Joschka Boedecker

Evaluating Transferability and Robustness of Process-Guided Neural Networks in Forest Carbon Flux Modelling

This study evaluates process-guided neural networks (PGNNs) and standard ANNs for predicting forest carbon fluxes, showing PGNNs outperform a process model in data-sparse and transfer scenarios.

Efficient Gaussian Process-based Motor Hotspot Hunting with Concurrent Optimization of TMS Coil Location and Orientation

This paper presents a Gaussian process-based method for optimizing TMS coil location and orientation to improve motor hotspot identification efficiency and precision.

Enhancement of network architecture alignment in comparative single-cell studies

This paper proposes a deep learning method to align neural network architectures across species for single-cell data, revealing commonalities and differences in gene expression.

Sleep resolves competition between explicit exemplar memory and implicit memory generalization

The paper shows sleep resolves competition between explicit and implicit memory components and improves generalization of knowledge.

Efficient Prospective Electric Field-Informed Localization of Motor Cortical Targets of Transcranial Magnetic Stimulation

This study introduces an optimization strategy for TMS-based motor mapping using farthest point sampling, showing it is more efficient and robust than random sampling.

Mechanisms of Premotor-Motor Cortex Interactions during Goal Directed Behavior

This paper investigates the functional interactions between premotor and motor cortical areas in rats during goal-directed behavior, revealing differential roles of neuronal subpopulations in movement preparation and execution.