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Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity
Open accessAbstract
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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“Unlike synaptically driven Vm fluctuations, changes in membrane conductance and depolarization cause lateral, or additive, shifts in the I/O transfer function of a neuron without changes in neuronal gain57,58,60,61,83,84.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [83]
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“Together, temporally coincident changes in conductance state and synaptic fluctuations exert a powerful effect on neuronal gain at the single-cell level57,58,60,61,83,84.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [83]
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Metadata
- 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)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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