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Browsing Pósters by Subject "FASES DEL SUEÑO"
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póster.listelement.badge K-Complex detection algorithm in non-REM sleep(2020) Vázquez Chenlo, Aylin; Carosi, Julia; Carbonari, Giulia; Forcato, Cecilia; Ramele, Rodrigo"In order to evaluate the relation between KC and memory processes our main goal was to create a method with Machine Learning techniques to characterize and identify KCs."póster.listelement.badge Role of dream content in memory processing during sleep: Preliminary setup(2021) Pretel, Matías; Herrero, Nerea; Fernández Sande, Joaquín; Brusco, Luis Ignacio; Ramele, Rodrigo; Kaczer, Laura; Forcato, Cecilia"After acquisition memories are in a labile state followed by a period of stabilization known as consolidation. This process is particularly favored by sleep, where the new information is spontaneously reactivated in the hippocampus, transferred and redistributed in neocortical networks facilitating long term consolidation. Also, during sleep, specifically during REM sleep, new memories are integrated into the stored information. From a neuroscientific perspective, dream content is proposed to be a consequence of the memory processes that occur during sleep. Thus, the incorporation of elements about the learned tasks during wakefulness in the content of a dream, can predict the performance of the task after sleep. Here, we developed a new paradigm to study whether dream content related to a new word learning task correlates with consolidation of new words and integration into the pre-existed semantic networks."póster.listelement.badge Slow wave detection algorithm in non-REM sleep(2020) Carbonari, Giulia; Carosi, Julia; Vázquez Chenlo, Aylin; Moris, Eugenia; Forcato, Cecilia; Ramele, Rodrigo; Larrabide, Ignacio"Online detection of slow waves."póster.listelement.badge Structural differences between non-lucid, lucid dreams and out-of-body experience reports assessed by graph analysis(2021) Gallo, Francisco; Tommasel, Antonela; Herrero, Nerea; Forcato, Cecilia; Godoy, Daniela; Gleiser, Pablo"It has been recently found using graph theory that measures of network structure can predict ratings of dream complexity, where increases in connectedness and decreases in randomness are observed in relation to increasing dream report complexity. This approach proved to be useful to differentiate dream reports in the pathological population as well as NREM and REM dream reports, but it has not yet been used to study the differences between different oneiric experiences. In this work we analyze dream reports that include non-lucid, lucid dreams and out-of body experiences initiated from sleep paralysis. The reports are presented as directed graphs, where each different word plays the role of a node, and consecutive words are connected by a directed, unweighted edge. We analyze different network measures to compare the graphs. Preliminary results presented here suggest that both local measures, such as the degree of nodes, and global measures, such as clustering and the number of strongly connected components, allow for a categorization of different dream experiences."póster.listelement.badge Wavelets for sleep scoring: a machine learning approach(2020) Moris, Eugenia; Forcato, Cecilia; Larrabide, Ignacio"Sleep scoring it a common method used by experts to monitor the quantity and quality of sleep in people, but it is a time-consuming and labour-intense task."