50 citations
- National Research CouncilLK3 papers
- Princeton UniversityUS3 papers
- University of FlorenceIT3 papers
- Forschungszentrum JülichDE2 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma IIT2 papers
- Jülich Aachen Research AllianceDE2 papers
- Neurosciences InstituteUS2 papers
- RWTH Aachen UniversityDE2 papers
- Sapienza University of RomeIT2 papers
- University of PaduaIT2 papers
- Brookhaven National LaboratoryUS1 paper
- Carnegie Mellon UniversityUS1 paper
19 papers
THz mixing of high-order harmonics using nanobridges
Núria Alcalde-Herraiz, Alessia Garibaldi, Karn Rongrueangkul +4
Superconducting materials are a key for technologies enabling a large number of devices including THz wave mixers and single photon detectors, though limited at very low temperatur…
Diffusive Spreading Across Dynamic Mitochondrial Network Architectures
Keaton B. Holt, Camryn Zurita, Lizzy Teryoshin +2
In eukaryotic cells, mitochondria form networks that range from highly fused interconnected structures to fragmented populations of individual organelles that undergo transient int…
Artificial Intelligence Can Emulate Human Normative Judgments on Emotional Visual Scenes
Zaira Romeo, Alberto Testolin
Affective reactions have deep biological foundations, however in humans the development of emotion concepts is also shaped by language and higher-order cognition. A recent breakthr…
Reconstruction of phase-amplitude dynamics from electrophysiological signals
Azamat Yeldesbay, Gemma Huguet, Silvia Daun
We present a novel method of reconstructing the phase-amplitude dynamics directly from measured electrophysiological signals to estimate the coupling between brain regions. For thi…
Predictive variational autoencoder for learning robust representations of time-series data
Julia Huiming Wang, Dexter Tsin, Tatiana Engel
Variational autoencoders (VAEs) have been used extensively to discover low-dimensional latent factors governing neural activity and animal behavior. However, without careful model…
Meta-Learning Strategies through Value Maximization in Neural Networks
Rodrigo Carrasco-Davis, Javier Masís, Andrew M. Saxe
Biological and artificial learning agents face numerous choices about how to learn, ranging from hyperparameter selection to aspects of task distributions like curricula. Understan…