Neuronal Dynamics: from Complexity to Simplicity
Abstract
An analytical study is performed to obtain the phase description of a network of Leaky Integrate-and-Fire (LIF) neurons. We start by discussing the behaviour of single LIF neuron in the presence of a constant current and then derive the corresponding phase oscillator model for some parameters setup. In the case of two identical LIF neurons where the interactions are ruled by the weak pulse input, we determine the analytical expression for the phase response curve. Next, we extend the phase reduction principles to a generic case of N networks of identical LIF neurons. The final model of so-called phase oscillators is widely used to study synchronization in many natural systems. Through numerical simulations, we find an agreement between the LIF neurons and the phase oscillators model.
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Abbott, L. F. (1999). Lapicque's introduction of the integrate-and-fire model neuron (1907). Brain Research Bulletin, 50(5/6), 303-304. doi:https://doi.org/10.1016/s0361-9230(99)00161-6
Abbott, L. F., & Vreeswijk, C. V. (1993). Asynchronous states in networks of pulse-coupled oscillators. Phys. Rev. E, 48(2), 1483-1490. doi:https://doi.org/10.1103/physreve.48.1483
Afifurrahman, Ullner, E., & Politi, A. (2020). Stability of synchronous states in sparse neuronal networks. Nonlinear Dynamics, 102(2), 733-743. doi:https://doi.org/10.1007/s11071-020-05880-4
Afifurrahman, Ullner, E., & Politi, A. (2021). Collective dynamics in the presence of finite-width pulses. Chaos: An Interdisciplinary Journal of Nonlinear Science, 31(4), 043135. doi:https://doi.org/10.1063/5.0046691
Barabash, N. V., Belykh, V. N., Osipov, G. V., & Belykh, I. V. (2021). Partial synchronization in the second-order Kuramoto model: An auxiliary system method. Chaos: An Interdisciplinary Journal of Nonlinear Science, 113113. doi:https://doi.org/10.1063/5.0066663
Bonnin, M., Corinto, F., & Gilli, M. (2010). Phase model reduction and synchronization of periodically forced nonlinear oscillators. Journal of circuits, systems and computers, 19(04), 749-762. doi:https://doi.org/10.1142/S0218126610006414
Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., . . . Pecevsk, D. (2007). Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience, 23(3), 349-398. doi:https://doi.org/10.1007/s10827-007-0038-6
Brunel, N., & Van Rossum, M. C. (2007). Lapicque's 1907 paper: from frogs to integrate-and-fire. Biological Cybernetics, 97(5), 337-339. doi:https://doi.org/10.1007/s00422-007-0190-0
Burke, K. J., & Bender, K. J. (2019). Modulation of Ion Channels in the Axon: Mechanisms and Function. Frontiers in Cellular Neuroscience, 13. doi:https://doi.org/10.3389/fncel.2019.00221
Canavier, C. C. (2006). Phase response curve. Scholarpedia, 1(12), 1332. doi:http://dx.doi.org/10.4249/scholarpedia.1332
Catterall, W. A., Raman, I. M., Robinson, H. P., Sejnowski, T. J., & Paulsen, O. (2012). The Hodgkin-Huxley Heritage: From Channels to Circuits. The Journal of Neuroscience, 32(41), 14064. doi:https://doi.org/10.1523/JNEUROSCI.3403-12.2012
Coombes, S., Thul, R., & Wedgwood, K. C. (2012). Nonsmooth dynamics in spiking neuron models. Physica D: Nonlinear Phenomena, 241(22), 2042-2057. doi:https://doi.org/10.1016/j.physd.2011.05.012
FitzHugh, R. (1961). Impulses and Physiological States in Theoretical Models of Nerve Membrane. Biophysical Journal, 445-466. doi:https://doi.org/10.1016/S0006-3495(61)86902-6
Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics: from single neurons to networks and models of cognition. Cambridge: Cambridge University Press. Retrieved from https://neuronaldynamics.epfl.ch/online/index.html
Godinez, S. V., Sossa, H., & Montero, R. S. (2017). How the accuracy and computational cost of spiking neuron simulation are affected by the time span and firing rate. Comp. y Sist., 21(4), 841-861. doi:https://doi.org/10.13053/CyS-21-4-2787
Golomb, D. (2007). Neuronal synchrony measure. Scholarpedia, 1347. doi:http://dx.doi.org/10.4249/scholarpedia.1347
Golomb, D., Hansel, D., & Mato, G. (2001). Mechanisms of synchrony of neural activity in large networks. In Neuro-Informatics and Neural Modelling (pp. 887-968). North-Holland. doi:https://doi.org/10.1016/S1383-8121(01)80024-5
Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 500-544. doi:https://doi.org/10.1113/jphysiol.1952.sp004764
Izhikevich, E. M., & Ermentrout, B. (2008). Phase model. Scholarpedia, 3(10), 1487. doi:http://dx.doi.org/10.4249/scholarpedia.1487
Izhikevich, E. M., & FitzHugh, R. (2006). FitzHugh-Nagumo model. Scholarpedia, 1(9), 1349. doi:http://dx.doi.org/10.4249/scholarpedia.1349
Izhikevich, E. M., Poggio, T. A., & Sejnowski, T. J. (2006). Dynamical systems in neuroscience : the geometry of excitability and bursting. Cambridge US: MIT Press. doi:https://doi.org/10.7551/mitpress/2526.001.0001
Kanamaru, T. (2007). Van der Pol oscillator. Scholarpedia, 2(1), 2202. doi:http://dx.doi.org/10.4249/scholarpedia.2202
Lewis, T. J., & Rinzel, J. (2003). Dynamics of Spiking Neurons Connected by Both Inhibitory and Electrical Coupling. Journal of Computational Neuroscience, 14(3), 283-309. doi:https://doi.org/10.1023/A:1023265027714
Mathie, A., Kennard, L. E., & Veale, E. L. (2003). Neuronal ion channels and their sensitivity to extremely low frequency weak electric field effects. Radiation Protection Dosimetry, 106(4), 311-315. doi:https://doi.org/10.1093/oxfordjournals.rpd.a006365
Mirollo, R. E., & Strogatz, S. H. (1990). Synchronization of Pulse-Coupled Biological Oscillators. SIAM Journal on Applied Mathematics, 50(6), 1645-1662. doi:https://doi.org/10.1137/0150098
Nakao, H. (2016). Phase reduction approach to synchronisation of nonlinear oscillators. Contemporary Physics, 57(2), 188-214. doi:https://doi.org/10.1080/00107514.2015.1094987
Pikovsky, A., Rosenblum, M. G., & Kurths, J. (2001). Synchronization, A Universal Concept in Nonlinear Sciences. Cambridge: Cambridge University Press.
Politi, A., & Rosenblum, M. (2015). Equivalence of phase-oscillator and integrate-and-fire models. Phys. Rev. E, 042916. doi:https://doi.org/10.1103/PhysRevE.91.042916
Sherwood, W. E. (2013). FitzHugh-Nagumo model. In Encyclopedia of Computational Neuroscience (pp. 1-11). New York: Springer New York. doi:https://doi.org/10.1007/978-1-4614-7320-6_147-1
Strogatz, S. H. (2000). From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators. Physica D: Nonlinear Phenomena, 143(1), 1-20. doi:https://doi.org/10.1016/S0167-2789(00)00094-4
Weisstein, E. W. (2023, March 24). Strong Law of Large Numbers. Retrieved from Wolfram Mathworld: https://mathworld.wolfram.com/StrongLawofLargeNumbers.html
Winfree, A. T. (1967). Biological rhyhtms and the behavior of populations of coupled oscillators. Journal of Theoretical Biology, 15-42. doi:https://doi.org/10.1016/0022-5193(67)90051-3
DOI: https://doi.org/10.31764/jtam.v7i2.12410
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