[HTML][HTML] Quantifying the separability of data classes in neural networks
Abstract We introduce the Generalized Discrimination Value (GDV) that measures, in a non-
invasive manner, how well different data classes separate in each given layer of an artificial …
invasive manner, how well different data classes separate in each given layer of an artificial …
Neural network based successor representations to form cognitive maps of space and language
How does the mind organize thoughts? The hippocampal-entorhinal complex is thought to
support domain-general representation and processing of structural knowledge of arbitrary …
support domain-general representation and processing of structural knowledge of arbitrary …
Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts
How do we make sense of the input from our sensory organs, and put the perceived
information into context of our past experiences? The hippocampal-entorhinal complex …
information into context of our past experiences? The hippocampal-entorhinal complex …
[HTML][HTML] Analysis and visualization of sleep stages based on deep neural networks
Automatic sleep stage scoring based on deep neural networks has come into focus of sleep
researchers and physicians, as a reliable method able to objectively classify sleep stages …
researchers and physicians, as a reliable method able to objectively classify sleep stages …
[HTML][HTML] 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 …
in electroencephalographic (EEG) and other bio-signals by trained specialists or machine …
Sleep as a random walk: A super-statistical analysis of EEG data across sleep stages
In clinical practice, human sleep is classified into stages, each associated with different
levels of muscular activity and marked by characteristic patterns in the EEG signals. It is …
levels of muscular activity and marked by characteristic patterns in the EEG signals. It is …
Classification at the accuracy limit: facing the problem of data ambiguity
Data classification, the process of analyzing data and organizing it into categories or
clusters, is a fundamental computing task of natural and artificial information processing …
clusters, is a fundamental computing task of natural and artificial information processing …
Word class representations spontaneously emerge in a deep neural network trained on next word prediction
K Surendra, A Schilling, P Stoewer… - 2023 International …, 2023 - ieeexplore.ieee.org
How do humans learn language, and can the first language be learned at all? These
fundamental questions are still hotly debated. In contemporary linguistics, there are two …
fundamental questions are still hotly debated. In contemporary linguistics, there are two …
Quantifying and maximizing the information flux in recurrent neural networks
Free-running recurrent neural networks (RNNs), especially probabilistic models, generate
an ongoing information flux that can be quantified with the mutual information I [x→(t), x→(t+ …
an ongoing information flux that can be quantified with the mutual information I [x→(t), x→(t+ …
Conceptual cognitive maps formation with neural successor networks and word embeddings
The human brain possesses the extraordinary capability to contextualize the information it
receives from our environment. The entorhinal-hippocampal plays a critical role in this …
receives from our environment. The entorhinal-hippocampal plays a critical role in this …