Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons

Destexhe, Alain; Rudolph, Michael; Fellous, Jean-Marc; Sejnowski, Terrence J.

doi:10.1016/s0306-4522(01)00344-x

Cited by 604 publications

(708 citation statements)

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“…Therefore, differences in correlations between burst and stimulus attributes seen in vivo and in vitro are most likely not due to differences between bursting seen in vitro and in vivo. We next hypothesized that these differences were due to the massive synaptic bombardment experienced by neurons in vivo (Destexhe et al 2001(Destexhe et al , 2003, which contributes to both increased conductance and variability in spiking activity due to large membrane potential fluctuations (Stein et al 2005). To test this hypothesis, we increased the leak conductance and introduced a noise source that was uncorrelated with the stimulus in our model.…”

Section: Summary Of Resultsmentioning

confidence: 99%

“…Indeed, the intense synaptic bombardment in vivo causes neurons to be in a "high-conductance" state (Destexhe et al 2001(Destexhe et al , 2003. We thus first tested whether increasing the membrane conductance might explain the differences in correlations between burst and stimulus attributes observed in vitro and in vivo in ELL pyramidal cells.…”

Section: Increasing the Input Conductance Preserves Correlations Betwmentioning

confidence: 99%

“…The intense synaptic bombardment in vivo also gives rise to large membrane potential fluctuations (Destexhe et al 2001(Destexhe et al , 2003. These fluctuations cause increased variability in the spiking activity obtained in response to repeated presentations of the same stimulus (Stein et al 2005).…”

Section: Increasing Variability Decreases Correlations Between Burst mentioning

confidence: 99%

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In vivo conditions influence the coding of stimulus features by bursts of action potentials

Akerberg

Chacron

2011

J Comput Neurosci

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The functional role of burst firing (i.e. the firing of packets of action potentials followed by quiescence) in sensory processing is still under debate. Should bursts be considered as unitary events that signal the presence of a particular feature in the sensory environment or is information about stimulus attributes contained within their temporal structure? We compared the coding of stimulus attributes by bursts in vivo and in vitro of electrosensory pyramidal neurons in weakly electric fish by computing correlations between burst and stimulus attributes. Our results show that, while these correlations were strong in magnitude and significant in vitro, they were actually much weaker in magnitude if at all significant in vivo. We used a mathematical model of pyramidal neuron activity in vivo and showed that such a model could reproduce the correlations seen in vitro, thereby suggesting that differences in burst coding were not due to differences in bursting seen in vivo and in vitro. We next tested whether variability in the baseline (i.e. without stimulation) activity of ELL pyramidal neurons could account for these differences. To do so, we injected noise into our model whose intensity was calibrated to mimic baseline activity variability as quantified by the coefficient of variation. We found that this noise caused significant decreases in the magnitude of correlations between burst and stimulus attributes and could account for differences between in vitro and in vivo conditions. We then tested this prediction experimentally by directly injecting noise in vitro through the recording electrode. Our results show that this caused a lowering in magnitude of the correlations between burst and stimulus attributes in vitro and gave rise to values that were quantitatively similar to those seen under in vivo conditions.While it is expected that noise in the form of baseline activity variability will lower correlations between burst and stimulus attributes, our results show that such variability can account for differences seen in vivo. Thus, the high variability seen under in vivo conditions has profound consequences on the coding of information by bursts in ELL pyramidal neurons. In particular, our results support the viewpoint that bursts serve as a detector of particular stimulus features but do not carry detailed information about such features in their structure.

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Section: Summary Of Resultsmentioning

confidence: 99%

Section: Increasing the Input Conductance Preserves Correlations Betwmentioning

confidence: 99%

Section: Increasing Variability Decreases Correlations Between Burst mentioning

confidence: 99%

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In vivo conditions influence the coding of stimulus features by bursts of action potentials

Akerberg

Chacron

2011

J Comput Neurosci

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“…2D) were integrated into a single-compartment model with Hodgkin-Huxley kinetics . The type of model considered was the point-conductance model (Destexhe et al, 2001), which consists in a single-compartment neuron subject to fluctuating conductances:…”

Section: Single-cell Model Of Activated Statesmentioning

confidence: 99%

“…g e (t) and g i (t) are stochastic excitatory and inhibitory conductances, with respective reversal potentials E e and E i . These synaptic conductances were described by the following OrnsteinUhlenbeck model (Destexhe et al, 2001):…”

Section: Single-cell Model Of Activated Statesmentioning

confidence: 99%

Activated cortical states: Experiments, analyses and models

Boustani

Pospischil

Rudolph-Lilith

et al. 2007

Journal of Physiology-Paris

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In awake animals, the cerebral cortex displays an ''activated'' state, with distinct characteristics compared to other states like slow-wave sleep or anesthesia. These characteristics include a sustained depolarized membrane potential (V m ) and irregular firing activity. In the present paper, we evaluate our understanding of cortical activated states from a computational neuroscience point of view. We start by reviewing the electrophysiological characteristics of activated cortical states based on recordings and analysis performed in awake cat association cortex. These analyses show that cortical activity is characterized by an apparent Poisson-distributed stochastic dynamics, both at the single-cell and population levels, and that single cells display a high-conductance state dominated by inhibition. We next overview computational models of the ''awake'' cortex, and perform the same analyses as in the experiments. Many properties identified experimentally are indeed reproduced by models, such as depolarized V m , irregular firing with apparent Poisson statistics, and the determinant role of inhibitory fluctuations on spiking. However, other features are not well reproduced, such as firing statistics and the conductance state of the membrane, suggesting that the network state displayed by models is not entirely correct. We also show how networks can approach a correct conductance state, suggesting ways by which future models will generate activity fully consistent with experimental data.

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