Thierry Bal

Feedback inhibition controls spike transfer in hybrid thalamic circuits

Sensory information reaches the cerebral cortex through the thalamus, which differentially relays this input depending on the state of arousal. Such ‘gating’ involves inhibition of the thalamocortical relay neurons by the reticular nucleus of the thalamus, but the underlying mechanisms are poorly understood. We reconstructed the thalamocortical circuit as an artificial and biological hybrid network in vitro. With visual input simulated as retinal cell activity, we show here that when the gain in the thalamic inhibitory feedback loop is greater than a critical value, the circuit tends towards oscillations—and thus imposes a temporal decorrelation of retinal cell input and thalamic relay output. This results in the functional disconnection of the cortex from the sensory drive, a feature typical of sleep states. Conversely, low gain in the feedback inhibition and the action of noradrenaline, a known modulator of arousal, converge to increase input–output correlation in relay neurons. Combining gain control of feedback inhibition and modulation of membrane excitability thus enables thalamic circuits to finely tune the gating of spike transmission from sensory organs to the cortex.

Synaptic background activity controls spike transfer from thalamus to cortex.

Characterizing the responsiveness of thalamic neurons is crucial to understanding the flow of sensory information. Typically, thalamocortical neurons possess two distinct firing modes. At depolarized membrane potentials, thalamic cells fire single action potentials and faithfully relay synaptic inputs to the cortex. At hyperpolarized potentials, the activation of T-type calcium channels promotes burst firing, and the transfer is less accurate. Our results suggest that this duality no longer holds if synaptic background activity is taken into account. By injecting stochastic conductances into guinea-pig thalamocortical neurons in slices, we show that the transfer function of these neurons is strongly influenced by conductance noise. The combination of synaptic noise with intrinsic properties gives a global responsiveness that is more linear, mixing single-spike and burst responses at all membrane potentials. Because in thalamic neurons, background synaptic input originates mainly from cortex, these results support a determinant role of corticothalamic feedback during sensory information processing.

Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons

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.

High-Resolution Intracellular Recordings Using a Real-Time Computational Model of the Electrode

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.

Network-state modulation of power-law frequency-scaling in visual cortical neurons.

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI.

T-Type Calcium Channels Consolidate Tonic Action Potential Output of Thalamic Neurons to Neocortex

The thalamic output during different behavioral states is strictly controlled by the firing modes of thalamocortical neurons. During sleep, their hyperpolarized membrane potential allows activation of the T-type calcium channels, promoting rhythmic high-frequency burst firing that reduces sensory information transfer. In contrast, in the waking state thalamic neurons mostly exhibit action potentials at low frequency (i.e., tonic firing), enabling the reliable transfer of incoming sensory inputs to cortex. Because of their nearly complete inactivation at the depolarized potentials that are experienced during the wake state, T-channels are not believed to modulate tonic action potential discharges. Here, we demonstrate using mice brain slices that activation of T-channels in thalamocortical neurons maintained in the depolarized/wake-like state is critical for the reliable expression of tonic firing, securing their excitability over changes in membrane potential that occur in the depolarized state. Our results establish a novel mechanism for the integration of sensory information by thalamocortical neurons and point to an unexpected role for T-channels in the early stage of information processing.

Minimal alterations in T-type calcium channel gating markedly modify physiological firing dynamics

Non‐technical summaryu2002 Voltage‐dependant calcium channels constitute a heterogeneous group playing ubiquitous roles in excitable cells. Among them the low‐voltage activated T‐type channels generate a family of currents that differ in their biophysical properties reflecting structural or neuromodulatory diversity. These T‐type calcium channels are highly expressed in neurons located in the thalamus, a brain structure considered as the gateway to the cortex. Thalamic T‐type calcium channels are critically involved in oscillatory neuronal activities associated with sleep or epilepsy and may contribute to sensory processing. Using injections of computer‐simulated T‐type conductances (a real time mimicry of ionic currents) in biological thalamic neurons, we dissect how the diversity in T‐type currents impact on the output of thalamic neurons. We show that very subtle modifications in the properties of the T current that were overlooked so far affect drastically the physiological output of the thalamic neurons and therefore condition the dynamics of thalamo‐cortical information integration.

Analog circuits for modeling biological neural networks: design and applications

Computational neuroscience is emerging as a new approach in biological neural networks studies. In an attempt to contribute to this field, the authors present here a modeling work based on the implementation of biological neurons using specific analog integrated circuits. They first describe the mathematical basis of such models, then present analog emulations of different neurons. Each model is compared to its biological real counterpart as well as its numerical computation. Finally, the authors demonstrate the possible use of these analog models to interact dynamically with real cells through artificial synapses within hybrid networks. This method is currently used to explore neural networks dynamics.

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 (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.

Hardware computation of conductance-based neuron models

We review different applications of silicon conductance-based neuron models implemented on analog circuits. At the single-cell level, we describe a circuit in which conductances are programmed to simulate various Hodgkin-Huxley type models; integrated in a hardware/software system, they provide a simulation tool; an illustrative example is the simulation of bursting neurons of the thalamus. At the network level, we present a mixed analog-digital architecture, where the connectivity and the plasticity rules are implemented digitally and are therefore very flexible. These circuits provide valuable tools for real-time simulations, including hybrid applications where single-neuron or network models are interfaced with biological cells.