In vitro and in vivo measures of evoked excitatory and inhibitory conductance dynamics in sensory cortices

Monier, Cyril; Fournier, Julien; Frégnac, Yves

doi:10.1016/j.jneumeth.2007.11.008

Cited by 142 publications

(199 citation statements)

References 117 publications

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“…Our study indeed predicts that the reliability should be modulated by the stimulus statistics, and this has been observed experimentally: intracellular studies demonstrate that the reliability of subthreshold activity is increased during natural scene viewing (Baudot et al, 2004;Frégnac et al, 2005) or when the stimulus evokes strong shunting inhibition (Monier et al, 2008).…”

Section: Relation To Experimental Datasupporting

confidence: 74%

Reliable Recall of Spontaneous Activity Patterns in Cortical Networks

Marre¹,

Yger²,

Davison³

et al. 2009

J. Neurosci.

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Irregular ongoing activity in cortical networks is often modeled as arising from recurrent connectivity. Yet it remains unclear to what extent its presence corrupts sensory signal transmission and network computational capabilities. In a recurrent cortical-like network, we have determined the activity patterns that are better transmitted and self-sustained by the network. We show that reproducible spiking and subthreshold dynamics can be triggered if the statistics of the imposed external drive are consistent with patterns previously seen in the ongoing activity. A subset of neurons in the network, constrained to replay temporal pattern segments extracted from the recorded ongoing activity of the same network, reliably drives the remaining, free-running neurons to call the rest of the pattern. Comparison with surrogate Poisson patterns indicates that the efficiency of the recall and completion process depends on the similarity between the statistical properties of the input with previous ongoing activity The reliability of evoked dynamics in recurrent networks is thus dependent on the stimulus used, and we propose that the similarity between spontaneous and evoked activity in sensory cortical areas could be a signature of efficient transmission and propagation across cortical networks.

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Section: Relation To Experimental Datasupporting

confidence: 74%

Reliable Recall of Spontaneous Activity Patterns in Cortical Networks

Marre¹,

Yger²,

Davison³

et al. 2009

J. Neurosci.

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“…Synaptic conductance variances are useful parameters that can be related to spike initiation (see Sections 3.3 and 3.4). The VmD method provided the first published estimates for these parameters in vivo (for a different approach, see Monier et al, 2008, this issue).…”

Section: Analysis Of Intracellular Recordings Of Cortical Neurons In mentioning

confidence: 99%

“…To our knowledge, the two reviewed studies using the VmD method are the only ones (together with Monier et al, 2008, this issue) explicitely providing estimates of synaptic conductance variances. The application of the probabilistic method also allowed for the first time the extraction of synaptic conductance STAs from in vivo recordings of spontaneous V m fluctuations in awake and naturally sleeping cats , and led to the observation of both types of firing regimes described above -average total conductance increase and average total conductance drop -with a majority of cases displaying the inhibition-dominated, conductance-drop pattern.…”

Section: Patterns Of Excitation and Inhibition Triggering Spikes Depementioning

confidence: 99%

Characterizing synaptic conductance fluctuations in cortical neurons and their influence on spike generation

Piwkowska

Pospischil

Brette

et al. 2008

Journal of Neuroscience Methods

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Cortical neurons are subject to sustained and irregular synaptic activity which causes important fluctuations of the membrane potential (V m ). We review here different methods to characterize this activity and its impact on spike generation. The simplified, fluctuating point-conductance model of synaptic activity provides the starting point of a variety of methods for the analysis of intracellular V m recordings. In this model, the synaptic excitatory and inhibitory conductances are described by Gaussian-distributed stochastic variables, or "colored conductance noise". The matching of experimentally recorded V m distributions to an invertible theoretical expression derived from the model allows the extraction of parameters characterizing the synaptic conductance distributions. This analysis can be complemented by the matching of experimental V m power spectral densities (PSDs) to a theoretical template, even though the unexpected scaling properties of experimental PSDs limit the precision of this latter approach. Building on this stochastic characterization of synaptic activity, we also propose methods to qualitatively and quantitatively evaluate spike-triggered averages of synaptic time-courses preceding spikes. This analysis points to an essential role for synaptic conductance variance in determining spike times. The presented methods are evaluated using controlled conductance injection in cortical neurons in vitro with the dynamic-clamp technique. We review their applications to the analysis of in vivo intracellular recordings in cat association cortex, which suggest a predominant role for inhibition in determining both sub-and supra-threshold dynamics of cortical neurons embedded in active networks.

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“…Indeed, the model can reproduce the main response characteristics of firstorder thalamo-cortical neurons in layer 4 of cat V1. During drifting gratings, excitatory and inhibitory conductances of cortical neurons were anti-correlated [6,7], such that excitation can be freely integrated and induce multiple spikes. In contrast, during natural scenes the conductances were correlated, with inhibition lagging by few milliseconds [1,8].…”

Section: Introductionmentioning

confidence: 99%

Control of the temporal interplay between excitation and inhibition by the statistics of visual input

et al. 2009

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IntroductionIn the primary visual cortex (V1), single cell responses to simple visual stimuli (drifting gratings) are usually strong but with a high trial-by-trial variability. In contrast, when exposed to full field natural scenes with simulated eye movements, the firing patterns of these neurons are sparse but highly reproducible over trials [1]. So far the mechanisms behind these two distinct different response behaviours are not yet fully understood. Different mechanisms are candidates for controlling spike timing precision and models are needed to clarify their respective contribution, which may be of thalamic or intracortical origin. As a first step, we investigated which aspects of the neuronal dynamics can be explained by balanced feedforward excitation and inhibition and its dependency upon the spatiotemporal statistics of the different stimuli. We built a simple model of the early visual system (LGN, V1). The thalamocortical connectivity was similar to the gpush-pullh architecture used in [2], with additional depressing thalamocortical synapses [3]. The model was written in PyNN [4] using NEST [5] as simulator. Indeed, the model can reproduce the main response characteristics of firstorder thalamo-cortical neurons in layer 4 of cat V1. During drifting gratings, excitatory and inhibitory conductances of cortical neurons were anti-correlated [6,7], such that excitation can be freely integrated and induce multiple spikes. In contrast, during natural scenes the conductances were correlated, with inhibition lagging by few milliseconds [1,8]. This small lag between excitation and inhibition induces a strong selectivity to synchronous inputs, with a consequence that the responses became sparse and precise. However, some key aspects of the in vivo recordings in cat area V1 cannot be explained, such as selective reduction of stimulus-locked subthreshold noise during natural scene viewing, precise firing during fixational eye-movements and center-surround non-linearities, opening the door for future investigation about the role of intra-cortical recurrent connectivity in further shaping the neuronal responses to natural images. In conclusion, our study points that correlated inhibition can explain, at least in part, sparse and reliable spiking activity as observed in response to natural scenes. This is consistent with its role reported from other sensory modalities and cortical areas [8]. Thus correlated excitation and inhibition could be a general mechanism to induce sparse and precise spiking in the nervous system.

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In vitro and in vivo measures of evoked excitatory and inhibitory conductance dynamics in sensory cortices

Cited by 142 publications

References 117 publications

Reliable Recall of Spontaneous Activity Patterns in Cortical Networks

Reliable Recall of Spontaneous Activity Patterns in Cortical Networks

Characterizing synaptic conductance fluctuations in cortical neurons and their influence on spike generation

Control of the temporal interplay between excitation and inhibition by the statistics of visual input

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