Definition
The act of reducing the complexity of a mathematical neuron model while preserving its essential features.
Detailed Description
The biophysical description of ion channels distributed on a three-dimensional neuron membrane is a typical example of a complex mathematical neuron model. In such models, the voltage dependence of ion channels follows the formalism of Hodgkin and Huxley. Therefore, each patch of membrane is characterized by the membrane potential, x 1, and the state of activation/inactivation for every ion channel type, x 2, … , x n . These state variables evolve according to a differential equation arising from biophysics and biochemistry. The equations are typically of first order in time, but nonlinear. Given the nonlinear functions f 1, f 2, …, f n , the evolution of n state variables follows:
References
Bialek W, Rieke F, van Steveninck RR, Warland D (1991) Reading a neural code. Science 252(5014):1854–1857
Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94(5):3637–3642
Chaturantabut S, Sorensen D (2010) Nonlinear model reduction via discrete empirical interpolation. SIAM J Sci Comput 32(5):2737–2764
Chichilnisky EJ (2001) A simple white noise analysis of neuronal light responses. Network 12(2):199–213
Eikenberry SE, Marmarelis VZ (2013) A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations. J Comput Neurosci 34(1):163–183
Fitzhugh R (1961) Impulses and physiological states in models of nerve membrane. Biophys J 1(6):445–466
Gerstner W, Kistler W (2002) Spiking neuron models. Cambridge University Press, Cambridge
Hunter IW, Korenberg MJ (1986) The identification of nonlinear biological systems: Wiener and Hammerstein cascade models. Biol Cybern 55(2–3):135–144
Izhikevich EM (2003) Which model to use for cortical spiking neurons? IEEE Trans Neural Network 14(6):1569–1572
Izhikevich EM (2007) Dynamical systems in neuroscience. MIT Press, Cambridge
Kellems AR, Roos D, Xiao N, Cox SJ (2009) Low-dimensional, morphologically accurate models of subthreshold membrane potential. J Comput Neurosci 27(2):161–176
Kellems AR, Chaturantabut S, Sorensen DC, Cox SJ (2010) Morphologically accurate reduced order modeling of spiking neurons. J Comput Neurosci 28(3):477–494
Kistler W, Gerstner W, van Hemmen L (1997) Reduction of the Hodgkin-Huxley equations to a single-variable threshold model. Neural Comput 9(5):1015–1045
Koch C (1984) Cable theory in neurons with active, linearized membranes. Biol Cybern 50(1):15–33
Korenberg MJ, Hunter IW (1986) The identification of nonlinear biological systems: LNL cascade models. Biol Cybern 55(2–3):125–134
London M, Meunier C, Segev I (1999) Signal transfer in passive dendrites with nonuniform membrane conductance. J Neurosci 19(19):8219–8233
Marmarelis PZ, Marmarelis VZ (1978) Analysis of physiological systems: the white-noise approach. Plenum Press, New York
Marmarelis PZ, Naka KI (1973) Nonlinear analysis and synthesis of receptive-field responses in the catfish retinaI. Horizontal cell leads to ganglion cell chain. J Neurophysiol 36(4):605–618
Mel B (2007).Why have dendrites? A computational perspective. In: Stuart G, Spruston N (eds) Dendrites. Oxford University Press, Oxford
Mensi S, Naud R, Pozzorini C, Averman M, Peterson CC, Gerstner W (2012) Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J Neurophysiol 107(6):1756–1775
Morris C, Lecar H (1981) Voltage oscillations in the barnacle giant muscle fiber. Biophys J 35(1):193–213
Nagumo J, Arimoto S, Yoshizawa S (1962) An active pulse transmission line simulating nerve axon. Proc IRE 50:2061–2070
Naud R, Gerstner W (2012) Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram. PLoS Comput Biol 8(10):e1002711
Paninski L (2004) Maximum likelihood estimation of cascade point-process neural encoding models. Network 15(4):243–262
Pinsky PF, Rinzel J (1994) Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. J Comput Neurosci 1(1–2):39–60
Rall W (1995) The theoretical foundation of dendritic function: selected papers of Wilfrid Rall with commentaries. The MIT Press, Chicago
Renart A, Brunel N, Wang XJ (2004). Mean-field theory of irregularly spiking neuronal populations and working memory in recurrent cortical networks. In: Feng J (ed) Computational neuroscience: a comprehensive approach. Chapman & Hall/CRC, Boca Raton, pp 431–490
Further Reading
Gerstner W, Kistler W, Naud R, Paninski L (2014) Neuronal dynamics. Cambridge University Press, Cambridge
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Naud, R. (2014). Neuronal Model Reduction. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_166-1
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DOI: https://doi.org/10.1007/978-1-4614-7320-6_166-1
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