What Is the Most Realistic Single-Compartment Model of Spike Initiation?

A large variety of neuron models are used in theoretical and computational neuroscience, and among these, single-compartment models are a popular kind. These models do not explicitly include the dendrites or the axon, and range from the Hodgkin-Huxley (HH) model to various flavors of integrate-and-fire (IF) models. The main classes of models differ in the way spikes are initiated. Which one is the most realistic? Starting with some general epistemological considerations, I show that the notion of realism comes in two dimensions: empirical content (the sort of predictions that a model can produce) and empirical accuracy (whether these predictions are correct). I then examine the realism of the main classes of single-compartment models along these two dimensions, in light of recent experimental evidence.

[1]  W. Wildman,et al.  Theoretical Neuroscience , 2014 .

[2]  Bertrand Fontaine,et al.  Fitting Neuron Models to Spike Trains , 2011, Front. Neurosci..

[3]  Jose A Encinar,et al.  Clustering and Coupled Gating Modulate the Activity in KcsA, a Potassium Channel Model* , 2006, Journal of Biological Chemistry.

[4]  G. Stuart,et al.  Is action potential threshold lowest in the axon? , 2008, Nature Neuroscience.

[5]  Fred Wolf,et al.  Neurophysiology: Hodgkin and Huxley model — still standing? (Reply) , 2007, Nature.

[6]  G. Baranauskas,et al.  Sodium Currents Activate without a Hodgkin and Huxley-Type Delay in Central Mammalian Neurons , 2006, The Journal of Neuroscience.

[7]  D. McCormick,et al.  Cortical Action Potential Backpropagation Explains Spike Threshold Variability and Rapid-Onset Kinetics , 2008, The Journal of Neuroscience.

[8]  D. Debanne,et al.  Axon physiology. , 2011, Physiological reviews.

[9]  Cyrille Rossant,et al.  Automatic Fitting of Spiking Neuron Models to Electrophysiological Recordings , 2010, Front. Neuroinform..

[10]  Fred Wolf,et al.  Fast Computations in Cortical Ensembles Require Rapid Initiation of Action Potentials , 2013, The Journal of Neuroscience.

[11]  M. Volgushev,et al.  Unique features of action potential initiation in cortical neurons , 2006, Nature.

[12]  D. McCormick,et al.  Neurophysiology: Hodgkin and Huxley model — still standing? , 2007, Nature.

[13]  Wulfram Gerstner,et al.  Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. , 2005, Journal of neurophysiology.

[14]  S. Marx,et al.  Coupled Gating Between Cardiac Calcium Release Channels (Ryanodine Receptors) , 2001, Circulation research.

[15]  S. Marx,et al.  Coupled gating between individual skeletal muscle Ca2+ release channels (ryanodine receptors) , 1998, Science.

[16]  B. Sakmann,et al.  Action potential initiation and propagation in rat neocortical pyramidal neurons , 1997, The Journal of physiology.

[17]  J. Eccles,et al.  The interpretation of spike potentials of motoneurones , 1957, The Journal of physiology.

[18]  Yousheng Shu,et al.  Distinct contributions of Nav1.6 and Nav1.2 in action potential initiation and backpropagation , 2009, Nature Neuroscience.

[19]  Lorin S Milescu,et al.  Isolation of Somatic Na+ Currents by Selective Inactivation of Axonal Channels with a Voltage Prepulse , 2010, The Journal of Neuroscience.

[20]  John P. Dekker,et al.  Cooperative Gating between Single HCN Pacemaker Channels , 2006, The Journal of general physiology.

[21]  M. Kendall,et al.  The Logic of Scientific Discovery. , 1959 .

[22]  Romain Brette,et al.  Sharpness of Spike Initiation in Neurons Explained by Compartmentalization , 2013, PLoS Comput. Biol..

[23]  Greg J. Stuart,et al.  Signal Processing in the Axon Initial Segment , 2012, Neuron.

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

[25]  Fred Wolf,et al.  A Small Fraction of Strongly Cooperative Sodium Channels Boosts Neuronal Encoding of High Frequencies , 2012, PloS one.

[26]  Wulfram Gerstner,et al.  How Good Are Neuron Models? , 2009, Science.

[27]  G. Ermentrout,et al.  Parabolic bursting in an excitable system coupled with a slow oscillation , 1986 .

[28]  S. Wray,et al.  Neural Crest and Olfactory System: New Prospective , 2012, Molecular Neurobiology.

[29]  Elaine Angelino,et al.  Excitability Constraints on Voltage-Gated Sodium Channels , 2007, PLoS Comput. Biol..

[30]  Frances S. Chance,et al.  Effects of synaptic noise and filtering on the frequency response of spiking neurons. , 2001, Physical review letters.

[31]  Wulfram Gerstner,et al.  Reduction of the Hodgkin-Huxley Equations to a Single-Variable Threshold Model , 1997, Neural Computation.

[32]  Johannes J. Letzkus,et al.  Axon Initial Segment Kv1 Channels Control Axonal Action Potential Waveform and Synaptic Efficacy , 2007, Neuron.

[33]  J. Rinzel,et al.  The role of dendrites in auditory coincidence detection , 1998, Nature.

[34]  D. Johnston,et al.  Active properties of neuronal dendrites. , 1996, Annual review of neuroscience.

[35]  José Luis Peña,et al.  Spike-Threshold Adaptation Predicted by Membrane Potential Dynamics In Vivo , 2014, PLoS Comput. Biol..

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

[37]  I. Fleidervish,et al.  Inward sodium current at resting potentials in single cardiac myocytes induced by the ischemic metabolite lysophosphatidylcholine. , 1992, Circulation research.

[38]  G. Marmont.  Studies on the axon membrane; a new method. , 1949, Journal of cellular and comparative physiology.

[39]  B. Kampa,et al.  Action potential generation requires a high sodium channel density in the axon initial segment , 2008, Nature Neuroscience.

[40]  Matthew H. Higgs,et al.  Kv1 channels control spike threshold dynamics and spike timing in cortical pyramidal neurones , 2011, The Journal of physiology.

[41]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[42]  Hao Huang,et al.  Estimating parameters and predicting membrane voltages with conductance-based neuron models , 2014, Biological Cybernetics.

[43]  D. Hansel,et al.  How Spike Generation Mechanisms Determine the Neuronal Response to Fluctuating Inputs , 2003, The Journal of Neuroscience.

[44]  Bruno A. Olshausen,et al.  Book Review , 2003, Journal of Cognitive Neuroscience.

[45]  W. Gerstner,et al.  Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. , 2008, Journal of neurophysiology.

[46]  Simon B. Laughlin,et al.  Action Potential Energy Efficiency Varies Among Neuron Types in Vertebrates and Invertebrates , 2010, PLoS Comput. Biol..

[47]  Daniele Linaro,et al.  High Bandwidth Synaptic Communication and Frequency Tracking in Human Neocortex , 2014, PLoS biology.

[48]  Gary James Jason,et al.  The Logic of Scientific Discovery , 1988 .

[49]  I Segev,et al.  Untangling dendrites with quantitative models. , 2000, Science.

[50]  M. Volgushev,et al.  Ultrafast Population Encoding by Cortical Neurons , 2011, The Journal of Neuroscience.