Research Themes and Projects

Picture illustrating the combination of computational models (color snapshots) with in vivo intracellular recordings (yellow trace) to study the oscillatory behavior generated in a neuronal structure (see A model of spindle rhythmicity in the isolated thalamic reticular nucleus, Journal of Neurophysiology 72: 803-818, 1994).

The research conducted in this laboratory stands at the interface between several disciplines, such as biophysics, physics and neuroscience. The themes investigated (see below) range from the microscopic (single neurons) to the macroscopic (networks or populations of neurons) aspects of the central nervous system function. We use theoretical methods and computer-based simulation techniques to explore the complex behavior of single neurons and understand their basic integrative properties (see The integrative properties of cortical neurons in vivo, The integrative properties of thalamic neurons). This task requires to integrate details about experimental measurements of these neurons, their morphology, their biophysical properties, as well as the properties of their synaptic inputs (see Biophysical models of synaptic transmission). There is also a need for a constant and continuous exchange with experimentalists recording single cells (intracellular measurements).

At the network level, we try to understand the collective behavior of neuronal populations, which in many cases cannot be simply deduced from single-cell behavior. In cerebral cortex and thalamus, neurons are characterized by complex intrinsic properties (see above) and also influence each-other through many different types of synaptic interactions involving different classes of receptors. These networks are therefore highly complex and computational methods can be particularly pertinent in predicting their behavior. This approach was followed for the case of oscillatory behavior in thalamus and cortex (see Network models of thalamic oscillations and Network models of thalamocortical oscillations). Models can also be used to understand the genesis of pathological behavior such as epileptic seizures (see Network models of epileptic discharges). Here again, a tight relation with experimental data is needed.

Finally, another aspect of computational neuroscience is to directly provide methods to analyze experimental data. Single- or multi-electrode recordings often reveal complex behavior which may not be easy to analyze. Such complex signals can be analyzed in many different ways with the help of theoretical approaches (see Spatiotemporal analysis of electrophysiological data). In some cases, the theory can help analyzing complex, apparently random signals. This is the case for intracellular recordings of "synaptic noise", from which many useful information can be extracted (see Stochastic analysis of synaptic noise).

These different approaches have been summarized in the following review papers:

as well as in the following book:

See also the Research Grants page for more details about current funding and on-going research projects, as well as possible PhD or postdoc opportunities.

Research themes of the laboratory and overview of publications

(each link below gives, for each topic, a summary, a list of the published work in the laboratory, and PDFs copies of the corresponding publications)

1. Modeling ion channels and extracellular potentials

1.1 Biophysical models of synaptic transmission

1.2 Model of the hyperpolarization-activated current Ih and its regulation by calcium

1.3 Models of local field potentials

2. Modeling the integrative properties in single neurons

2.1 The integrative properties of cortical neurons in vivo

2.2 Integrative properties of thalamic neurons

2.3 Model of hyperpolarization-activated persistent activity

2.4 Non-ideal cable equations for neurons

3. Network models

3.1 Models of thalamic oscillations

3.2 Thalamocortical oscillations

3.3 Role of sleep in memory consolidation

3.4 Models of epileptic discharges and absence seizures

3.5 Networks of "silicon" neurons

3.6 In vivo like states in cortical networks

3.7 Macroscopic models of cortical networks

4. Computational methods for to analyze experimental data

4.1 Spatiotemporal analysis of electrophysiological data

4.2 Analysis of synaptic noise from intracellular recordings

4.3 The Active Electrode Compensation (AEC) method for high-resolution intracellular recordings

5. Dynamic-clamp experiments

5.1 The Dynamic-clamp: real-time interaction between models and living neurons

(see also The Active Electrode Compensation (AEC) method above)

6. Theoretical and numerical methods

6.1 Conductance-based integrate and fire models

6.2 Event-based integration algorithms for conductance-based integrate and fire networks

For more information, please contact:

Unité de Neurosciences, Information & Complexité (UNIC)
CNRS
UPR-3293, Bat 33,
1 Avenue de la Terrasse,
91198 Gif-sur-Yvette, France.

Tel: 33-1-69-82-34-35
Fax: 33-1-69-82-34-27

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