Electrophysiological models of neural processing

The brain is an amazing information processing system that allows organisms to adaptively monitor and control complex dynamic interactions with their environment across multiple spatial and temporal scales. Mathematical modeling and computer simulation techniques have become essential tools in understanding diverse aspects of neural processing ranging from sub‐millisecond temporal coding in the sound localization circuity of barn owls to long‐term memory storage and retrieval in humans that can span decades. The processing capabilities of individual neurons lie at the core of these models, with the emphasis shifting upward and downward across different levels of biological organization depending on the nature of the questions being addressed. This review provides an introduction to the techniques for constructing biophysically based models of individual neurons and local networks. Topics include Hodgkin‐Huxley‐type models of macroscopic membrane currents, Markov models of individual ion‐channel currents, compartmental models of neuronal morphology, and network models involving synaptic interactions among multiple neurons. WIREs Syst Biol Med 2011 3 74–92 DOI: 10.1002/wsbm.95

[1]  R J Dunn,et al.  A prominent soma‐dendritic distribution of Kv3.3 K+ channels in electrosensory and cerebellar neurons , 2001, The Journal of comparative neurology.

[2]  I Segev,et al.  Propagation of action potentials along complex axonal trees. Model and implementation. , 1991, Biophysical journal.

[3]  Perry L. Miller,et al.  The Human Brain Project: neuroinformatics tools for integrating, searching and modeling multidisciplinary neuroscience data , 1998, Trends in Neurosciences.

[4]  C. Koch,et al.  Multiple channels and calcium dynamics , 1989 .

[5]  J. Ruppersberg Ion Channels in Excitable Membranes , 1996 .

[6]  Idan Segev,et al.  Cable and Compartmental Models of Dendritic Trees , 1998 .

[7]  A. Destexhe Kinetic Models of Synaptic Transmission , 1997 .

[8]  R J Dunn,et al.  The Contribution of Dendritic Kv3 K+ Channels to Burst Threshold in a Sensory Neuron , 2001, The Journal of Neuroscience.

[9]  R. Traub Simulation of intrinsic bursting in CA3 hippocampal neurons , 1982, Neuroscience.

[10]  Erik De Schutter,et al.  Modeling Simple and Complex Active Neurons , 2000 .

[11]  Nicholas T. Carnevale,et al.  Neuron simulation environment , 2007, Scholarpedia.

[12]  Idan Segev,et al.  Methods in neuronal modeling: From synapses to networks , 1989 .

[13]  Hillel J. Chiel,et al.  Fast Calculation of Synaptic Conductances , 1993, Neural Computation.

[14]  B. Rudy,et al.  Diversity and ubiquity of K channels , 1988, Neuroscience.

[15]  Idan Segev,et al.  The Impact of Parallel Fiber Background Activity on the Cable Properties of Cerebellar Purkinje Cells , 1992, Neural Computation.

[16]  M. F. Huerta,et al.  Neuroinformatics : An Overview of the Human Brain Project , 2013 .

[17]  A. Erisir,et al.  Contributions of Kv3 Channels to Neuronal Excitability , 1999, Annals of the New York Academy of Sciences.

[18]  L. Kaczmarek,et al.  Contribution of the Kv3.1 potassium channel to high‐frequency firing in mouse auditory neurones , 1998, The Journal of physiology.

[19]  C. Koch,et al.  Methods in Neuronal Modeling: From Ions to Networks , 1998 .

[20]  Idan Segev,et al.  Compartmental models of complex neurons , 1989 .

[21]  B. Rudy,et al.  Molecular Diversity of K+ Channels , 1999, Annals of the New York Academy of Sciences.

[22]  R. Barker,et al.  Foundations of Neurobiology , 1998 .

[23]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[24]  A. Destexhe,et al.  Which Formalism to Use for Modeling Voltage- Dependent Conductances? , 2000 .

[25]  Terrence J. Sejnowski,et al.  Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism , 1994, Journal of Computational Neuroscience.

[26]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[27]  Ed Zintel,et al.  Resources , 1998, IT Prof..

[28]  Erik De Schutter,et al.  Computational neuroscience : realistic modeling for experimentalists , 2000 .

[29]  A Longtin,et al.  Model of gamma frequency burst discharge generated by conditional backpropagation. , 2001, Journal of neurophysiology.

[30]  C. Agner,et al.  The Neuron: Cell and Molecular Biology, 3rd Edition , 2002 .

[31]  James M. Bower,et al.  The GENESIS Simulation System , 2003 .

[32]  C. Blackstone The Neuron: Cell and Molecular Biology , 2003 .

[33]  W. Rall Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. , 1967, Journal of neurophysiology.

[34]  J. Bower,et al.  An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice. , 1994, Journal of neurophysiology.

[35]  M. F. Huerta,et al.  The human brain project: an international resource , 1993, Trends in Neurosciences.

[36]  Upinder Bhalla,et al.  Modeling Networks of Signaling Pathways , 2000 .

[37]  J. Bower,et al.  An active membrane model of the cerebellar Purkinje cell II. Simulation of synaptic responses. , 1994, Journal of neurophysiology.

[38]  Nicholas T. Carnevale,et al.  The NEURON Simulation Environment , 1997, Neural Computation.

[39]  James M. Bower,et al.  The Book of GENESIS , 1994, Springer New York.

[40]  J. Patlak Molecular kinetics of voltage-dependent Na+ channels. , 1991, Physiological reviews.