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

Cortical neurons are subject to sustained and irregular synaptic activity which causes important fluctuations of the membrane potential (Vm). 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 Vm 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 Vm 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 Vm 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.Comment: 9 figures, Journal of Neuroscience Methods (in press, 2008

Real-time interactions between cortical neurons and computational models (synaptic conductance analysis and digital compensation of electrode artifacts)

Grâce au dynamic-clamp, une technique d électrophysiologie pour l injection en temps réel de conductances artificielles, nous avons validé in vitro des méthodes d extraction des propriétés statistiques des conductances synaptiques à partir du potentiel de membrane de neurones corticaux, et montré que le ratio des variances des conductances excitatrices et inhibitrices déterminait leur contrôle relatif du déclenchement d un potentiel d action. Nous avons aussi développé une nouvelle technique de compensation active (AEC) de la réponse de l électrode intracellulaire au passage du courant, basée sur un modèle numérique de l électrode interagissant en temps réel avec le poste d enregistrement. Nos tests avec des électrodes fines de haute résistance dans des neurones corticaux in vitro montrent une performance améliorée de l AEC par rapport aux méthodes traditionnelles, ce qui permettra d étendre à de nouvelles préparations l application du dynamic-clamp à haute résolution temporellePARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Characterizing neuronal activity by describing the membrane potential as a stochastic process.

International audienceCortical neurons behave similarly to stochastic processes, as a consequence of their irregularity and dense connectivity. Their firing pattern is close to a Poisson process, and their membrane potential (V(m)) is analogous to colored noise. One way to characterize this activity is to identify V(m) to a multidimensional stochastic process. We review here this approach and how it can be used to extract important statistical signatures of neuronal activity. The "VmD method" consists of fitting the V(m) distribution obtained intracellularly to analytic expressions derived from stochastic processes, and thereby deduce synaptic conductance parameters. However, this method requires at least two levels of V(m), which prevents applications to single-trial measurements. We also discuss methods that can be applied to single V(m) traces, such as power spectral analysis and the "STA method" to calculate spike-triggered average conductances based on a maximum likelihood procedure. A recently proposed method, the "VmT method", is based on the fusion of these two concepts. This method is analogous to the VmD method and estimates the mean excitatory and inhibitory conductances and their variances. However, it does so by using a maximum-likelihood estimation, and can thus be applied to single V(m) traces. All methods were tested using controlled conductance injection in dynamic-clamp experiments

Comparison of different neuron models to conductance-based post-stimulus time histograms obtained in cortical pyramidal cells using dynamic-clamp in vitro.

International audienceA wide diversity of models have been proposed to account for the spiking response of central neurons, from the integrate-and-fire (IF) model and its quadratic and exponential variants, to multiple-variable models such as the Izhikevich (IZ) model and the well-known Hodgkin-Huxley (HH) type models. Such models can capture different aspects of the spiking response of neurons, but there is few objective comparison of their performance. In this article, we provide such a comparison in the context of well-defined stimulation protocols, including, for each cell, DC stimulation, and a series of excitatory conductance injections, arising in the presence of synaptic background activity. We use the dynamic-clamp technique to characterize the response of regular-spiking neurons from guinea-pig visual cortex by computing families of post-stimulus time histograms (PSTH), for different stimulus intensities, and for two different background activities (low- and high-conductance states). The data obtained are then used to fit different classes of models such as the IF, IZ, or HH types, which are constrained by the whole data set. This analysis shows that HH models are generally more accurate to fit the series of experimental PSTH, but their performance is almost equaled by much simpler models, such as the exponential or pulse-based IF models. Similar conclusions were also reached by performing partial fitting of the data, and examining the ability of different models to predict responses that were not used for the fitting. Although such results must be qualified by using more sophisticated stimulation protocols, they suggest that nonlinear IF models can capture surprisingly well the response of cortical regular-spiking neurons and appear as useful candidates for network simulations with conductance-based synaptic interactions