Literature DB >> 23197724

Channel noise from both slow adaptation currents and fast currents is required to explain spike-response variability in a sensory neuron.

Karin Fisch1, Tilo Schwalger, Benjamin Lindner, Andreas V M Herz, Jan Benda.   

Abstract

Spike-timing variability has a large effect on neural information processing. However, for many systems little is known about the noise sources causing the spike-response variability. Here we investigate potential sources of spike-response variability in auditory receptor neurons of locusts, a classic insect model system. At low-spike frequencies, our data show negative interspike-interval (ISI) correlations and ISI distributions that match the inverse Gaussian distribution. These findings can be explained by a white-noise source that interacts with an adaptation current. At higher spike frequencies, more strongly peaked distributions and positive ISI correlations appear, as expected from a canonical model of suprathreshold firing driven by temporally correlated (i.e., colored) noise. Simulations of a minimal conductance-based model of the auditory receptor neuron with stochastic ion channels exclude the delayed rectifier as a possible noise source. Our analysis suggests channel noise from an adaptation current and the receptor or sodium current as main sources for the colored and white noise, respectively. By comparing the ISI statistics with generic models, we find strong evidence for two distinct noise sources. Our approach does not involve any dendritic or somatic recordings that may harm the delicate workings of many sensory systems. It could be applied to various other types of neurons, in which channel noise dominates the fluctuations that shape the neuron's spike statistics.

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Year:  2012        PMID: 23197724      PMCID: PMC6621841          DOI: 10.1523/JNEUROSCI.6231-11.2012

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  51 in total

Review 1.  Detecting and estimating signals in noisy cable structure, I: neuronal noise sources.

Authors:  A Manwani; C Koch
Journal:  Neural Comput       Date:  1999-11-15       Impact factor: 2.026

Review 2.  Channel noise in neurons.

Authors:  J A White; J T Rubinstein; A R Kay
Journal:  Trends Neurosci       Date:  2000-03       Impact factor: 13.837

3.  Putting ion channels to work: mechanoelectrical transduction, adaptation, and amplification by hair cells.

Authors:  A J Hudspeth; Y Choe; A D Mehta; P Martin
Journal:  Proc Natl Acad Sci U S A       Date:  2000-10-24       Impact factor: 11.205

4.  Low response variability in simultaneously recorded retinal, thalamic, and cortical neurons.

Authors:  P Kara; P Reinagel; R C Reid
Journal:  Neuron       Date:  2000-09       Impact factor: 17.173

5.  Robustness and variability of neuronal coding by amplitude-sensitive afferents in the weakly electric fish eigenmannia.

Authors:  G Kreiman; R Krahe; W Metzner; C Koch; F Gabbiani
Journal:  J Neurophysiol       Date:  2000-07       Impact factor: 2.714

6.  Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron.

Authors:  Y H Liu; X J Wang
Journal:  J Comput Neurosci       Date:  2001 Jan-Feb       Impact factor: 1.621

7.  Nonrenewal statistics of electrosensory afferent spike trains: implications for the detection of weak sensory signals.

Authors:  R Ratnam; M E Nelson
Journal:  J Neurosci       Date:  2000-09-01       Impact factor: 6.167

8.  Suprathreshold stochastic firing dynamics with memory in P-type electroreceptors.

Authors:  M J Chacron; A Longtin; M St-Hilaire; L Maler
Journal:  Phys Rev Lett       Date:  2000-08-14       Impact factor: 9.161

Review 9.  Calcium-activated potassium currents in mammalian neurons.

Authors:  P Sah; P Davies
Journal:  Clin Exp Pharmacol Physiol       Date:  2000-09       Impact factor: 2.557

10.  Representation of acoustic communication signals by insect auditory receptor neurons.

Authors:  C K Machens; M B Stemmler; P Prinz; R Krahe; B Ronacher; A V Herz
Journal:  J Neurosci       Date:  2001-05-01       Impact factor: 6.167

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  23 in total

1.  Resolving molecular contributions of ion channel noise to interspike interval variability through stochastic shielding.

Authors:  Shusen Pu; Peter J Thomas
Journal:  Biol Cybern       Date:  2021-05-22       Impact factor: 2.086

2.  Interspike interval correlation in a stochastic exponential integrate-and-fire model with subthreshold and spike-triggered adaptation.

Authors:  LieJune Shiau; Tilo Schwalger; Benjamin Lindner
Journal:  J Comput Neurosci       Date:  2015-04-19       Impact factor: 1.621

3.  Statistical structure of neural spiking under non-Poissonian or other non-white stimulation.

Authors:  Tilo Schwalger; Felix Droste; Benjamin Lindner
Journal:  J Comput Neurosci       Date:  2015-05-05       Impact factor: 1.621

4.  A temperature rise reduces trial-to-trial variability of locust auditory neuron responses.

Authors:  Monika J B Eberhard; Jan-Hendrik Schleimer; Susanne Schreiber; Bernhard Ronacher
Journal:  J Neurophysiol       Date:  2015-06-03       Impact factor: 2.714

5.  Stochastic representations of ion channel kinetics and exact stochastic simulation of neuronal dynamics.

Authors:  David F Anderson; Bard Ermentrout; Peter J Thomas
Journal:  J Comput Neurosci       Date:  2014-11-19       Impact factor: 1.621

Review 6.  Computational themes of peripheral processing in the auditory pathway of insects.

Authors:  K Jannis Hildebrandt; Jan Benda; R Matthias Hennig
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2014-10-31       Impact factor: 1.836

7.  Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.

Authors:  Tilo Schwalger; Moritz Deger; Wulfram Gerstner
Journal:  PLoS Comput Biol       Date:  2017-04-19       Impact factor: 4.475

8.  Stochastic slowly adapting ionic currents may provide a decorrelation mechanism for neural oscillators by causing wander in the intrinsic period.

Authors:  Sharon E Norman; Robert J Butera; Carmen C Canavier
Journal:  J Neurophysiol       Date:  2016-06-08       Impact factor: 2.714

9.  Coding of time-dependent stimuli in homogeneous and heterogeneous neural populations.

Authors:  Manuel Beiran; Alexandra Kruscha; Jan Benda; Benjamin Lindner
Journal:  J Comput Neurosci       Date:  2017-12-08       Impact factor: 1.621

10.  A dynamic spike threshold with correlated noise predicts observed patterns of negative interval correlations in neuronal spike trains.

Authors:  Robin S Sidhu; Erik C Johnson; Douglas L Jones; Rama Ratnam
Journal:  Biol Cybern       Date:  2022-10-16       Impact factor: 3.072

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