By Hans Liljenström
This quantity is the complaints of the 4th overseas convention on Cognitive Neurodynamics (ICCN2013) held in Sweden in 2013. The incorporated papers replicate the big span of study provided and are grouped in ten elements which are geared up primarily in a top-down constitution. the 1st components take care of social/interactive (I) and psychological (II) points of mind capabilities and their relation to belief and cognition (III). subsequent, extra particular facets of sensory structures (IV) and neural community dynamics of mind capabilities (V), together with the consequences of oscillations, synchronization and synaptic plasticity (VI), are addressed, by means of papers fairly emphasizing using neural computation and knowledge processing (VII). With the subsequent components, the degrees of mobile and intracellular methods (VIII) and eventually quantum results (IX) are reached. The final half (X) is dedicated to the contributions invited via the Dynamic mind discussion board (DBF), which used to be co-organized with ICCN2013.
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Extra info for Advances in Cognitive Neurodynamics (IV): Proceedings of the Fourth International Conference on Cognitive Neurodynamics - 2013
Integrating verbal and nonverbal communication in a dynamic neural field architecture for human-robot interaction. Frontiers in Neurorobotics 4, article 5. 8. Oudeyer, PY (2003). The production and recognition of emotions in speech: features and algorithms. International Journal of Human Computer Studies 59 (1–2), 157–183. 9. Rolls E (2005). What are emotions, why do we have emotions, and what is their computational basis in the brain. Oxford University Press. 10. Glenbach AM & Kaschak MP (2002).
E. H. 2009. Efficiency of functional brain networks and intellectual performance. The Journal of Neuroscience, 29, 7619–7624. Computational Emotions Mauricio Iza Miqueleiz and Jesús Ezquerro Martínez Abstract Research on the interaction between emotion and cognition has become particularly active in the last years, and many computational models of emotion have been developed. However, none of these models is able to address satisfactorily the integration of emotion generation and its effects in the context of cognitive processes.
EEG based BCIs operate a real-time translation of predefined features into commands reflecting the users’ intent. Many of the currently available BCI applications exploit different classes of EEG features as control signals, such as sensorimotor rhythms, steady-state visual evoked potentials, eventrelated potentials (ERPs) and the modulation of sensorimotor rhythms (SMRs; ). Different kind of BCI applications can be thought on the base of these kinds of potentials. In addition, such as measurements could be useful in several applications related to the monitoring of the brain states in complex tasks, such as driving cars or piloting aircrafts.