Inhibition Determines Membrane Potential Dynamics and Controls Action Potential Generation in Awake and Sleeping Cat Cortex

Rudolph, Michelle; Pospischil, Martin; Timofeev, Igor; Destexhe, Alain

doi:10.1523/jneurosci.4652-06.2007

Cited by 229 publications

(293 citation statements)

References 36 publications

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“…This simulation (Fig. 4, Awake), allows us to verify that these conductance measurements led the model to generate V m activity in excellent agreement with the intracellular recordings (see details in Rudolph et al, 2007). Similarly, integrating conductance measurements during up-states (not shown) generated V m activity consistent with the up-down-state transitions seen experimentally (Fig.…”

Section: Single-cell Model Of Activated Statessupporting

confidence: 74%

“…These features were confirmed recently by relating those conductance measurements to a spike-triggered average (STA) conductance analysis Rudolph et al, 2007;Piwkowska et al, in press). It was found that the main synaptic conductance pattern associated to spikes in cortical neurons was a decrease of total conductance, which was due to a decrease of inhibitory conductance (Fig.…”

Section: Experimental Characterization Of Activated Cortical Statesmentioning

confidence: 54%

“…The values obtained by VmD analysis of intracellular recordings in awake cats ( Fig. 2D; Rudolph et al, 2007) were integrated in this model. This simulation (Fig.…”

Section: Single-cell Model Of Activated Statesmentioning

confidence: 99%

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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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Section: Single-cell Model Of Activated Statessupporting

confidence: 74%

Section: Experimental Characterization Of Activated Cortical Statesmentioning

confidence: 54%

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Activated cortical states: Experiments, analyses and models

Boustani

Pospischil

Rudolph-Lilith

et al. 2007

Journal of Physiology-Paris

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“…The repeated inhibitory conductance led to a higher precision than the repeated excitatory conductance. To find out what type of input most effectively drives the cell in vivo, a reverse correlation analysis 113 was performed on the inhibitory and excitatory conductance waveforms separately 114 . This showed that spikes of neurons in the association cortex of the cat were on average preceded by a reduction in inhibition.…”

Section: Inhibition Can Modulate Firing Rate and Influence Spike Timesmentioning

confidence: 99%

Regulation of spike timing in visual cortical circuits

2008

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A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.Reliability and precision are two different quantities. When you make an appointment with your friend, she can either keep the appointment or not show up at all. If she does show up, she might or might not be on time. The former uncertainty is related to reliability, whereas the latter is related to precision. When the same stimulus waveform is repeatedly injected at the soma of a neuron in vitro (FIG. 1a), a similar spike train is obtained on each trial 1,2 (FIG. 1b). When approximately the same number of spikes occur on each trial the neuron is said to be reliable, whereas when the spikes occur almost at the same time across trials it is said to be precise (FIG. 1c). For a single neuron, the potential information content of precise and reliable spike times is many times larger than that which is contained in the firing rate, which is averaged across a typical interval of a hundred milliseconds [3][4][5][6] . The information contained in spike timing is available immediately, rather than after an averaging period. Furthermore, the timing of patterns of spikes can potentially transmit even more information than the timing of the individual constituent spikes 3,7 . The potential relevance of spike patterns becomes apparent when we consider neurons at the population level: when a group of similar neurons (a 'pool') produces precise and reliable spike trains, the neurons they project to receive volleys of synchronous spikes 8,9 . This opens up the possibility of communicating between different cortical areas through synchronous spike volleys.In contrast to the in vitro situation described above, in the intact cortex most excitatory synaptic inputs arrive at the dendrites rather than at the soma (FIG. 1d), and synaptic transmission is typically unreliable [10][11][12][13] . Furthermore, most of these dendritic inputs are not directly related to ongoing sensory stimulation; rather, they reflect spatiotemporally structured internal activity. Therefore, when the same stimulus is presented repeatedly, the resulting spike trains are usually neither precise nor reliable when they are aligned to the stimulus onset 6 . Instead, HHMI Author ManuscriptHHMI Author Manuscript HHMI Author Manuscript neural activity in vivo might be dominated by internally generated complex reverberations or rhythmic oscillations, and precise and reliable sp...

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“…These tests confirmed the weakness of the method in the low-inhibition regime, but also revealed a good performance of the VmT method during HC states. Since in cortical neurons in vivo the membrane potential is usually well above the inhibitory reversal potential, and since inhibitory conductances tend to dominate over excitatory ones (Borg-Graham et al, 1998;Rudolph et al, 2007; note that dendritic recordings may be dominated by excitatory inputs), we anticipate that this method should provide good conductance estimates from in vivo recordings. Gaussian white noise was added to the voltage trace of the model, and the VmT method was applied to the V m trace obtained with noise, to yield estimates of conductances and variances.…”

Section: Discussionmentioning

confidence: 99%

Extracting synaptic conductances from single membrane potential traces

Pospischil

Piwkowska²,

Bal³

et al. 2009

Neuroscience

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In awake animals, the activity of the cerebral cortex is highly complex, with neurons firing irregularly with apparent Poisson statistics. One way to characterize this complexity is to take advantage of the high interconnectivity of cerebral cortex and use intracellular recordings of cortical neurons, which contain information about the activity of thousands of other cortical neurons. Identifying the membrane potential (V m ) to a stochastic process enables the extraction of important statistical signatures of this complex synaptic activity. Typically, one estimates the total synaptic conductances (excitatory and inhibitory) but this type of estimation requires at least two V m levels and therefore cannot be applied to single V m traces. We propose here a method to extract excitatory and inhibitory conductances (mean and variance) from single V m traces. This "VmT method" estimates conductance parameters using maximum likelihood criteria, under the assumption that synaptic conductances are described by Gaussian stochastic processes and are integrated by a passive leaky membrane. The method is illustrated using models and is tested on guinea-pig visual cortex neurons in vitro using dynamic-clamp experiments. The VmT method holds promises for extracting conductances from single-trial measurements, which has a high potential for in vivo applications.

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Inhibition Determines Membrane Potential Dynamics and Controls Action Potential Generation in Awake and Sleeping Cat Cortex

Cited by 229 publications

References 36 publications

Activated cortical states: Experiments, analyses and models

Activated cortical states: Experiments, analyses and models

Regulation of spike timing in visual cortical circuits

Extracting synaptic conductances from single membrane potential traces

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