Neural Comp. NEW Faster Access
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
 QUICK SEARCH:   [advanced]


     


This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Gutkin, B. S.
Right arrow Articles by Ermentrout, G. B.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Gutkin, B. S.
Right arrow Articles by Ermentrout, G. B.

Neural Computation, Vol 10, 1047-1065, Copyright © 1998 by The MIT Press


ARTICLES

Dynamics of Membrane Excitability Determine Inter-Spike Interval Variability: A Link Between Spike Generation Mechanisms and Cortical Spike Train Statistics

Boris S. Gutkin and G. Bard Ermentrout

We propose a biophysical mechanism for the high interspike interval variability observed in cortical spike trains. The key lies in the nonlinear dynamics of cortical spike generation, which are consistent with type I membranes where saddle-node dynamics underlie excitability (Rinzel & Ermentrout, 1989). We present a canonical model for type I membranes, the -neuron. The -neuron is a phase model whose dynamics reflect salient features of type I membranes. This model generates spike trains with coefficient of variation (CV) above 0.6 when brought to firing by noisy inputs. This happens because the timing of spikes for a type I excitable cell is exquisitely sensitive to the amplitude of the suprathreshold stimulus pulses. Anoisy input current, giving random amplitude "kicks" to the cell, evokes highly irregular firing across a wide range of firing rates; an intrinsically oscillating cell gives regular spike trains. We corroborate the results with simulations of the Morris-Lecar (M-L) neural model with random synaptic inputs: type I M-L yields high CVs. When this model is modified to have type II dynamics (periodicity arises via a Hopf bifurcation), however, it gives regular spike trains (CV below 0.3). Our results suggest that the high CV values such as those observed in cortical spike trains are an intrinsic characteristic of type I membranes driven to firing by "random" inputs. In contrast, neural oscillators or neurons exhibiting type II excitability should produce regular spike trains.


This article has been cited by other articles:


Home page
Neural Comput.Home page
C. Borgers and N. J. Kopell
Gamma Oscillations and Stimulus Selection
Neural Comput., February 1, 2008; 20(2): 383 - 414.
[Abstract] [Full Text] [PDF]


Home page
J. Neurophysiol.Home page
L. Badel, S. Lefort, R. Brette, C. C. H. Petersen, W. Gerstner, and M. J. E. Richardson
Dynamic I-V Curves Are Reliable Predictors of Naturalistic Pyramidal-Neuron Voltage Traces
J Neurophysiol, February 1, 2008; 99(2): 656 - 666.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
A. Tonnelier, H. Belmabrouk, and D. Martinez
Event-Driven Simulations of Nonlinear Integrate-and-Fire Neurons.
Neural Comput., December 1, 2007; 19(12): 3226 - 3238.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
D. Marinazzo, H. J. Kappen, and S. C. A. M. Gielen
Input-Driven Oscillations in Networks with Excitatory and Inhibitory Neurons with Dynamic Synapses.
Neural Comput., July 1, 2007; 19(7): 1739 - 1765.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
P. Rowat
Interspike Interval Statistics in the Stochastic Hodgkin-Huxley Model: Coexistence of Gamma Frequency Bursts and Highly Irregular Firing
Neural Comput., May 1, 2007; 19(5): 1215 - 1250.
[Abstract] [Full Text] [PDF]


Home page
J. Neurophysiol.Home page
R. M. Davies, G. L. Gerstein, and S. N. Baker
Measurement of Time-Dependent Changes in the Irregularity of Neural Spiking
J Neurophysiol, August 1, 2006; 96(2): 906 - 918.
[Abstract] [Full Text] [PDF]


Home page
J. Neurophysiol.Home page
T. Tateno and H.P.C. Robinson
Rate Coding and Spike-Time Variability in Cortical Neurons With Two Types of Threshold Dynamics
J Neurophysiol, April 1, 2006; 95(4): 2650 - 2663.
[Abstract] [Full Text] [PDF]


Home page
Am. J. Respir. Crit. Care Med.Home page
J. John, E. F. Bailey, and R. F. Fregosi
Respiratory-related Discharge of Genioglossus Muscle Motor Units
Am. J. Respir. Crit. Care Med., November 15, 2005; 172(10): 1331 - 1337.
[Abstract] [Full Text] [PDF]


Home page
J. Neurophysiol.Home page
B. S. Gutkin, G. B. Ermentrout, and A. D. Reyes
Phase-Response Curves Give the Responses of Neurons to Transient Inputs
J Neurophysiol, August 1, 2005; 94(2): 1623 - 1635.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
A. Tonnelier
Categorization of Neural Excitability Using Threshold Models
Neural Comput., July 1, 2005; 17(7): 1447 - 1455.
[Abstract] [Full Text] [PDF]


Home page
J. Physiol.Home page
G. A Jacobson, K. Diba, A. Yaron-Jakoubovitch, Y. Oz, C. Koch, I. Segev, and Y. Yarom
Subthreshold voltage noise of rat neocortical pyramidal neurones
J. Physiol., April 1, 2005; 564(1): 145 - 160.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
C. Borgers and N. Kopell
Effects of Noisy Drive on Rhythms in Networks of Excitatory and Inhibitory Neurons
Neural Comput., March 1, 2005; 17(3): 557 - 608.
[Abstract] [Full Text] [PDF]


Home page
J. Neurophysiol.Home page
T. Tateno, A. Harsch, and H. P. C. Robinson
Threshold Firing Frequency-Current Relationships of Neurons in Rat Somatosensory Cortex: Type 1 and Type 2 Dynamics
J Neurophysiol, October 1, 2004; 92(4): 2283 - 2294.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
P. E. Latham and S. Nirenberg
Computing and Stability in Cortical Networks
Neural Comput., July 1, 2004; 16(7): 1385 - 1412.
[Abstract] [Full Text] [PDF]


Home page
J. Neurosci.Home page
A. Yamaguchi, L. K. Kaczmarek, and D. B. Kelley
Functional Specialization of Male and Female Vocal Motoneurons
J. Neurosci., December 17, 2003; 23(37): 11568 - 11576.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
N. Brunel and P. E. Latham
Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron
Neural Comput., October 1, 2003; 15(10): 2281 - 2306.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
B. Lindner, A. Longtin, and A. Bulsara
Analytic Expressions for Rate and CV of a Type I Neuron Driven by White Gaussian Noise
Neural Comput., August 1, 2003; 15(8): 1761 - 1788.
[Abstract] [Full Text]


Home page
Neural Comput.Home page
C. Borgers and N. Kopell
Synchronization in Networks of Excitatory and Inhibitory Neurons with Sparse, Random Connectivity
Neural Comput., March 1, 2003; 15(3): 509 - 538.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
E. Salinas and T. J. Sejnowski
Integrate-and-Fire Neurons Driven by Correlated Stochastic Input
Neural Comput., September 1, 2002; 14(9): 2111 - 2155.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
K. Pakdaman
The Reliability of the Stochastic Active Rotator
Neural Comput., April 1, 2002; 14(4): 781 - 792.
[Abstract] [Full Text] [PDF]


Home page
Neural Comput.Home page
P. Roper, P. C. Bressloff, and A. Longtin
A Phase Model of Temperature-Dependent Mammalian Cold Receptors
Neural Comput., May 1, 2000; 12(5): 1067 - 1093.
[Abstract] [Full Text]


Home page
Neural Comput.Home page
W. Gerstner
Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking
Neural Comput., January 1, 2000; 12(1): 43 - 89.
[Abstract] [Full Text]




HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
J COGNITIVE NEUROSCIENCE NEURAL COMPUTATION MIT PRESS JOURNALS
Copyright © 1998 by The MIT Press.