Publications by authors named "J Triesch"
Virtual reality-based Harms tangent screen test for strabismus measurement.
Graefes Arch Clin Exp Ophthalmol
January 2025
Purpose: Our study presents a virtual reality-based tangent screen test (VTS) to measure subjective ocular deviations including torsion in nine directions of gaze. The test was compared to the analogous Harms tangent screen test (HTS).
Methods: We used an Oculus Go controller and head-mounted-display with rotation sensors to measure patient's head orientation for the VTS.
Cortical networks are capable of unsupervised learning and spontaneous replay of complex temporal sequences. Endowing artificial spiking neural networks with similar learning abilities remains a challenge. In particular, it is unresolved how different plasticity rules can contribute to both learning and the maintenance of network stability during learning.
View Article and Find Full Text PDFThe hippocampal-dependent memory system and striatal-dependent memory system modulate reinforcement learning depending on feedback timing in adults, but their contributions during development remain unclear. In a 2-year longitudinal study, 6-to-7-year-old children performed a reinforcement learning task in which they received feedback immediately or with a short delay following their response. Children's learning was found to be sensitive to feedback timing modulations in their reaction time and inverse temperature parameter, which quantifies value-guided decision-making.
View Article and Find Full Text PDFArticle Synopsis
- Spiking neural network simulations are key in fields like Computational Neuroscience, AI, and Neuromorphic Engineering, with various simulators available for different applications.
- PymoNNto is a new Python toolbox for spiking neural networks that allows users to embed custom code flexibly, operating with a NumPy backend and GPU support.
- PymoNNtorch builds on this by using PyTorch for better performance, showing faster results than traditional simulators like NEST and Brian 2 through its optimized GPU capabilities and modular design.
Background: Of all urothelial carcinomas (UCs), 25% are muscle invasive and associated with a 5-year overall survival rate of 50%. Findings regarding the molecular classification of muscle-invasive urothelial carcinomas (MIUCs) have not yet found their way into clinical practice.
Objectives: Prediction of molecular consensus subtypes in MIUCs with artificial intelligence (AI) based on histologic hematoxylin-eosin (HE) sections.