Cited by 273Neural dynamics and brain functionNeuroscience and Neuropharmacology Researchstochastic dynamics and bifurcation

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Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity

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Fellous, J.-M, Rudolph, M, Destexhe, A, Sejnowski, T.J·2003·Neuroscience

DOI 10.1016/j.neuroscience.2003.08.027PMC PMC2928821

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Abstract

In vivo, in vitro and computational studies were used to investigate the impact of the synaptic background activity observed in neocortical neurons in vivo. We simulated background activity in vitro using two stochastic Ornstein-Uhlenbeck processes describing glutamatergic and GABAergic synaptic conductances, which were injected into a cell in real time using the dynamic clamp technique. With parameters chosen to mimic in vivo conditions, layer 5 rat prefrontal cortex cells recorded in vitro were depolarized by about 15 mV, their membrane fluctuated with a S.D. of about 4 mV, their input resistances decreased five-fold, their spontaneous firing had a high coefficient of variation and an average firing rate of about 5-10 Hz. Brief changes in the variance of the alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) synaptic conductance fluctuations induced time-locked spiking without significantly changing the average membrane potential of the cell. These transients mimicked increases in the correlation of excitatory inputs. Background activity was highly effective in modulating the firing-rate/current curve of the cell: the variance of the simulated gamma-aminobutyric acid (GABA) and AMPA conductances individually set the input/output gain, the mean excitatory and inhibitory conductances set the working point, and the mean inhibitory conductance controlled the input resistance. An average ratio of inhibitory to excitatory mean conductances close to 4 was optimal in generating membrane potential fluctuations with high coefficients of variation. We conclude that background synaptic activity can dynamically modulate the input/output properties of individual neocortical neurons in vivo.

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A Simplex Method for Function Minimization
Nelder, J. A., & Mead, R. (1965) · The Computer Journal · doi:10.1093/comjnl/7.4.308 · Cited by 28,905
On the Theory of the Brownian Motion
Uhlenbeck, G. E., & Ornstein, L. S. (1930) · Physical Review · doi:10.1103/physrev.36.823 · Cited by 4,713
Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention
Fries, P., Reynolds, J. H., Rorie, A. E., & Desimone, R. (2001) · Science · doi:10.1126/science.1055465 · Cited by 2,786
The NEURON Simulation Environment
Hines, M. L., & Carnevale, N. T. (1997) · Neural Computation · doi:10.1162/neco.1997.9.6.1179 · Cited by 2,630
Single-Channel Recording
Sakmann, B. J., & Neher, E. (1995) · Springer eBooks · doi:10.1007/978-1-4419-1229-9 · Cited by 2,543
The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding
Shadlen, M. N., & Newsome, W. T. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-10-03870.1998 · Cited by 2,178
Reliability of spike timing in neocortical neurons
Mainen, Z. F., & Sejnowski, T. J. (1995) · Science · doi:10.1126/science.7770778 · Cited by 1,950
A novel slow (< 1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components
Steriade, M., Nunez, A., & Amzica, F. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-08-03252.1993 · Cited by 1,903
The highly irregular firing of cortical cells is inconsistent with temporal integration of random epsps.
Softky, W., & Koch, C. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00334.1993 · Cited by 1,689
Theory of stochastic resonance
McNamara, B., & Wiesenfeld, K. A. (1989) · Physical Review A · doi:10.1103/physreva.39.4854 · Cited by 1,554

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Authors: Fellous, J.-M., Rudolph, M., Destexhe, A., & Sejnowski, T. J.
Affiliations: Salk Institute for Biological Studies (US)
Centre National de la Recherche Scientifique (FR)
Corresponding author: Fellous, J.-M
ORCID: Fellous, J.-M
Venue: Neuroscience
Publisher: Elsevier BV
Published: 2003-01
Volume: 122
Issue: 3
Pages: 811-829
ISSN: 0306-4522 (ISSN-L)
DOI: 10.1016/j.neuroscience.2003.08.027
PMC: PMC2928821
OpenAlex: W2125421120
Cited by: 273
References: 72
Citations by year: 2026: 5 · 2025: 11 · 2024: 6 · 2023: 7 · 2022: 9 · 2021: 4 · 2020: 7 · 2019: 10 · 2018: 9 · 2017: 11 · 2016: 7 · 2015: 10 · 2014: 10 · 2013: 15 · 2012: 16
FWCI: 7.98
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Neuroscience (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
HAL (submittedVersion)
Funders: Howard Hughes Medical Institute
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
Keywords: neocortical neurons, prefrontal cortex, layer 5 neurons, Ornstein-Uhlenbeck process, input resistance, firing rate, membrane potential fluctuations, dynamic clamp
MeSH terms: Animals, Computer Simulation, Membrane Potentials, Models, Neurological, Neural Conduction, Neural Inhibition, Synaptic Transmission, Neurons, Probability, Synapses, Time Factors, Electric Impedance, Rats, Sprague-Dawley, Patch-Clamp Techniques, Neocortex, Rats, In Vitro Techniques

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Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity

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