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2016, 13(3): 483-493. doi: 10.3934/mbe.2016002

A leaky integrate-and-fire model with adaptation for the generation of a spike train

Aniello Buonocore 1, , Luigia Caputo 2, , Enrica Pirozzi 2, and  Maria Francesca Carfora 3,

1. 

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

Dipartimento di Matematica e Applicazioni, Università di Napoli Federico II, Via Cintia, Napoli
3. 

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

Received  April 2015 Revised  November 2015 Published  January 2016

A model is proposed to describe the spike-frequency adaptation observed in many neuronal systems. We assume that adaptation is mainly due to a calcium-activated potassium current, and we consider two coupled stochastic differential equations for which an analytical approach combined with simulation techniques and numerical methods allow to obtain both qualitative and quantitative results about asymptotic mean firing rate, mean calcium concentration and the firing probability density. A related algorithm, based on the Hazard Rate Method, is also devised and described.
Keywords: hazard rate method., Calcium-activated potassium current, fast-slow analysis.
Mathematics Subject Classification: Primary: 60J60; Secondary: 60H3.
Citation: Aniello Buonocore, Luigia Caputo, Enrica Pirozzi, Maria Francesca Carfora. A leaky integrate-and-fire model with adaptation for the generation of a spike train. Mathematical Biosciences & Engineering, 2016, 13 (3) : 483-493. doi: 10.3934/mbe.2016002
References:
[1]

J. Benda and A. W. M. Herz, A universal model for spike-frequency adaptation,, Neural Computation, 15 (2003), 2523.  doi: 10.1162/089976603322385063.  Google Scholar

[2]

R. Brette and W. Gerstner, Adaptive exponential integrate-and-fire model as an effective description of neuronal activity,, Journal of Neurophysiology, 94 (2005), 3637.  doi: 10.1152/jn.00686.2005.  Google Scholar

[3]

D. A. Brown and P. R. Adams, Muscarinic supression of a novel voltage-sensitive K+ current in a vertebrate neuron,, Nature, 183 (1980), 673.   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,, Mathematical Biosciences and Engineering, 11 (2014), 1.   Google Scholar

[5]

A. Buonocore, L. Caputo, E. Pirozzi and L. M. Ricciardi, On a generalized leaky integrate-and-fire model for single neuron activity,, in Computer Aided Systems Theory - EUROCAST 2005, (2005), 152.  doi: 10.1007/978-3-642-04772-5_21.  Google Scholar

[6]

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

[7]

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

[8]

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

[9]

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 Computation, 15 (2003), 253.  doi: 10.1162/089976603762552915.  Google Scholar

[10]

S. M. Crook, G. B. Ermentrout and J. M. Bower, Spike frequency adaptation affects the synchronization properties of networks of cortical oscillators,, Neural Computation, 10 (1998), 837.  doi: 10.1162/089976698300017511.  Google Scholar

[11]

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

[12]

B. Ermentrout, M. Pascal and B. Gutkin, The effects of spike frequency adaptation and negative feedback on the synchronization of neural oscillators,, Neural Computation, 13 (2001), 1285.  doi: 10.1162/08997660152002861.  Google Scholar

[13]

I. A. Fleidervish, A. Friedman and M. J. Gutnick, Slow inactivation of Na+ current and slow cumulative spike adaptation in mouse and guinea-pig neocortical neurones in slices,, The Journal of Physiology, 493 (1996), 83.  doi: 10.1113/jphysiol.1996.sp021366.  Google Scholar

[14]

G. Fuhrmann, H. Markram and M. Tsodyks, Spike frequency adaptation and neocortical rhythms,, Journal of Neurophysiology, 88 (2002), 761.   Google Scholar

[15]

R. Granit, D. Kernell and G. K. Shortess, Quantitative aspects of repetitive firing of mammalian motoneurones, caused by injected currents,, The Journal of Physiology, 168 (1963), 911.  doi: 10.1113/jphysiol.1963.sp007230.  Google Scholar

[16]

B. Hille, Ion Channels of Excitable Membranes,, Sinauer Associates, (2001).   Google Scholar

[17]

A. L. Hodgkin and A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve,, Journal of Physiology, 117 (1952), 500.   Google Scholar

[18]

A. V. Holden, Models of the Stochastic Activity of Neurones,, Springer-Verlag, (1976).   Google Scholar

[19]

R. Jolivet, R. Kobayashi, A. Rauch, R. Naud, S. Shinomoto and W. Gerstner, A benchmark test for a quantitative assessment of simple neuron models,, Journal of Neuroscience Methods, 169 (2008), 417.  doi: 10.1016/j.jneumeth.2007.11.006.  Google Scholar

[20]

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

[21]

G. La Camera, A. Rauch, H. R. Luescher, W. Senn and S. Fusi, Minimal models of adapted neuronal response to in vivo-like input currents,, Neural Computation, 16 (2004), 2101.  doi: 10.1162/0899766041732468.  Google Scholar

[22]

Y. H. Liu and X. J. Wang, Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron,, Journal of Computational Neuroscience, 10 (2001), 24.   Google Scholar

[23]

D. V. Madison and R. A. Nicoll, Control of the repetitive discharge of rat CA1 pyramidal neurones in vitro,, The Journal of Physiology, 354 (1984), 319.   Google Scholar

[24]

A. G. Nobile, L. M. Ricciardi and L. Sacerdote, Exponential trends of Ornstein-Uhlenbeck first-passage-time densities,, Journal of Applied Probability, 22 (1985), 360.  doi: 10.2307/3213779.  Google Scholar

[25]

R. K. Powers, A. Sawczuk, J. R. Musick and M. D. Binder, Multiple mechanisms of spike-frequency adaptation in motoneurones,, Journal of Physiology, 93 (1999), 101.  doi: 10.1016/S0928-4257(99)80141-7.  Google Scholar

[26]

A. Rauch, G. La Camera, H. R. Luescher, W. Senn and S. Fusi, Neocortical cells respond as integrate-and-fire neurons to in vivo-like input currents,, Journal of Neurophysiology, 90 (2003), 1598.  doi: 10.1152/jn.00293.2003.  Google Scholar

[27]

L. Sacerdote and M. Giraudo, Stochastic integrate and fire models: A review on mathematical methods and their applications,, in Stochastic Biomathematical Models with Applications to Neuronal Modeling (eds. Bachar, (2013), 99.  doi: 10.1007/978-3-642-32157-3_5.  Google Scholar

[28]

P. Sah, Ca2+-activated K+ currents in neurones: Types, physiological roles and modulation,, Trends in Neurosciences, 19 (1996), 150.  doi: 10.1016/S0166-2236(96)80026-9.  Google Scholar

[29]

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

[30]

W. R. Softky and C. Koch, The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs,, Journal of Neurosciences, 13 (1993), 334.   Google Scholar

[31]

H. C. Tuckwell, Introduction to Theoretical Neurobiology, Vol. 2,, Cambridge University Press, (1988).   Google Scholar

show all references

References:
[1]

J. Benda and A. W. M. Herz, A universal model for spike-frequency adaptation,, Neural Computation, 15 (2003), 2523.  doi: 10.1162/089976603322385063.  Google Scholar

[2]

R. Brette and W. Gerstner, Adaptive exponential integrate-and-fire model as an effective description of neuronal activity,, Journal of Neurophysiology, 94 (2005), 3637.  doi: 10.1152/jn.00686.2005.  Google Scholar

[3]

D. A. Brown and P. R. Adams, Muscarinic supression of a novel voltage-sensitive K+ current in a vertebrate neuron,, Nature, 183 (1980), 673.   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,, Mathematical Biosciences and Engineering, 11 (2014), 1.   Google Scholar

[5]

A. Buonocore, L. Caputo, E. Pirozzi and L. M. Ricciardi, On a generalized leaky integrate-and-fire model for single neuron activity,, in Computer Aided Systems Theory - EUROCAST 2005, (2005), 152.  doi: 10.1007/978-3-642-04772-5_21.  Google Scholar

[6]

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

[7]

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

[8]

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

[9]

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 Computation, 15 (2003), 253.  doi: 10.1162/089976603762552915.  Google Scholar

[10]

S. M. Crook, G. B. Ermentrout and J. M. Bower, Spike frequency adaptation affects the synchronization properties of networks of cortical oscillators,, Neural Computation, 10 (1998), 837.  doi: 10.1162/089976698300017511.  Google Scholar

[11]

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

[12]

B. Ermentrout, M. Pascal and B. Gutkin, The effects of spike frequency adaptation and negative feedback on the synchronization of neural oscillators,, Neural Computation, 13 (2001), 1285.  doi: 10.1162/08997660152002861.  Google Scholar

[13]

I. A. Fleidervish, A. Friedman and M. J. Gutnick, Slow inactivation of Na+ current and slow cumulative spike adaptation in mouse and guinea-pig neocortical neurones in slices,, The Journal of Physiology, 493 (1996), 83.  doi: 10.1113/jphysiol.1996.sp021366.  Google Scholar

[14]

G. Fuhrmann, H. Markram and M. Tsodyks, Spike frequency adaptation and neocortical rhythms,, Journal of Neurophysiology, 88 (2002), 761.   Google Scholar

[15]

R. Granit, D. Kernell and G. K. Shortess, Quantitative aspects of repetitive firing of mammalian motoneurones, caused by injected currents,, The Journal of Physiology, 168 (1963), 911.  doi: 10.1113/jphysiol.1963.sp007230.  Google Scholar

[16]

B. Hille, Ion Channels of Excitable Membranes,, Sinauer Associates, (2001).   Google Scholar

[17]

A. L. Hodgkin and A. F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve,, Journal of Physiology, 117 (1952), 500.   Google Scholar

[18]

A. V. Holden, Models of the Stochastic Activity of Neurones,, Springer-Verlag, (1976).   Google Scholar

[19]

R. Jolivet, R. Kobayashi, A. Rauch, R. Naud, S. Shinomoto and W. Gerstner, A benchmark test for a quantitative assessment of simple neuron models,, Journal of Neuroscience Methods, 169 (2008), 417.  doi: 10.1016/j.jneumeth.2007.11.006.  Google Scholar

[20]

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

[21]

G. La Camera, A. Rauch, H. R. Luescher, W. Senn and S. Fusi, Minimal models of adapted neuronal response to in vivo-like input currents,, Neural Computation, 16 (2004), 2101.  doi: 10.1162/0899766041732468.  Google Scholar

[22]

Y. H. Liu and X. J. Wang, Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron,, Journal of Computational Neuroscience, 10 (2001), 24.   Google Scholar

[23]

D. V. Madison and R. A. Nicoll, Control of the repetitive discharge of rat CA1 pyramidal neurones in vitro,, The Journal of Physiology, 354 (1984), 319.   Google Scholar

[24]

A. G. Nobile, L. M. Ricciardi and L. Sacerdote, Exponential trends of Ornstein-Uhlenbeck first-passage-time densities,, Journal of Applied Probability, 22 (1985), 360.  doi: 10.2307/3213779.  Google Scholar

[25]

R. K. Powers, A. Sawczuk, J. R. Musick and M. D. Binder, Multiple mechanisms of spike-frequency adaptation in motoneurones,, Journal of Physiology, 93 (1999), 101.  doi: 10.1016/S0928-4257(99)80141-7.  Google Scholar

[26]

A. Rauch, G. La Camera, H. R. Luescher, W. Senn and S. Fusi, Neocortical cells respond as integrate-and-fire neurons to in vivo-like input currents,, Journal of Neurophysiology, 90 (2003), 1598.  doi: 10.1152/jn.00293.2003.  Google Scholar

[27]

L. Sacerdote and M. Giraudo, Stochastic integrate and fire models: A review on mathematical methods and their applications,, in Stochastic Biomathematical Models with Applications to Neuronal Modeling (eds. Bachar, (2013), 99.  doi: 10.1007/978-3-642-32157-3_5.  Google Scholar

[28]

P. Sah, Ca2+-activated K+ currents in neurones: Types, physiological roles and modulation,, Trends in Neurosciences, 19 (1996), 150.  doi: 10.1016/S0166-2236(96)80026-9.  Google Scholar

[29]

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

[30]

W. R. Softky and C. Koch, The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs,, Journal of Neurosciences, 13 (1993), 334.   Google Scholar

[31]

H. C. Tuckwell, Introduction to Theoretical Neurobiology, Vol. 2,, Cambridge University Press, (1988).   Google Scholar

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