American Institute of Mathematical Sciences

Advanced
2016, 13(3): 495-507. doi: 10.3934/mbe.2016003

Successive spike times predicted by a stochastic neuronal model with a variable input signal

Giuseppe D'Onofrio 1, and  Enrica Pirozzi 2,

1. 

Dipartimento di Matematica e Applicazioni, Università degli studi di Napoli, FEDERICO II, Via Cinthia, Monte S.Angelo, Napoli, 80126, Italy
2. 

Dipartimento di Matematica e Applicazioni “R. Caccioppoli”, Università di Napoli Federico II, Via Cintia, 80126 Napoli

Received  April 2015 Revised  November 2015 Published  January 2016

Two different stochastic processes are used to model the evolution of the membrane voltage of a neuron exposed to a time-varying input signal. The first process is an inhomogeneous Ornstein-Uhlenbeck process and its first passage time through a constant threshold is used to model the first spike time after the signal onset. The second process is a Gauss-Markov process identified by a particular mean function dependent on the first passage time of the first process. It is shown that the second process is also of a diffusion type. The probability density function of the maximum between the first passage time of the first and the second process is considered to approximate the distribution of the second spike time. Results obtained by simulations are compared with those following the numerical and asymptotic approximations. A general equation to model successive spike times is given. Finally, examples with specific input signals are provided.
Keywords: LIF neuronal model, Gauss-Markov processes, first passage time..
Mathematics Subject Classification: Primary: 60G20, 60J70; Secondary: 65C3.
Citation: Giuseppe D'Onofrio, Enrica Pirozzi. Successive spike times predicted by a stochastic neuronal model with a variable input signal. Mathematical Biosciences & Engineering, 2016, 13 (3) : 495-507. doi: 10.3934/mbe.2016003
References:
[1]

A. N. Burkitt, A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input,, Biological Cybernetics, 95 (2006), 1. doi: 10.1007/s00422-006-0068-6. Google Scholar

[2]

A. Buonocore, L. Caputo, E. Pirozzi and L. M. Ricciardi, The first passage time problem for Gauss-diffusion processes: Algorithmic approaches and applications to LIF neuronal model,, Methodol. Comput. Appl. Prob., 13 (2011), 29. doi: 10.1007/s11009-009-9132-8. Google Scholar

[3]

A. Buonocore, L. Caputo, E. Pirozzi and L. M. Ricciardi, On a stochastic leaky integrate-and-fire neuronal model,, Neural Computation, 22 (2010), 2558. doi: 10.1162/NECO_a_00023. Google Scholar

[4]

A. Buonocore, L. Caputo, E. Pirozzi and M. F. Carfora, Gauss-diffusion processes for modeling the dynamics of a couple of interacting neurons,, Math. Biosci. Eng., 11 (2014), 189. Google Scholar

[5]

A. Buonocore, L. Caputo, A. G. Nobile and E. Pirozzi, Gauss-Markov processes in the presence of a reflecting boundary and applications in neuronal models,, Applied Mathematics and Computation, 232 (2014), 799. doi: 10.1016/j.amc.2014.01.143. Google Scholar

[6]

A. Buonocore, L. Caputo, A. G. Nobile and E. Pirozzi, Restricted Ornstein-Uhlenbeck process and applications in neuronal models with periodic input signals,, Journal of Computational and Applied Mathematics, 285 (2015), 59. doi: 10.1016/j.cam.2015.01.042. Google Scholar

[7]

A. Buonocore, L. Caputo, A. G. Nobile and E. Pirozzi, Gauss-markov processes for neuronal models including reversal potentials,, Advances in Cognitive Neurodynamics (IV), 11 (2015), 299. doi: 10.1007/978-94-017-9548-7_42. Google Scholar

[8]

M. J. Chacron, K. Pakdaman and A. Longtin, Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire neuron with threshold fatigue., Neural Computation, 15 (2003), 253. Google Scholar

[9]

E. Di Nardo, A. G. Nobile, E. Pirozzi and L. M. Ricciardi, A computational approach to first passage-time problems for Gauss-Markov processes,, Adv. Appl. Prob., 33 (2001), 453. doi: 10.1239/aap/999188324. Google Scholar

[10]

J. M. Fellous, P. H. Tiesinga, P. J. Thomas and T. J. Sejnowski, Discovering spike patterns in neuronal responses,, The Journal of Neuroscience, 24 (2004), 2989. doi: 10.1523/JNEUROSCI.4649-03.2004. Google Scholar

[11]

V. Giorno and S. Spina, On the return process with refractoriness for a non-homogeneous Ornstein-Uhlenbeck neuronal model,, Math. Bios. Eng., 11 (2014), 285. Google Scholar

[12]

H. Kim and S. Shinomoto, Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation,, Math. Bios. Eng., 11 (2014), 49. Google Scholar

[13]

P. Lánský and S. Ditlevsen, A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models,, Biol. Cybern., 99 (2008), 253. doi: 10.1007/s00422-008-0237-x. Google Scholar

[14]

P. Lánský, Sources of periodical force in noisy integrate-and-fire models of neuronal dynamics,, Physical Review E, 55 (1997), 2040. Google Scholar

[15]

B. Lindner, Interspike interval statistics for neurons driven by colored noise,, Physical Review E, 69 (2004), 022901. doi: 10.1103/PhysRevE.69.022901. Google Scholar

[16]

L. M. Ricciardi and L. Sacerdote, The Ornstein-Uhlenbeck process as a model for neuronal activity,, Biological Cybernetics, 35 (1979), 1. doi: 10.1007/BF01845839. Google Scholar

[17]

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

[18]

T. Schwalger, F. Droste and B. Lindner, Statistical structure of neural spiking under non-Poissonian or other non-white stimulation,, Journal of Computational Neuroscience, 39 (2015), 29. doi: 10.1007/s10827-015-0560-x. Google Scholar

[19]

M. Shaked and J. G. Shanthikumar, Stochastic Orders and Their Applications,, Academic Press, (1994). Google Scholar

[20]

S. Shinomoto, Y. Sakai and S. Funahashi, The Ornstein-Uhlenbeck process does not reproduce spiking statistics of cortical neurons,, Neural Computation, 11 (1997), 935. Google Scholar

[21]

T. Taillefumier and M. 0. Magnasco, A phase transition in the first passage of a Brownian process through a fluctuating boundary: Implications for neural coding,, PNAS, 110 (2013). doi: 10.1073/pnas.1212479110. Google Scholar

[22]

T. Taillefumier and M. Magnasco, A transition to sharp timing in stochastic leaky integrate-and-fire neurons driven by frozen noisy input,, Neural Computation, 26 (2014), 819. doi: 10.1162/NECO_a_00577. Google Scholar

[23]

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

[24]

P. J. Thomas, A lower bound for the first passage time density of the suprathreshold Ornstein-Uhlenbeck process,, J. Appl. Probab., 48 (2011), 420. doi: 10.1239/jap/1308662636. Google Scholar

[25]

J. V. Toups, J. M. Fellous, P. J. Thomas, T. J. Sejnowski and P. H. Tiesinga, Multiple spike time patterns occur at bifurcation points of membrane potential dynamics,, PLoS Comput. Biol., 8 (2012), 1. doi: 10.1371/journal.pcbi.1002615. Google Scholar

[26]

H. C. Tuckwell, Stochastic Processes in the Neurosciences,, SIAM, (1989). doi: 10.1137/1.9781611970159. Google Scholar

[27]

E. Urdapilleta, Series solution to the first-passage-time problem of a Brownian motion with an exponential time-dependent drift,, J. Stat. Phys., 140 (2010), 1130. doi: 10.1088/1751-8113/45/18/185001. Google Scholar

show all references

References:
[1]

A. N. Burkitt, A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input,, Biological Cybernetics, 95 (2006), 1. doi: 10.1007/s00422-006-0068-6. Google Scholar

[2]

A. Buonocore, L. Caputo, E. Pirozzi and L. M. Ricciardi, The first passage time problem for Gauss-diffusion processes: Algorithmic approaches and applications to LIF neuronal model,, Methodol. Comput. Appl. Prob., 13 (2011), 29. doi: 10.1007/s11009-009-9132-8. Google Scholar

[3]

A. Buonocore, L. Caputo, E. Pirozzi and L. M. Ricciardi, On a stochastic leaky integrate-and-fire neuronal model,, Neural Computation, 22 (2010), 2558. doi: 10.1162/NECO_a_00023. Google Scholar

[4]

A. Buonocore, L. Caputo, E. Pirozzi and M. F. Carfora, Gauss-diffusion processes for modeling the dynamics of a couple of interacting neurons,, Math. Biosci. Eng., 11 (2014), 189. Google Scholar

[5]

A. Buonocore, L. Caputo, A. G. Nobile and E. Pirozzi, Gauss-Markov processes in the presence of a reflecting boundary and applications in neuronal models,, Applied Mathematics and Computation, 232 (2014), 799. doi: 10.1016/j.amc.2014.01.143. Google Scholar

[6]

A. Buonocore, L. Caputo, A. G. Nobile and E. Pirozzi, Restricted Ornstein-Uhlenbeck process and applications in neuronal models with periodic input signals,, Journal of Computational and Applied Mathematics, 285 (2015), 59. doi: 10.1016/j.cam.2015.01.042. Google Scholar

[7]

A. Buonocore, L. Caputo, A. G. Nobile and E. Pirozzi, Gauss-markov processes for neuronal models including reversal potentials,, Advances in Cognitive Neurodynamics (IV), 11 (2015), 299. doi: 10.1007/978-94-017-9548-7_42. Google Scholar

[8]

M. J. Chacron, K. Pakdaman and A. Longtin, Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire neuron with threshold fatigue., Neural Computation, 15 (2003), 253. Google Scholar

[9]

E. Di Nardo, A. G. Nobile, E. Pirozzi and L. M. Ricciardi, A computational approach to first passage-time problems for Gauss-Markov processes,, Adv. Appl. Prob., 33 (2001), 453. doi: 10.1239/aap/999188324. Google Scholar

[10]

J. M. Fellous, P. H. Tiesinga, P. J. Thomas and T. J. Sejnowski, Discovering spike patterns in neuronal responses,, The Journal of Neuroscience, 24 (2004), 2989. doi: 10.1523/JNEUROSCI.4649-03.2004. Google Scholar

[11]

V. Giorno and S. Spina, On the return process with refractoriness for a non-homogeneous Ornstein-Uhlenbeck neuronal model,, Math. Bios. Eng., 11 (2014), 285. Google Scholar

[12]

H. Kim and S. Shinomoto, Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation,, Math. Bios. Eng., 11 (2014), 49. Google Scholar

[13]

P. Lánský and S. Ditlevsen, A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models,, Biol. Cybern., 99 (2008), 253. doi: 10.1007/s00422-008-0237-x. Google Scholar

[14]

P. Lánský, Sources of periodical force in noisy integrate-and-fire models of neuronal dynamics,, Physical Review E, 55 (1997), 2040. Google Scholar

[15]

B. Lindner, Interspike interval statistics for neurons driven by colored noise,, Physical Review E, 69 (2004), 022901. doi: 10.1103/PhysRevE.69.022901. Google Scholar

[16]

L. M. Ricciardi and L. Sacerdote, The Ornstein-Uhlenbeck process as a model for neuronal activity,, Biological Cybernetics, 35 (1979), 1. doi: 10.1007/BF01845839. Google Scholar

[17]

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

[18]

T. Schwalger, F. Droste and B. Lindner, Statistical structure of neural spiking under non-Poissonian or other non-white stimulation,, Journal of Computational Neuroscience, 39 (2015), 29. doi: 10.1007/s10827-015-0560-x. Google Scholar

[19]

M. Shaked and J. G. Shanthikumar, Stochastic Orders and Their Applications,, Academic Press, (1994). Google Scholar

[20]

S. Shinomoto, Y. Sakai and S. Funahashi, The Ornstein-Uhlenbeck process does not reproduce spiking statistics of cortical neurons,, Neural Computation, 11 (1997), 935. Google Scholar

[21]

T. Taillefumier and M. 0. Magnasco, A phase transition in the first passage of a Brownian process through a fluctuating boundary: Implications for neural coding,, PNAS, 110 (2013). doi: 10.1073/pnas.1212479110. Google Scholar

[22]

T. Taillefumier and M. Magnasco, A transition to sharp timing in stochastic leaky integrate-and-fire neurons driven by frozen noisy input,, Neural Computation, 26 (2014), 819. doi: 10.1162/NECO_a_00577. Google Scholar

[23]

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

[24]

P. J. Thomas, A lower bound for the first passage time density of the suprathreshold Ornstein-Uhlenbeck process,, J. Appl. Probab., 48 (2011), 420. doi: 10.1239/jap/1308662636. Google Scholar

[25]

J. V. Toups, J. M. Fellous, P. J. Thomas, T. J. Sejnowski and P. H. Tiesinga, Multiple spike time patterns occur at bifurcation points of membrane potential dynamics,, PLoS Comput. Biol., 8 (2012), 1. doi: 10.1371/journal.pcbi.1002615. Google Scholar

[26]

H. C. Tuckwell, Stochastic Processes in the Neurosciences,, SIAM, (1989). doi: 10.1137/1.9781611970159. Google Scholar

[27]

E. Urdapilleta, Series solution to the first-passage-time problem of a Brownian motion with an exponential time-dependent drift,, J. Stat. Phys., 140 (2010), 1130. doi: 10.1088/1751-8113/45/18/185001. Google Scholar

[1]

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
[2]

Qiuying Li, Lifang Huang, Jianshe Yu. Modulation of first-passage time for bursty gene expression via random signals. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1261-1277. doi: 10.3934/mbe.2017065
[3]

Vincent Renault, Michèle Thieullen, Emmanuel Trélat. Optimal control of infinite-dimensional piecewise deterministic Markov processes and application to the control of neuronal dynamics via Optogenetics. Networks & Heterogeneous Media, 2017, 12 (3) : 417-459. doi: 10.3934/nhm.2017019
[4]

Vladimir Kazakov. Sampling - reconstruction procedure with jitter of markov continuous processes formed by stochastic differential equations of the first order. Conference Publications, 2009, 2009 (Special) : 433-441. doi: 10.3934/proc.2009.2009.433
[5]

Karoline Disser, Matthias Liero. On gradient structures for Markov chains and the passage to Wasserstein gradient flows. Networks & Heterogeneous Media, 2015, 10 (2) : 233-253. doi: 10.3934/nhm.2015.10.233
[6]

Wael Bahsoun, Paweł Góra. SRB measures for certain Markov processes. Discrete & Continuous Dynamical Systems - A, 2011, 30 (1) : 17-37. doi: 10.3934/dcds.2011.30.17
[7]

Mathias Staudigl. A limit theorem for Markov decision processes. Journal of Dynamics & Games, 2014, 1 (4) : 639-659. doi: 10.3934/jdg.2014.1.639
[8]

Artur Stephan, Holger Stephan. Memory equations as reduced Markov processes. Discrete & Continuous Dynamical Systems - A, 2019, 39 (4) : 2133-2155. doi: 10.3934/dcds.2019089
[9]

Linyi Qian, Wei Wang, Rongming Wang. Risk-minimizing portfolio selection for insurance payment processes under a Markov-modulated model. Journal of Industrial & Management Optimization, 2013, 9 (2) : 411-429. doi: 10.3934/jimo.2013.9.411
[10]

Zhenzhong Zhang, Enhua Zhang, Jinying Tong. Necessary and sufficient conditions for ergodicity of CIR model driven by stable processes with Markov switching. Discrete & Continuous Dynamical Systems - B, 2018, 23 (6) : 2433-2455. doi: 10.3934/dcdsb.2018053
[11]

Aniello Buonocore, Luigia Caputo, Enrica Pirozzi, Maria Francesca Carfora. Gauss-diffusion processes for modeling the dynamics of a couple of interacting neurons. Mathematical Biosciences & Engineering, 2014, 11 (2) : 189-201. doi: 10.3934/mbe.2014.11.189
[12]

H.Thomas Banks, Shuhua Hu. Nonlinear stochastic Markov processes and modeling uncertainty in populations. Mathematical Biosciences & Engineering, 2012, 9 (1) : 1-25. doi: 10.3934/mbe.2012.9.1
[13]

Xian Chen, Zhi-Ming Ma. A transformation of Markov jump processes and applications in genetic study. Discrete & Continuous Dynamical Systems - A, 2014, 34 (12) : 5061-5084. doi: 10.3934/dcds.2014.34.5061
[14]

A. M. Vershik. Polymorphisms, Markov processes, quasi-similarity. Discrete & Continuous Dynamical Systems - A, 2005, 13 (5) : 1305-1324. doi: 10.3934/dcds.2005.13.1305
[15]

Lakhdar Aggoun, Lakdere Benkherouf. A Markov modulated continuous-time capture-recapture population estimation model. Discrete & Continuous Dynamical Systems - B, 2005, 5 (4) : 1057-1075. doi: 10.3934/dcdsb.2005.5.1057
[16]

Hyukjin Lee, Cheng-Chew Lim, Jinho Choi. Joint backoff control in time and frequency for multichannel wireless systems and its Markov model for analysis. Discrete & Continuous Dynamical Systems - B, 2011, 16 (4) : 1083-1099. doi: 10.3934/dcdsb.2011.16.1083
[17]

S. Mohamad, K. Gopalsamy. Neuronal dynamics in time varying enviroments: Continuous and discrete time models. Discrete & Continuous Dynamical Systems - A, 2000, 6 (4) : 841-860. doi: 10.3934/dcds.2000.6.841
[18]

A. Mittal, N. Hemachandra. Learning algorithms for finite horizon constrained Markov decision processes. Journal of Industrial & Management Optimization, 2007, 3 (3) : 429-444. doi: 10.3934/jimo.2007.3.429
[19]

Lixia Duan, Zhuoqin Yang, Shenquan Liu, Dunwei Gong. Bursting and two-parameter bifurcation in the Chay neuronal model. Discrete & Continuous Dynamical Systems - B, 2011, 16 (2) : 445-456. doi: 10.3934/dcdsb.2011.16.445
[20]

Marie Levakova. Effect of spontaneous activity on stimulus detection in a simple neuronal model. Mathematical Biosciences & Engineering, 2016, 13 (3) : 551-568. doi: 10.3934/mbe.2016007

2018 Impact Factor: 1.313

Tools

Metrics

  • PDF downloads (8)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2019 American Institute of Mathematical Sciences