Biomedical subjects

Thierry Bal

Publications and source records attributed to Thierry Bal.

8 recordsLinked to original sources

PubMed2006

Calculating event-triggered average synaptic conductances from the membrane potential.

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.

Animals↗

PubMed2006

Biophysical and phenomenological models of multiple spike interactions in spike-timing dependent plasticity.

Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. We also make links with other well-known plasticity rules. A simplified phenomenological model is also derived, which should be useful for fast numerical simulation and analytical investigation of the impact of STDP at the network level.

Action Potentials↗

PubMed2005

Exploring spike transfer through the thalamus using hybrid artificial-biological neuronal networks.

We use dynamic clamp to construct "hybrid" thalamic circuits by connecting a biological neuron in situ to silicon- or software-generated "neurons" through artificial synapses. The purpose is to explore cellular sensory gating mechanisms that regulate the transfer efficiency of signals during different sleep-wake states. Hybrid technology is applied in vitro to different paradigms such as: (1) simulating interactions between biological thalamocortical neurons, artificial reticular thalamic inhibitory interneurons and a simulated sensory input, (2) grafting an artificial sensory input to a wholly biological thalamic network that generates spontaneous sleep-like oscillations, (3) injecting in thalamocortical neurons a background synaptic bombardment mimicking the activity of corticothalamic inputs. We show that the graded control of the strength of intrathalamic inhibition, combined with the membrane polarization and the fluctuating synaptic noise in thalamocortical neurons, is able to govern functional shifts between different input/output transmission states of the thalamic gate.

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PubMed2005

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.

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PubMed2004

A method to estimate synaptic conductances from membrane potential fluctuations.

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.

Electricity↗

PubMed2003

Barrages of synaptic activity control the gain and sensitivity of cortical neurons.

Ongoing synaptic activity, ever present in cortical neurons, may vary widely in its amplitude and characteristics, potentially having a strong influence on neuronal processing. Intracellular recordings in layer 5 pyramidal cells in prefrontal and visual cortical slices maintained in vitro revealed spontaneous periods of synaptic bombardment. Testing the responsiveness of these cortical cells to synaptic inputs or the injection of artificial excitatory postsynaptic conductances of various amplitudes revealed that background synaptic activity dramatically increased the probability of response to small inputs, decreased the slope of the input-output curve, and decreased both the latency and jitter of action potential activation. Examining the effects of different components of synaptic barrages (namely, depolarization, increase in membrane conductance, and increase in membrane potential variance) revealed that the effects observed were dominated by the membrane depolarization and increase in variance. Depolarization increased the peak cross-correlation between injected complex in vivo-like waveforms through enhancement of responsiveness to small inputs, whereas increases in variance did so through a shift in firing mode from one of threshold detection to probabilistic discharge. These results indicate that rapid increases in neuronal responsiveness, as well as increases in spike timing precision, can be achieved through balanced barrages of excitatory and inhibitory synaptic activity.

Action Potentials↗

PubMed2003

Persistent cortical activity: mechanisms of generation and effects on neuronal excitability.

Local cortical networks in the prefrontal cortex and visual cortex are capable of spontaneously generating sustained activity for periods of seconds or longer. This sustained activity is generated through recurrent excitation between pyramidal cells that is controlled by feedback inhibition and can have both a rapid onset and a rapid offset. The period of activity is associated with a marked increase in neuronal responsiveness to the intracellular injection of current pulses, especially those of smaller amplitude. Independently mimicking the depolarization, increase in membrane conductance and increase in noise associated with sustained activity revealed that the depolarization is largely responsible for the increase in neuronal responsiveness, although an increase in membrane noise also facilitates responses to small inputs. These results indicate that the persistent activity associated with the performance of working memory tasks may be generated largely through recurrent networks. They also suggest that feedback pathways, such as those involved in selective attention, may exert a powerful influence on neuronal responsiveness through synaptic bombardment.

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PubMed2002

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.

Action Potentials↗