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Found 10 papers, 0 papers with code
Beyond Labels: Advancing Cluster Analysis with the Entropy of Distance Distribution (EDD)
In the evolving landscape of data science, the accurate quantification of clustering in high-dimensional data sets remains a significant challenge, especially in the absence of predefined labels.
Quantifying and maximizing the information flux in recurrent neural networks
We find that in networks with moderately strong connections, the mutual information $I$ is approximately a monotonic transformation of the root-mean-square averaged Pearson correlations between neuron-pairs, a quantity that can be efficiently computed even in large systems.
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?
Neural Network based Successor Representations of Space and Language
We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence.
Dynamical Phases and Resonance Phenomena in Information-Processing Recurrent Neural Networks
Moreover, we find a completely new type of resonance phenomenon, called 'Import Resonance' (IR), where the information import shows a maximum, i. e. a peak-like dependence on the coupling strength between the RNN and its input.
Detecting long-range interactions between migrating cells
By contrast, we find attractive interactions between NK cells and an IL-15-secreting variant of K562 tumor cells.
Detecting long-range attraction between migrating cells based on p-value distributions
Immune cells have evolved to recognize and eliminate pathogens, and the efficiency of this process can be measured in a Petri dish.
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.
