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Biol Cybern (2008) 99:427441 DOI 10.1007/s00422-008-0263-8
PROSPECTS
Minimal HodgkinHuxley type models for different classes of cortical and thalamic neurons
Martin Pospischil Maria Toledo-Rodriguez
Cyril Monier Zuzanna Piwkowska Thierry Bal
Yves Frgnac Henry Markram Alain Destexhe
Received: 4 February 2008 / Accepted: 16 September 2008 Springer-Verlag 2008
Abstract 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 t 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 tting 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 thalamo-cortical system with realistic cellular dynamical properties.
Keywords Computational models Cerebral cortex
Thalamus Intrinsic neuronal properties Biophysical
models Model tting Intracellular recordings
1 Introduction
Central neurons are characterized by a wide diversity of intrinsic cellular properties (reviewed in Llins 1988; Connors and Gutnick 1990; Gupta et al. 2000). To design network models of the thalamocortical system which take into account this diversity, one needs to obtain precise single-cell models that capture these intrinsic properties. In particular, for large-scale networks, it is necessary to have models that are not only dynamically precise, but also fast and efcient to simulate. Candidates for such simplied models, are either integrate-and-re models, in particular those who can capture complex ring properties (Smith et al. 2000; Izhikevich 2004; Brette and Gerstner 2005), or Hodgkin and Huxley (1952) type models. In the present paper, we focus on the latter type to model the intrinsic properties of thalamic and cortical neurons.
To estimate the parameters of models, automatic tting procedures were used since over a decade,...