American Institute of Mathematical Sciences

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2014, 11(2): 189-201. doi: 10.3934/mbe.2014.11.189

Gauss-diffusion processes for modeling the dynamics of a couple of interacting neurons

Aniello Buonocore 1, , Luigia Caputo 1, , Enrica Pirozzi 1, and  Maria Francesca Carfora 2,

1. 

Dipartimento di Matematica e Applicazioni “R. Caccioppoli”, Università di Napoli Federico II, Via Cintia, 80126 Napoli
2. 

Istituto per le Appplicazioni del Calcolo "Mauro Picone", Consiglio Nazionale delle Ricerche, Via Pietro Castellino, Napoli

Received  October 2012 Revised  April 2013 Published  October 2013

With the aim to describe the interaction between a couple of neurons a stochastic model is proposed and formalized. In such a model, maintaining statements of the Leaky Integrate-and-Fire framework, we include a random component in the synaptic current, whose role is to modify the equilibrium point of the membrane potential of one of the two neurons and when a spike of the other one occurs it is turned on. The initial and after spike reset positions do not allow to identify the inter-spike intervals with the corresponding first passage times. However, we are able to apply some well-known results for the first passage time problem for the Ornstein-Uhlenbeck process in order to obtain (i) an approximation of the probability density function of the inter-spike intervals in one-way-type interaction and (ii) an approximation of the tail of the probability density function of the inter-spike intervals in the mutual interaction. Such an approximation is admissible for small instantaneous firing rates of both neurons.
Keywords: Leaky integrate-and-fire model, stochastic connection, synaptic current, asymptotic behavior., first passage time.
Mathematics Subject Classification: Primary: 60J60, 60J70; Secondary: 92-0.
Citation: 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
References:
[1]

K. Amemori and S. Ishii, Gaussian process approach to spiking neurons for inhomogeneous Poisson inputs, Neural Comp., 13 (2001), 2763-2797. doi: 10.1162/089976601317098529.  Google Scholar

[2]

A. Buonocore, A. G. Nobile and L. M. Ricciardi, A new integral equation for evaluation of first-passage-time probability densities, Advances in Applied Probability, 19 (1987), 784-990. doi: 10.2307/1427102.  Google Scholar

[3]

A. Buonocore, L. Caputo, E. Pirozzi and L. M. Ricciardi, On a stochastic leaky integrate-and-fire neuronal model, Neural Comput., 22 (2010), 2558-2585. doi: 10.1162/NECO_a_00023.  Google Scholar

[4]

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. Probab., 13 (2011), 29-57. doi: 10.1007/s11009-009-9132-8.  Google Scholar

[5]

A. N. Burkitt, A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input, Biol. Cybern., 95 (2006), 1-19. doi: 10.1007/s00422-006-0068-6.  Google Scholar

[6]

A. Di Crescenzo, B. Martinucci and E. Pirozzi, Feedback effects in simulated Stein's coupled neurons, in Computer Aided Systems Theory – EUROCAST 2005, Lecture Notes in Computer Science, 3643, Springer, Berlin-Heidelberg, 2005, 436-446. doi: 10.1007/11556985_57.  Google Scholar

[7]

A. Di Crescenzo, B. Martinucci and E. Pirozzi, On the dynamics of a pair of coupled neurons subject to alternating input rates, BioSystems, 79 (2005), 109-116. doi: 10.1016/j.biosystems.2004.09.020.  Google Scholar

[8]

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), 453-482. doi: 10.1239/aap/999188324.  Google Scholar

[9]

Y. Dong, F. Mihalas and E. Niebur, Improved integral equation solution for the first passage time of leaky integrate-and-fire neurons, Neural Computation, 23 (2011), 421-434. doi: 10.1162/NECO_a_00078.  Google Scholar

[10]

V. Giorno, A. G. Nobile and L. M. Ricciardi, On the asymptotic behaviour of first-passage-time densities for one-dimensional diffusion processes and varying boundaries, Adv. Appl. Prob., 22 (1990), 883-914. doi: 10.2307/1427567.  Google Scholar

[11]

D. Golomb and G. B. Ermentrout, Bistability in pulse propagation in networks of excitatory and inhibitory populations, Phys. Rev. Lett., 68 (2001), 4179-4182. doi: 10.1103/PhysRevLett.86.4179.  Google Scholar

[12]

A. G. Nobile, E. Pirozzi and L. M. Ricciardi, On the estimation of first-passage time densities for a class of Gauss-Markov processes, in Computer Aided Systems Theory – EUROCAST 2007, Lecture Notes in Computer Science, 4739, Springer, Berlin-Heidelberg, 2007, 146-153. doi: 10.1007/978-3-540-75867-9_19.  Google Scholar

[13]

A. G. Nobile, E. Pirozzi and L. M. Ricciardi., Asymptotics and evaluations of fpt densities through varying boundaries for Gauss-Markov processes, Scientiae Mathematicae Japonicae, 67 (2008), 241-266. Available from: http://www.jams.or.jp/scm/contents/e-2008-2/2008-12.pdf.  Google Scholar

[14]

A. Politi and S. Luccioli, Dynamics of networks of leaky-integrate-and-fire neurons, in Network Science, Springer, London, 2010, 217-242. doi: 10.1007/978-1-84996-396-1_11.  Google Scholar

[15]

L. Sacerdote, M. Tamborrino and C. Zucca, Detecting dependencies between spike trains of pairs of neurons through copulas, Brain Research, 1434 (2012), 243-256. doi: 10.1016/j.brainres.2011.08.064.  Google Scholar

[16]

H. Sakaguchi, Oscillatory phase transition and pulse propagation in noisy integrate-and-fire neurons, Phys. Rev. E., 70 (2004), 1-4. doi: 10.1103/PhysRevE.70.022901.  Google Scholar

[17]

H. Sakaguchi and S. Tobiishi, Synchronization and spindle oscillation in noisy integrate-and-fire-or-burst neurons with inhibitory coupling, Progress of Theoretical Physics, 114 (2005), 1-18. doi: 10.1143/PTP.114.539.  Google Scholar

[18]

R. Sirovich, L. Sacerdote and A. E. P. Villa, Effect of increasing inhibitory inputs on information processing within a small network of spiking neurons, in Computational and Ambient Intelligence, Lecture Notes in Computer Science, 4507, Springer, Berlin-Heidelberg, 2007, 23-30. doi: 10.1007/978-3-540-73007-1_4.  Google Scholar

[19]

H. Soula and C. C. Chow, Stochastic dynamics of a finite-size spiking neural network, Neural Comput., 19 (2007), 3262-3292. doi: 10.1162/neco.2007.19.12.3262.  Google Scholar

show all references

References:
[1]

K. Amemori and S. Ishii, Gaussian process approach to spiking neurons for inhomogeneous Poisson inputs, Neural Comp., 13 (2001), 2763-2797. doi: 10.1162/089976601317098529.  Google Scholar

[2]

A. Buonocore, A. G. Nobile and L. M. Ricciardi, A new integral equation for evaluation of first-passage-time probability densities, Advances in Applied Probability, 19 (1987), 784-990. doi: 10.2307/1427102.  Google Scholar

[3]

A. Buonocore, L. Caputo, E. Pirozzi and L. M. Ricciardi, On a stochastic leaky integrate-and-fire neuronal model, Neural Comput., 22 (2010), 2558-2585. doi: 10.1162/NECO_a_00023.  Google Scholar

[4]

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. Probab., 13 (2011), 29-57. doi: 10.1007/s11009-009-9132-8.  Google Scholar

[5]

A. N. Burkitt, A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input, Biol. Cybern., 95 (2006), 1-19. doi: 10.1007/s00422-006-0068-6.  Google Scholar

[6]

A. Di Crescenzo, B. Martinucci and E. Pirozzi, Feedback effects in simulated Stein's coupled neurons, in Computer Aided Systems Theory – EUROCAST 2005, Lecture Notes in Computer Science, 3643, Springer, Berlin-Heidelberg, 2005, 436-446. doi: 10.1007/11556985_57.  Google Scholar

[7]

A. Di Crescenzo, B. Martinucci and E. Pirozzi, On the dynamics of a pair of coupled neurons subject to alternating input rates, BioSystems, 79 (2005), 109-116. doi: 10.1016/j.biosystems.2004.09.020.  Google Scholar

[8]

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), 453-482. doi: 10.1239/aap/999188324.  Google Scholar

[9]

Y. Dong, F. Mihalas and E. Niebur, Improved integral equation solution for the first passage time of leaky integrate-and-fire neurons, Neural Computation, 23 (2011), 421-434. doi: 10.1162/NECO_a_00078.  Google Scholar

[10]

V. Giorno, A. G. Nobile and L. M. Ricciardi, On the asymptotic behaviour of first-passage-time densities for one-dimensional diffusion processes and varying boundaries, Adv. Appl. Prob., 22 (1990), 883-914. doi: 10.2307/1427567.  Google Scholar

[11]

D. Golomb and G. B. Ermentrout, Bistability in pulse propagation in networks of excitatory and inhibitory populations, Phys. Rev. Lett., 68 (2001), 4179-4182. doi: 10.1103/PhysRevLett.86.4179.  Google Scholar

[12]

A. G. Nobile, E. Pirozzi and L. M. Ricciardi, On the estimation of first-passage time densities for a class of Gauss-Markov processes, in Computer Aided Systems Theory – EUROCAST 2007, Lecture Notes in Computer Science, 4739, Springer, Berlin-Heidelberg, 2007, 146-153. doi: 10.1007/978-3-540-75867-9_19.  Google Scholar

[13]

A. G. Nobile, E. Pirozzi and L. M. Ricciardi., Asymptotics and evaluations of fpt densities through varying boundaries for Gauss-Markov processes, Scientiae Mathematicae Japonicae, 67 (2008), 241-266. Available from: http://www.jams.or.jp/scm/contents/e-2008-2/2008-12.pdf.  Google Scholar

[14]

A. Politi and S. Luccioli, Dynamics of networks of leaky-integrate-and-fire neurons, in Network Science, Springer, London, 2010, 217-242. doi: 10.1007/978-1-84996-396-1_11.  Google Scholar

[15]

L. Sacerdote, M. Tamborrino and C. Zucca, Detecting dependencies between spike trains of pairs of neurons through copulas, Brain Research, 1434 (2012), 243-256. doi: 10.1016/j.brainres.2011.08.064.  Google Scholar

[16]

H. Sakaguchi, Oscillatory phase transition and pulse propagation in noisy integrate-and-fire neurons, Phys. Rev. E., 70 (2004), 1-4. doi: 10.1103/PhysRevE.70.022901.  Google Scholar

[17]

H. Sakaguchi and S. Tobiishi, Synchronization and spindle oscillation in noisy integrate-and-fire-or-burst neurons with inhibitory coupling, Progress of Theoretical Physics, 114 (2005), 1-18. doi: 10.1143/PTP.114.539.  Google Scholar

[18]

R. Sirovich, L. Sacerdote and A. E. P. Villa, Effect of increasing inhibitory inputs on information processing within a small network of spiking neurons, in Computational and Ambient Intelligence, Lecture Notes in Computer Science, 4507, Springer, Berlin-Heidelberg, 2007, 23-30. doi: 10.1007/978-3-540-73007-1_4.  Google Scholar

[19]

H. Soula and C. C. Chow, Stochastic dynamics of a finite-size spiking neural network, Neural Comput., 19 (2007), 3262-3292. doi: 10.1162/neco.2007.19.12.3262.  Google Scholar

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