[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 …
Stochastic resonance in the small-world networks with higher order neural motifs interactions
T Li, D Yu, Y Wu, Q Ding, Y Jia - The European Physical Journal Special …, 2024 - Springer
Transmission of weak signals in neural networks is crucial for understanding the
functionality of brain. In this work, stochastic resonance (SR) in the three neuron FitzHugh …
functionality of brain. In this work, stochastic resonance (SR) in the three neuron FitzHugh …
[HTML][HTML] Neural network based successor representations to form cognitive maps of space and language
P Stoewer, C Schlieker, A Schilling, C Metzner… - Scientific Reports, 2022 - nature.com
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 …
[HTML][HTML] Intrinsic noise improves speech recognition in a computational model of the auditory pathway
Noise is generally considered to harm information processing performance. However, in the
context of stochastic resonance, noise has been shown to improve signal detection of weak …
context of stochastic resonance, noise has been shown to improve signal detection of weak …
[HTML][HTML] 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] Sparsity through evolutionary pruning prevents neuronal networks from overfitting
Modern Machine learning techniques take advantage of the exponentially rising calculation
power in new generation processor units. Thus, the number of parameters which are trained …
power in new generation processor units. Thus, the number of parameters which are trained …
[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] Simulated transient hearing loss improves auditory sensitivity
P Krauss, K Tziridis - Scientific reports, 2021 - nature.com
Recently, it was proposed that a processing principle called adaptive stochastic resonance
plays a major role in the auditory system, and serves to maintain optimal sensitivity even to …
plays a major role in the auditory system, and serves to maintain optimal sensitivity even to …
Analysis of continuous neuronal activity evoked by natural speech with computational corpus linguistics methods
A Schilling, R Tomasello… - Language, Cognition …, 2021 - Taylor & Francis
In the field of neurobiology of language, neuroimaging studies are generally based on
stimulation paradigms consisting of at least two different conditions. Designing those …
stimulation paradigms consisting of at least two different conditions. Designing those …
[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 …