Low-dimensional models of single neurons: a review

被引:0
作者
Ulises Chialva
Vicente González Boscá
Horacio G. Rotstein
机构
[1] Universidad Nacional del Sur and CONICET,Departamento de Matemática
[2] New York University,Courant Institute of Mathematical Sciences
[3] New Jersey Institute of Technology and Rutgers University,Federated Department of Biological Sciences
[4] Behavioral Neurosciences Program,undefined
[5] Rutgers University,undefined
[6] Corresponding Investigators Group,undefined
来源
Biological Cybernetics | 2023年 / 117卷
关键词
Models of Hodgkin–Huxley type; Conductance-based models; Model reduction of dimensions; Phenomenological reduced models; Linearized and quadratized models; Models of integrate-and-fire type;
D O I
暂无
中图分类号
学科分类号
摘要
The classical Hodgkin–Huxley (HH) point-neuron model of action potential generation is four-dimensional. It consists of four ordinary differential equations describing the dynamics of the membrane potential and three gating variables associated to a transient sodium and a delayed-rectifier potassium ionic currents. Conductance-based models of HH type are higher-dimensional extensions of the classical HH model. They include a number of supplementary state variables associated with other ionic current types, and are able to describe additional phenomena such as subthreshold oscillations, mixed-mode oscillations (subthreshold oscillations interspersed with spikes), clustering and bursting. In this manuscript we discuss biophysically plausible and phenomenological reduced models that preserve the biophysical and/or dynamic description of models of HH type and the ability to produce complex phenomena, but the number of effective dimensions (state variables) is lower. We describe several representative models. We also describe systematic and heuristic methods of deriving reduced models from models of HH type.
引用
收藏
页码:163 / 183
页数:20
相关论文
共 279 条
  • [1] Abbott LF(1999)Lapicque’s introduction of the integrate-and-fire model neuron (1907) Brain Res Bull 50 303-304
  • [2] Acker CD(2003)Synchronization of strongly coupled excitatory neurons: Relating network behavior to biophysics J Comput Neurosci 15 71-90
  • [3] Kopell N(2015)An evolutionary computing approach for parameter estimation investigation of a model for cholera J Biol Dyn 9 147-158
  • [4] White JA(1989)The slow passage through a Hopf bifurcation: delay, memory effects, and resonance SIAM J Appl Math 49 55-71
  • [5] Akman O(2013)Dynamics of the exponential integrate-and-fire model with slow currents and adaptation J Comput Neurosci 37 161-180
  • [6] Schaefer E(1995)Topological and phenomenological classification of bursting oscillations Bull Math Biol 57 413-439
  • [7] Baer SM(1948)Activation of passive iron as a model for the excitation of nerve J Gen Physiol 32 69-91
  • [8] Erneux T(2005)Adaptive exponential integrate-and-fire model as an effective description of neuronal activity J Neurophysiol 94 3637-3642
  • [9] Rinzel J(2008)Introduction to focus issue: mixed mode oscillations: experiment, computation, and analysis Chaos 18 337-339
  • [10] Barranca VJ(2007)Lapicque’s 1907 paper: from frogs to integrate-and-fire Biol Cybern 97 3932-3937