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20112023
most citedHow deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks

61 citations · 278 across the 24 of their papers we have counts for

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6 papers · 1 filter

q-bio.QM2023★ 1 cited

Extracting continuous sleep depth from EEG data without machine learning

Claus Metzner, Achim Schilling, Maximilian Traxdorf +3

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 le…

q-bio.QM2018★ 30 cited

Objective estimation of sensory thresholds based on neurophysiological parameters

Achim Schilling, Richard Gerum, Patrick Krauss +3

Reliable determination of sensory thresholds is the holy grail of signal detection theory. However, there exists no gold standard for the estimation of thresholds based on neurophy…

q-bio.QM2016★ 1 cited

A statistical method for analyzing and comparing spatiotemporal cortical activation patterns

Patrick Krauss, Claus Metzner, Achim Schilling +3

We present a new statistical method to analyze multichannel steady-state local field potentials (LFP) recorded within different sensory cortices of different rodent species. Our sp…

q-bio.QM2016

Stochastic resonance controlled upregulation of internal noise after hearing loss as a putative correlate of tinnitus-related neuronal hyperactivity

Patrick Krauss, Konstantin Tziridis, Achim Schilling +2

Subjective tinnitus (ST) is generally assumed to be a consequence of hearing loss (HL). In animal studies acoustic trauma can lead to behavioral signs of ST, in human studies ST pa…

q-bio.QM2011

Reconstructing fiber networks from confocal image stacks

Patrick Krauss, Claus Metzner, Janina Lange +2

We present a numerically efficient method to reconstruct a disordered network of thin biopolymers, such as collagen gels, from three-dimensional (3D) image stacks recorded with a c…

q-bio.QM2011★ 4 cited

Poresizes in random line networks

Claus Metzner, Patrick Krauss, Ben Fabry

Many natural fibrous networks with fiber diameters much smaller than the average poresize can be described as three-dimensional (3D) random line networks. We consider here a `Mikad…