Ionic mechanisms in the generation of subthreshold oscillations and action potential clustering in entorhinal layer II stellate neurons.

A multicompartmental biophysical model of entorhinal cortex layer II stellate cells was developed to analyze the ionic basis of physiological properties, such as subthreshold membrane potential oscillations, action potential clustering, and the medium afterhyperpolarization. In particular, the simulation illustrates the interaction of the persistent sodium current (INaP) and the hyperpolarization activated inward current (Ih) in the generation of subthreshold membrane potential oscillations. The potential role of Ih in contributing to the medium hyperpolarization (mAHP) and rebound spiking was studied. The role of Ih and the slow calcium-activated potassium current IK(AHP) in action potential clustering was also studied. Representations of Ih and INaP were developed with parameters based on voltage-clamp data from whole-cell patch and single channel recordings of stellate cells (Dickson et al., J Neurophysiol 83:2562–2579, 2000; Magistretti and Alonso, J Gen Physiol 114:491–509, 1999; Magistretti et al., J Physiol 521:629–636, 1999a; J Neurosci 19:7334–7341, 1999b). These currents interacted to generate robust subthreshold membrane potentials with amplitude and frequency corresponding to data observed in the whole cell patch recordings. The model was also able to account for effects of pharmacological manipulations, including blockade of Ih with ZD7288, partial blockade with cesium, and the influence of barium on oscillations. In a model with a wider range of currents, the transition from oscillations to single spiking, to spike clustering, and finally tonic firing could be replicated. In agreement with experiment, blockade of calcium channels in the model strongly reduced clustering. In the voltage interval during which no data are available, the model predicts that the slow component of Ih does not follow the fast component down to very short time constants. The model also predicts that the fast component of Ih is responsible for the involvement in the generation of subthreshold oscillations, and the slow component dominates in the generation of spike clusters

Affective, Natural Interaction Using EEG: Sensors, Application and Future Directions

Abstract. ElectroEncephaloGraphy signals have been studied in relation to emotion even prior to the establishment of Affective Computing as a research area. Technological advancements in the sensor and network communication technology allowed EEG collection during interaction with low obtrusiveness levels as opposed to earlier work which classified physiological signals as the most obtrusive modality in affective analysis. The current article provides a critical survey of research work dealing with broadly affective analysis of EEG signals collected during natural or naturalistic interaction. It focuses on sensors that allow such natural interaction (namely NeuroSky and Emotiv), related technological features and affective aspects of applications in several application domains. These aspects include emotion representation approach, induction method and stimuli and annotation chosen for the application. Additionally, machine learning issues related to affective analysis (such as incorporation of multiple modalities and related issues, feature selection for dimensionality reduction and classification architectures) are revised. Finally, future directions of EEG incorporation in affective and natural interaction are discussed