Literature DB >> 11138142

Comparison of current-driven and conductance-driven neocortical model neurons with Hodgkin-Huxley voltage-gated channels.

P H Tiesinga1, J V José, T J Sejnowski.   

Abstract

Intrinsic noise and random synaptic inputs generate a fluctuating current across neuron membranes. We determine the statistics of the output spike train of a biophysical model neuron as a function of the mean and variance of the fluctuating current, when the current is white noise, or when it derives from Poisson trains of excitatory and inhibitory postsynaptic conductances. In the first case, the firing rate increases with increasing variance of the current, whereas in the latter case it decreases. In contrast, the firing rate is independent of variance (for constant mean) in the commonly used random walk, and perfect integrate-and-fire models for spike generation. The model neuron can be in the current-dominated state, representative of neurons in the in vitro slice preparation, or in the fluctuation-dominated state, representative of in vivo neurons. We discuss the functional relevance of these states to cortical information processing.

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Year:  2000        PMID: 11138142     DOI: 10.1103/physreve.62.8413

Source DB:  PubMed          Journal:  Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics        ISSN: 1063-651X


  26 in total

1.  An analytical model for the "large, fluctuating synaptic conductance state" typical of neocortical neurons in vivo.

Authors:  Hamish Meffin; Anthony N Burkitt; David B Grayden
Journal:  J Comput Neurosci       Date:  2004 Mar-Apr       Impact factor: 1.621

2.  Gain control of firing rate by shunting inhibition: roles of synaptic noise and dendritic saturation.

Authors:  Steven A Prescott; Yves De Koninck
Journal:  Proc Natl Acad Sci U S A       Date:  2003-02-04       Impact factor: 11.205

3.  Neuronal integration of synaptic input in the fluctuation-driven regime.

Authors:  Alexandre Kuhn; Ad Aertsen; Stefan Rotter
Journal:  J Neurosci       Date:  2004-03-10       Impact factor: 6.167

4.  The effects of various spatial distributions of weak noise on rhythmic spiking.

Authors:  Henry C Tuckwell; Jürgen Jost
Journal:  J Comput Neurosci       Date:  2010-07-22       Impact factor: 1.621

5.  Extracting information from the power spectrum of synaptic noise.

Authors:  Alain Destexhe; Michael Rudolph
Journal:  J Comput Neurosci       Date:  2004 Nov-Dec       Impact factor: 1.621

6.  Dynamic gain changes during attentional modulation.

Authors:  Arun P Sripati; Kenneth O Johnson
Journal:  Neural Comput       Date:  2006-08       Impact factor: 2.026

7.  Attentional modulation of firing rate and synchrony in a model cortical network.

Authors:  Calin Buia; Paul Tiesinga
Journal:  J Comput Neurosci       Date:  2006-04-22       Impact factor: 1.621

8.  Spike coding from the perspective of a neurone.

Authors:  G S Bhumbra; R E J Dyball
Journal:  Cogn Process       Date:  2005-08-12

9.  Inhibition of rhythmic neural spiking by noise: the occurrence of a minimum in activity with increasing noise.

Authors:  Boris S Gutkin; Jürgen Jost; Henry C Tuckwell
Journal:  Naturwissenschaften       Date:  2009-06-10

10.  Synaptic depression enables neuronal gain control.

Authors:  Jason S Rothman; Laurence Cathala; Volker Steuber; R Angus Silver
Journal:  Nature       Date:  2009-01-14       Impact factor: 49.962

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