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Network models of thalamocortical oscillationsThe initial motivation for building a model of thalamocortical oscillations was the observation that different types of oscillations follow dynamics of different levels of spatiotemporal coherence although generated by the same thalamocortical circuits (see Section 4.1). Initially, a simple model was considered where the thalamus was assumed to be a pacemaker without feedback from the cortex [1]. This model consisted of networks of excitatory and inhibitory neurons and reproduced the most salient features observed experimentally, namely that slow-frequency rhythms are highly coherent whereas fast-frequency rhythms display more limited coherence [1].The presence of propagating properties of oscillations in thalamic slices contrasted with the apparent simultaneity and large-scale synchrony of the same oscillations in the thalamocortical system in vivo. We therefore investigated experimentally the spatiotemporal properties of these oscillations using multisite recordings in vivo (in collaboration with Diego Contreras and Mircea Steriade). We found that oscillations are characterized by a remarkable coherence across large cortical or thalamic areas, and that this coherence is lost following removal or inactivation of the cortex [2,3,4,6], therefore ruling-out possible intrathalamic synchronizing mechanisms proposed by early studies of Andersen and Andersson. Physically cutting cortico-cortical connections did not affect coherence [2,3] suggesting that the large-scale coherence is not due to intracortical mechanisms. However, coherence was maximal during slow-wave sleep, and was lower during anesthesia, or after artificial depression of the cortex [4,6]. To investigate if these results can be explained by interacting thalamocortical loops, we investigated computational models of networks comprising thalamic and cortical layers [5,6]. Thalamocortical models could reproduce the experimental findings described above, but one condition was necessary: corticothalamic feedback must affect TC cells through dominant inhibition [5,7]. This hypothesis was confirmed intracellularly [5], and provided a powerful synchronizing mechanism for corticothalamic feedback. The same model was consistent with the propagating properties of thalamic slices, the large-scale coherence found in the thalamocortical system in vivo and the different levels of coherence observed after anesthesia or cortical depression. The mechanism proposed for large-scale coherence was based on successive thalamo-cortical recruitment loops. Thus, experiments and models provide a framework which is consistent with data from different preparations. The main conclusion is that the cortex has an extremely powerful synchronizing effect over the thalamus, and that this effect is mediated through inhibitory mechanisms. Although oscillations are generated by the thalamus, they are entirely controlled and triggered by the cortex. This view also allowed us to explain some type of pathological behavior such as absence seizures (see Section 3.4). More recently, thalamocortical networks models were studied based on adaptive exponential integrate-and-fire models [8]. These models were shown to generate various network states, such as spindle oscillations, slow-wave oscillations (Up/Down states) and desynchronized network states (AI states). Modulating spike frequency adaptation enabled the transition between these states. These networks were also simulated on hardware ASIC neurons [9,10] (see Section 3.5). Alain Destexhe |