We review here the development of Hodgkin-Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are "fast spiking", "regular spiking", "intrinsically bursting" and "low-threshold spike" cells. For each class, we fit "minimal" HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.

In neocortical neurons, network activity can activate a large number of synaptic inputs, resulting in highly irregular subthreshold membrane potential (V(m)) fluctuations, commonly called "synaptic noise." This activity contains information about the underlying network dynamics, but it is not easy to extract network properties from such complex and irregular activity. Here, we propose a method to estimate properties of network activity from intracellular recordings and test this method using theoretical and experimental approaches. The method is based on the analytic expression of the subthreshold V(m) distribution at steady state in conductance-based models. Fitting this analytic expression to V(m) distributions obtained from intracellular recordings provides estimates of the mean and variance of excitatory and inhibitory conductances. We test the accuracy of these estimates against computational models of increasing complexity. We also test the method using dynamic-clamp recordings of neocortical neurons in vitro. By using an on-line analysis procedure, we show that the measured conductances from spontaneous network activity can be used to re-create artificial states equivalent to real network activity. This approach should be applicable to intracellular recordings during different network states in vivo, providing a characterization of the global properties of synaptic conductances and possible insight into the underlying network mechanisms.

Intracellular recordings of neuronal membrane potential are a central tool in neurophysiology. In many situations, especially in vivo, the traditional limitation of such recordings is the high electrode resistance and capacitance, which may cause significant measurement errors during current injection. We introduce a computer-aided technique, Active Electrode Compensation (AEC), based on a digital model of the electrode interfaced in real time with the electrophysiological setup. The characteristics of this model are first estimated using white noise current injection. The electrode and membrane contribution are digitally separated, and the recording is then made by online subtraction of the electrode contribution. Tests performed in vitro and in vivo demonstrate that AEC enables high-frequency recordings in demanding conditions, such as injection of conductance noise in dynamic-clamp mode, not feasible with a single high-resistance electrode until now. AEC should be particularly useful to characterize fast neuronal phenomena intracellularly in vivo.

SummaryDriving perception by direct activation of neural ensembles in cortex is a necessary step for achieving a causal understanding of the neural code for auditory perception and developing central sensory rehabilitation methods. Here, using optogenetic manipulations during an auditory discrimination task in mice, we show that auditory cortex can be short-circuited by coarser pathways for simple sound identification. Yet when the sensory decision becomes more complex, involving temporal integration of information, auditory cortex activity is required for sound discrimination and targeted activation of specific cortical ensembles changes perceptual decisions, as predicted by our readout of the cortical code. Hence, auditory cortex representations contribute to sound discriminations by refining decisions from parallel routes.

No 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.

The optimal patterns of synaptic conductances for spike generation in central neurons is a subject of considerable interest. Ideally, such conductance time courses should be extracted from membrane potential (V m ) activity, but this is difficult because the nonlinear contribution of conductances to the V m renders their estimation from the membrane equation extremely sensitive. We outline here a solution to this problem based on a discretization of the time axis. This procedure can extract the time course of excitatory and inhibitory conductances solely from the analysis of V m activity. We test this method by calculating spike-triggered averages of synaptic conductances using numerical simulations of the integrate-and-fire model subject to colored conductance noise. The procedure was also tested successfully in biological cortical neurons using conductance noise injected with dynamic-clamp. This method should allow the extraction of synaptic conductances from V m recordings in vivo.

We present a new way to model the response of an electrode to an injected current. The electrode is represented by an unknown complex linear circuit, characterized by a kernel which we determine by injecting a noisy current. We show both in simulations and experiments that, when applied to a full recording setup (including acquisition board and amplifier), the method captures not only the characteristics of the electrode, but also those of all the devices between the computer and the tip of the electrode, including filters and the capacitance neutralization circuit on the amplifier. Simulations show that the method allows correct predictions of the response of complex electrode models. Finally, we successfully apply the technique to challenging intracellular recording situations in which the voltage across the electrode during injection needs to be subtracted from the recording, in particular conductance injection with the dynamic clamp protocol. We show in numerical simulations and confirm with experiments that the method performs well in cases when both bridge recording and recording in discontinuous mode ( Modelling the electrodeWhen recording intracellularly with a single electrode and injecting current at the same time, the recorded potential iswhere V m is the membrane potential (which is the variable we are interested in) and U e is the voltage across the electrode. As a first approximation, the electrode acts as a resistance: U e % R e I, where R e is the electrode resistance and I is the injected current. Thus a first estimation, known as bridge compensation, is V m % V r À R e I. However, this is too crude an approximation because the electrode has a non-zero charge time and is better modelled by a RC circuit (resistance þ capacitance). Amplifiers include a capacitance neutralization circuit which amounts to inserting a negative capacitance in the circuit [8].Again, this model is too simple because there is always a residual capacitance, which appears as capacitive transients in recorded responses to pulses. These transients can cause serious problems in some situations such as dynamic clamp injection of conductances (injection of current of the form IðtÞ ¼ gðtÞðE À V m ðtÞÞ [6,7]), because transients are injected back and can destabilize the system. Therefore we defined a more complex model of an electrode, by assuming that the electrode can be seen as an arbitrarily complex circuit of resistances and capacitances, which can be represented by a linear time-invariant filter, i.e., the response of the electrode to a current IðtÞ is expressed as a convolution:where K e ðÁÞ is named the electrode kernel. The technique consists in identifying the electrode kernel by observing the response of the electrode to a known noisy current. In practice, the electrode kernel can only be estimated when the electrode is impaled into the neuron (because electrode properties change after impalement). In this case, we first ARTICLE IN PRESS www.elsevier.com/locate/neucom 0925-2312/$ -see front matter r