Immersive learning in medical education: analyzing behavioral insights to shape the future of VR-based courses.

Background: The emergence of virtual reality (VR) for medical education enables a range of new teaching opportunities. Skills and competences can be trained that cannot be demonstrated in any other way due to physical or ethical limitations. Immersion and presence may play an important role for learning in this context.

Probabilistic photonic computing with chaotic light.

Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as "point estimates" that do not capture the uncertainty of prediction, which is an inherently probabilistic process.

Compensation to visual impairments and behavioral plasticity in navigating ants.

Desert ants are known to rely heavily on vision while venturing for food and returning to the nest. During these foraging trips, ants memorize and recognize their visual surroundings, which enables them to recapitulate individually learned routes in a fast and effective manner. The compound eyes are crucial for such visual navigation; however, it remains unclear how information from both eyes are integrated and how ants cope with visual impairment.

deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks.

Background: Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods.