9 citations · 13 across the 9 of their papers we have counts for
9 papers
Power-Law Suppression of Superfluid Stiffness in High-Kinetic-Inductance NbN Films
Meenakshi Sharma, Hrishikesh Borah, Sandeep Singh +8
Disorder is a powerful route to high kinetic inductance in superconducting ultrathin films, enabling compact high-impedance quantum circuits. This functionality, however, comes at…
Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows
Peter Bouss, Sandra Nestler, Kirsten Fischer +3
Neuronal activity is found to lie on low-dimensional manifolds embedded within the high-dimensional neuron space. Variants of principal component analysis are frequently employed t…
Terahertz field effect in a two-dimensional semiconductor MoS2
Tomoki Hiraoka, Sandra Nestler, Wentao Zhang +6
Layered two-dimensional (2D) materials, with their atomic-scale thickness and tunable electronic, optical, and mechanical properties, open many promising pathways to significantly…
Bright quantum dot light sources using monolithic microlenses on gold back-reflectors
Moritz Langer, Sai A. Dhurjati, Yared G. Zena +8
We present the fabrication process of bright quantum dot (QD) photon sources by non-deterministic embedding into broadband monolithic microlens arrays…
Statistical physics, Bayesian inference and neural information processing
Erin Grant, Sandra Nestler, Berfin Şimşek +1
Lecture notes from the course given by Professor Sara A. Solla at the Les Houches summer school on "Statistical physics of Machine Learning". The notes discuss neural information p…
Learning Interacting Theories from Data
Claudia Merger, Alexandre René, Kirsten Fischer +5
One challenge of physics is to explain how collective properties arise from microscopic interactions. Indeed, interactions form the building blocks of almost all physical theories…
Neuronal architecture extracts statistical temporal patterns
Sandra Nestler, Moritz Helias, Matthieu Gilson
Neuronal systems need to process temporal signals. We here show how higher-order temporal (co-)fluctuations can be employed to represent and process information. Concretely, we dem…
Path classification by stochastic linear recurrent neural networks
Wiebke Bartolomaeus, Youness Boutaib, Sandra Nestler +1
We investigate the functioning of a classifying biological neural network from the perspective of statistical learning theory, modelled, in a simplified setting, as a continuous-ti…
Unfolding recurrence by Green's functions for optimized reservoir computing
Sandra Nestler, Christian Keup, David Dahmen +3
Cortical networks are strongly recurrent, and neurons have intrinsic temporal dynamics. This sets them apart from deep feed-forward networks. Despite the tremendous progress in the…