2 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
What should a neuron aim for? Designing local objective functions based on information theory
Andreas C. Schneider, Valentin Neuhaus, David A. Ehrlich +4
In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological system…
MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
Polina Turishcheva, Laura Hansel, Martin Ritzert +2
Driven by advances in recording technology, large-scale high-dimensional datasets have emerged across many scientific disciplines. Especially in biology, clustering is often used t…
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
Polina Turishcheva, Paul G. Fahey, Michaela Vystrčilová +22
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural…
Reproducibility of predictive networks for mouse visual cortex
Polina Turishcheva, Max Burg, Fabian H. Sinz +1
Deep predictive models of neuronal activity have recently enabled several new discoveries about the selectivity and invariance of neurons in the visual cortex. These models learn a…
Tailor-designed models for the turbulent velocity gradient through normalizing flow
Maurizio Carbone, Vincent J. Peterhans, Alexander S. Ecker +1
Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models co…