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Extracting continuous sleep depth from EEG data without machine learning
The human sleep-cycle has been divided into discrete sleep stages that can be recognized in electroencephalographic (EEG) and other bio-signals by trained specialists or machine learning systems.
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity
Remarkably, the accuracy limit is not affected by applying non-linear transformations to the data, even if these transformations are non-reversible and drastically reduce the information content of the input data.
Predictive coding and stochastic resonance as fundamental principles of auditory perception
How is information processed in the brain during perception?
How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks
Deep neural networks typically outperform more traditional machine learning models in their ability to classify complex data, and yet is not clear how the individual hidden layers of a deep network contribute to the overall classification performance.
