/3215174.  Google Scholar

[40]

L. Wang and K. Pötzelberger, Crossing probabilities for diffusion processes with piecewise continuous boundaries,, Methodol. Comput. Appl. Probab., 9 (2007), 21.  doi: 10.1007/s11009-006-9002-6.  Google Scholar

[41]

C. Zucca and L. Sacerdote, On the inverse first-passage-time problem for a Wiener process,, Ann. Appl. Probab., 19 (2009), 1319.  doi: 10.1214/08-AAP571.  Google Scholar

show all references

References:
[1]

M. Abundo, Some results about boundary crossing for Brownian motion,, Ric. Mat., 50 (2001), 283.   Google Scholar

[2]

L. Alili, P. Patie and J. Pedersen, Representation of the first hitting time density of an Ornstein-Uhlenbeck process,, Stoch. Models, 21 (2005), 967.  doi: 10.1080/15326340500294702.  Google Scholar

[3]

K. Borovkov and A. Novikov, Explicit bounds for approximation rates of boundary crossing probabilities for the Wiener process,, J. Appl. Probab., 42 (2005), 82.  doi: 10.1239/jap/1110381372.  Google Scholar

[4]

A. Buonocore, L. Caputo, E. Pirozzi and M. F. Carfora, A simple algorithm to generate firing times for leaky integrate-and-fire neuronal model,, Math. Biosci. Eng., 11 (2014), 1.   Google Scholar

[5]

A. Buonocore, A. G. Nobile and L. M. Ricciardi, A new integral equation for the evaluation of first-passage-time probability densities,, Adv. in Appl. Probab., 19 (1987), 784.  doi: 10.2307/1427102.  Google Scholar

[6]

R. M. Capocelli and L. M. Ricciardi, On the transformation of diffusion process into the Feller process,, Math. Biosci., 29 (1976), 219.  doi: 10.1016/0025-5564(76)90104-8.  Google Scholar

[7]

M. J. Chacron, A. Longtin and L. Maler, Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli,, J. Neurosci., 21 (2001), 5328.   Google Scholar

[8]

M. J. Chacron, K. Pakdaman and A. Longtin, Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire model with threshold fatigue,, Neural Comput., 15 (2003), 253.  doi: 10.1162/089976603762552915.  Google Scholar

[9]

M. J. Chacron, A. Longtin, M. St-Hilaire and L. Maler, Suprathreshold stochastic firing dynamics with memory in P-type electroreceptors,, Phys. Rev. Lett., 85 (2000), 1576.  doi: 10.1103/PhysRevLett.85.1576.  Google Scholar

[10]

R. S. Chhikara and J. L. Folks, The Inverse Gaussian Distribution: Theory, Methodology, and Applications,, Marcel Dekker, (1989).   Google Scholar

[11]

D. R. Cox and H. D. Miller, The Theory of Stochastic Processes,, CRC Press, (1977).   Google Scholar

[12]

G. L. Gerstein and B. Mandelbrot, Random walk models for the spike activity of a single neuron,, Biophys. J., 4 (1964), 41.  doi: 10.1016/S0006-3495(64)86768-0.  Google Scholar

[13]

M. T. Giraudo and L. Sacerdote, An improved technique for the simulation of first passage times for diffusion processes,, Commun. Stat. Simulat., 28 (1999), 1135.  doi: 10.1080/03610919908813596.  Google Scholar

[14]

M. T. Giraudo, L. Sacerdote and C. Zucca, A Monte Carlo method for the simulation of first passage times of diffusion processes,, Methodol. Comput. App. Probab., 3 (2001), 215.  doi: 10.1023/A:1012261328124.  Google Scholar

[15]

J. Honerkamp, Stochastic Dynamical Systems. Concepts, Numerical Methods, Data Analysis,, Wiley/VCH, (1993).   Google Scholar

[16]

R. Jolivet, A. Roth, F. Schurmann, W. Gerstner and W. Senn, Special issue on quantitative neuron modeling,, Biol. Cybern., 99 (2008), 237.  doi: 10.1007/s00422-008-0274-5.  Google Scholar

[17]

R. Kobayashi, Y. Tsubo and S. Shinomoto, Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold,, Front. Comput. Neurosci., 3 (2009), 1.  doi: 10.3389/neuro.10.009.2009.  Google Scholar

[18]

B. Lindner, Moments of the first passage time under weak external driving,, J. Stat. Phys., 117 (2004), 703.  doi: 10.1007/s10955-004-2269-5.  Google Scholar

[19]

B. Lindner and A. Longtin, Effect of an exponentially decaying threshold on the firing statistics of a stochastic integrate-and-fire neuron,, J. Theor. Biol., 232 (2005), 505.  doi: 10.1016/j.jtbi.2004.08.030.  Google Scholar

[20]

A. Metzler, On the first passage problem for correlated Brownian motion,, Stat. Probabil. Lett., 80 (2010), 277.  doi: 10.1016/j.spl.2009.11.001.  Google Scholar

[21]

A. Molini, P. Talkner, G. G. Katul and A. Porporato, First passage time statistics of Brownian motion with purely time dependent drift and diffusion,, Physica A, 390 (2011), 1841.  doi: 10.1016/j.physa.2011.01.024.  Google Scholar

[22]

A. Novikov, V. Frishling and N. Kordzakhia, Approximations of boundary crossing probabilities for a Brownian motion,, J. Appl. Probab., 36 (1999), 1019.  doi: 10.1239/jap/1032374752.  Google Scholar

[23]

K. Pötzelberger and L. Wang, Boundary crossing probability for Brownian motion,, J. Appl. Probab., 38 (2001), 152.  doi: 10.1239/jap/996986650.  Google Scholar

[24]

R Core Team, R: A Language and Environment for Statistical Computing,, R Foundation for Statistical Computing, (2014).   Google Scholar

[25]

L. M. Ricciardi, On the transformation of diffusion processes into the Wiener process,, J. Math. Anal. Appl., 54 (1976), 185.  doi: 10.1016/0022-247X(76)90244-4.  Google Scholar

[26]

L. M. Ricciardi, Diffusion Processes and Related Topics in Biology,, Lecture notes in Biomathematics, (1977).   Google Scholar

[27]

L. M. Ricciardi, A. Di Crescenzo, V. Giorno and A. Nobile, An outline of theoretical and algorithmic approaches to first passage time problems with applications to biological modeling,, Math. Japonica, 50 (1999), 247.   Google Scholar

[28]

L. Sacerdote and M. T. Giraudo, Stochastic Integrate and Fire Models: A Review on Mathematical Methods and Their Applications,, in Stochastic Biomathematical Models, (2058), 99.  doi: 10.1007/978-3-642-32157-3_5.  Google Scholar

[29]

L. Sacerdote, M. Tamborrino and C. Zucca, First passage times of two-dimensional correlated processes: Analytical results for the Wiener process and a numerical method for diffusion processes,, J. Comput. Appl. Math., 296 (2016), 275.  doi: 10.1016/j.cam.2015.09.033.  Google Scholar

[30]

L. Sacerdote, O. Telve and C. Zucca, Joint densities of first hitting times of a diffusion process through two time dependent boundaries,, Adv. Appl. Probab., 46 (2014), 186.  doi: 10.1239/aap/1396360109.  Google Scholar

[31]

T. H. Scheike, A boundary-crossing results for Brownian motion,, J. Appl. Probab., 29 (1992), 448.  doi: 10.2307/3214581.  Google Scholar

[32]

S. Shinomoto, Y. Sakai and S. Funahashi, The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex,, Neural Comput., 11 (1999), 935.  doi: 10.1162/089976699300016511.  Google Scholar

[33]

T. Taillefumier and M. O. Magnasco, A fast algorithm for the first-passage times of Gauss-Markov processes with Hölder continuous boundaries,, J. Stat. Phys., 140 (2010), 1130.  doi: 10.1007/s10955-010-0033-6.  Google Scholar

[34]

M. Tamborrino, S. Ditlevsen and P. Lansky, Parameter inference from hitting times for perturbed Brownian motion,, Lifetime Data Anal., 21 (2015), 331.  doi: 10.1007/s10985-014-9307-7.  Google Scholar

[35]

H. C. Tuckwell, Recurrent inhibition and afterhyperpolarization: Effects on neuronal discharge,, Biol. Cybernet., 30 (1978), 115.  doi: 10.1007/BF00337325.  Google Scholar

[36]

H. C. Tuckwell, Introduction to Theoretical Neurobiology, Volume 2. Nonlinear and Stochastic Theories,, Cambridge University Press, (1988).   Google Scholar

[37]

H. C. Tuckwell and F. Y. M. Wan, First passage time of Markov processes to moving barriers,, J. Appl. Probab., 21 (1984), 695.  doi: 10.2307/3213688.  Google Scholar

[38]

E. Urdapilleta, Survival probability and first-passage-time statistics of a Wiener process driven by an exponential time-dependent drift,, Phys. Rev. E, 83 (2011).  doi: 10.1103/PhysRevE.83.021102.  Google Scholar

[39]

L. Wang and K. Pötzelberger, Boundary crossing probability for Brownian motion and general boundaries,, J. App. Probab., 34 (1997), 54.  doi: 10.2307/3215174.  Google Scholar

[40]

L. Wang and K. Pötzelberger, Crossing probabilities for diffusion processes with piecewise continuous boundaries,, Methodol. Comput. Appl. Probab., 9 (2007), 21.  doi: 10.1007/s11009-006-9002-6.  Google Scholar

[41]

C. Zucca and L. Sacerdote, On the inverse first-passage-time problem for a Wiener process,, Ann. Appl. Probab., 19 (2009), 1319.  doi: 10.1214/08-AAP571.  Google Scholar

[1]

Lin Zhao, Zhi-Cheng Wang, Liang Zhang. Threshold dynamics of a time periodic and two–group epidemic model with distributed delay. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1535-1563. doi: 10.3934/mbe.2017080

[2]

Hui Meng, Fei Lung Yuen, Tak Kuen Siu, Hailiang Yang. Optimal portfolio in a continuous-time self-exciting threshold model. Journal of Industrial & Management Optimization, 2013, 9 (2) : 487-504. doi: 10.3934/jimo.2013.9.487

[3]

Yijun Lou, Xiao-Qiang Zhao. Threshold dynamics in a time-delayed periodic SIS epidemic model. Discrete & Continuous Dynamical Systems - B, 2009, 12 (1) : 169-186. doi: 10.3934/dcdsb.2009.12.169

[4]

Oleksandr Misiats, Nung Kwan Yip. Convergence of space-time discrete threshold dynamics to anisotropic motion by mean curvature. Discrete & Continuous Dynamical Systems - A, 2016, 36 (11) : 6379-6411. doi: 10.3934/dcds.2016076

[5]

Yung Chung Wang, Jenn Shing Wang, Fu Hsiang Tsai. Analysis of discrete-time space priority queue with fuzzy threshold. Journal of Industrial & Management Optimization, 2009, 5 (3) : 467-479. doi: 10.3934/jimo.2009.5.467

[6]

Roberta Fabbri, Russell Johnson, Sylvia Novo, Carmen Núñez. On linear-quadratic dissipative control processes with time-varying coefficients. Discrete & Continuous Dynamical Systems - A, 2013, 33 (1) : 193-210. doi: 10.3934/dcds.2013.33.193

[7]

Serge Nicaise, Julie Valein, Emilia Fridman. Stability of the heat and of the wave equations with boundary time-varying delays. Discrete & Continuous Dynamical Systems - S, 2009, 2 (3) : 559-581. doi: 10.3934/dcdss.2009.2.559

[8]

Serge Nicaise, Cristina Pignotti, Julie Valein. Exponential stability of the wave equation with boundary time-varying delay. Discrete & Continuous Dynamical Systems - S, 2011, 4 (3) : 693-722. doi: 10.3934/dcdss.2011.4.693

[9]

Yanan Zhao, Daqing Jiang, Xuerong Mao, Alison Gray. The threshold of a stochastic SIRS epidemic model in a population with varying size. Discrete & Continuous Dynamical Systems - B, 2015, 20 (4) : 1277-1295. doi: 10.3934/dcdsb.2015.20.1277

[10]

Shu Zhang, Jian Xu. Time-varying delayed feedback control for an internet congestion control model. Discrete & Continuous Dynamical Systems - B, 2011, 16 (2) : 653-668. doi: 10.3934/dcdsb.2011.16.653

[11]

Robert G. McLeod, John F. Brewster, Abba B. Gumel, Dean A. Slonowsky. Sensitivity and uncertainty analyses for a SARS model with time-varying inputs and outputs. Mathematical Biosciences & Engineering, 2006, 3 (3) : 527-544. doi: 10.3934/mbe.2006.3.527

[12]

Ruoxia Li, Huaiqin Wu, Xiaowei Zhang, Rong Yao. Adaptive projective synchronization of memristive neural networks with time-varying delays and stochastic perturbation. Mathematical Control & Related Fields, 2015, 5 (4) : 827-844. doi: 10.3934/mcrf.2015.5.827

[13]

Hongbiao Fan, Jun-E Feng, Min Meng. Piecewise observers of rectangular discrete fuzzy descriptor systems with multiple time-varying delays. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1535-1556. doi: 10.3934/jimo.2016.12.1535

[14]

Xinli Hu. Threshold dynamics for a Tuberculosis model with seasonality. Mathematical Biosciences & Engineering, 2012, 9 (1) : 111-122. doi: 10.3934/mbe.2012.9.111

[15]

Lijuan Wang, Yashan Xu. Admissible controls and controllable sets for a linear time-varying ordinary differential equation. Mathematical Control & Related Fields, 2018, 8 (3&4) : 1001-1019. doi: 10.3934/mcrf.2018043

[16]

K. Aruna Sakthi, A. Vinodkumar. Stabilization on input time-varying delay for linear switched systems with truncated predictor control. Numerical Algebra, Control & Optimization, 2019, 0 (0) : 0-0. doi: 10.3934/naco.2019050

[17]

Sin-Man Choi, Ximin Huang, Wai-Ki Ching. Minimizing equilibrium expected sojourn time via performance-based mixed threshold demand allocation in a multiple-server queueing environment. Journal of Industrial & Management Optimization, 2012, 8 (2) : 299-323. doi: 10.3934/jimo.2012.8.299

[18]

Mohammad-Sahadet Hossain. Projection-based model reduction for time-varying descriptor systems: New results. Numerical Algebra, Control & Optimization, 2016, 6 (1) : 73-90. doi: 10.3934/naco.2016.6.73

[19]

Zhen Zhang, Jianhua Huang, Xueke Pu. Pullback attractors of FitzHugh-Nagumo system on the time-varying domains. Discrete & Continuous Dynamical Systems - B, 2017, 22 (10) : 3691-3706. doi: 10.3934/dcdsb.2017150

[20]

Yangzi Hu, Fuke Wu. The improved results on the stochastic Kolmogorov system with time-varying delay. Discrete & Continuous Dynamical Systems - B, 2015, 20 (5) : 1481-1497. doi: 10.3934/dcdsb.2015.20.1481

2018 Impact Factor: 1.313

Metrics

  • PDF downloads (11)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]