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

Abstract

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.

Noisy Dendrites: Models of Dendritic Integration In Vivo

“…The fi nding that the gain and responsiveness of neurons are enhanced by synaptic noise was fi rst reported from modeling studies (Hô and Destexhe, 2000 ), and then investigated experimentally using dynamicclamp injection of in vivo-like synaptic noise (Destexhe et al, 2001 ;Chance et al, 2002 ;Fellous et al, 2003 ;Prescott and De Koninck, 2003 ;Shu et al, 2003 ;Wolfart et al, 2005 ;Higgs et al, 2006 ;Piwkowska et al, 2008 ), thus confi rming some of the predictions formulated by models. A fascinating possible consequence is that the enhanced responsiveness due to synaptic noise could be used as an attentional mechanism (Hô and Destexhe, 2000 ;Shu et al, 2003 ).…”

“…These quantities are evaluated from the sole knowledge of the V m activity, which makes it applicable to standard intracellular recording conditions. In the past years, each of these methods was tested using computational models, as well as in real neurons using the dynamicclamp technique (reviewed in Piwkowska et al, 2008 ).…”

“…A second, more direct measurement was obtained by using the "VmD method" Piwkowska et al, 2008 ), which estimates the total conductances and their variances by fi tting experimental V m distributions to the Gaussian approximation of an analytical expression for the membrane potential distribution of an effective stochastic membrane model Destexhe, 2003c , 2005 ). This approach leads to estimates of the mean excitatory and inhibitory conductances ( g e0 , g i0 ) and their standard deviations ( σ e , σ i ), respectively, and was successfully applied to intracellular recordings from awake and naturally sleeping cats ( Fig.…”

Extracting synaptic conductances from single membrane potential traces

“…Nevertheless, it is possible to follow a similar maximum-likelihood procedure by explicitly including fast voltagedependent conductances in the model. The VmT method also requires knowledge of the passive parameters of the recorded neuron (G L , C...) which may be difficult to obtain in vivo (see Piwkowska et al, 2008 for a discussion of this point).…”

Intrinsic and Network Contributions to Reverberatory Activity: Reactive Clamp and Modeling Studies

“…We found that the amount of correlations in the inhibitory background inputs (not belonging to the assembly, and modeled by the standard deviation of the inhibitory noise) controls the firing rate of individual cells in the assembly. Recent evidence has highlighted the predominant role of inhibition during reverberatory activity in vitro and in vivo (Piwkowska et al, 2008). The firing of the cells during the reverberation triggers slow intracellular calcium accumulation and activates calcium-dependent potassium currents.…”