Editorial—Methods and models in sleep research

Neuroscience Methods Vincenzo stepped down on 31st December 2017. His experience and dedication have been invaluable to set the Journal as a leading academic journal in the field of neuroscience methods and techniques. One of his initiatives was the regular publication of journal issues dedicated to the review of emerging thecnological advances and questions on a specific topic. Some of these Special Issues gathered contributions presented at international workshops dealing for example with computational neuroscience (volumes 169 (2008); 210 (2012); 220 (2013)) or behavioral measurements (volumes 268 (2016) and 300 (2018)). Others focused on methods and models for the study of neu-rological diseases (volumes 252 (2015); 260 (2016); 265 (2016); 272 (2016); 292 (2017)) or reported the outcome of a challenge for machine learning algorithms and analysis of Alzheimer's disease neuroimaging data (volume 300 (2018)). In honor of Vincenzo's own research interests , we decided to launch a Special Issue on sleep-a complex beha-vioral state shared by all animals. Vincenzo's interest in sleep mechanisms spans decades, from his initial work on thalamic excitability. His research during this time tackled the issue of thalamocortical network dynamics during non-REM sleep at multiple levels, including in vitro studies of neuronal excit-ability, recordings of cellular activities during natural sleep in rodents, and the development of computational models to predict and explain in vivo experimental observations. Vincenzo's laboratory contributed to our current understanding of the cellular and network mechanisms underlying non-REM sleep delta waves and established that the tha-lamus is essential to the full expression of non-REM sleep. His results emphasized the essential role of thalamic T-type calcium channels to the expression of slow waves of natural sleep and the key role played by calcium entry through these channels in the modulation of other voltage dependent channels critical for the generation of both non-REM slow waves and sleep spindles. A major focus of contributions to this Special Issue is the analysis of electroencephalographic (EEG) activities. Although EEG signals have been recorded for almost a century, the detection and analysis of specific waves characterizing different sleep stages is still a challenge. Two papers, Lacourse et al. and Sampson et al., propose new methods to detect sleep spindles. Sakellariou et al. describe a novel approach to estimate