Walking on the Edge: Brain Connectivity Changes in Response to Virtual Height Challenges

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Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that generalize ...

Fluent human-robot interactions are essential for robots to be accepted and used in social scenarios including education, healthcare, or entertainment. Foundation models and Large Language Models (LLMs) in particular facilitate natural generation of text i ...

Converging evidence suggests that sleep disturbances can directly contribute to a transdiagnostic combination of behavior and neurocognitive difficulties characterizing most forms of psychopathology. However, it remains unclear how the growing comprehensio ...

Haptic interfaces enhance the virtual reality experience by emulating real‐world touch‐based interactions and delivering tactile feedback, while also supporting rehabilitation through interactive systems that speed up recovery. However, current haptic tech ...

Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain–machine interface with sensory feedback would provide the user with sensation about movement, pressure, curv ...

Virtual reality (VR) technology revitalises rehabilitation training by creating rich, interactive virtual rehabilitation scenes and tasks that deeply engage patients. Robotics with immersive VR environments have the potential to significantly enhance the s ...

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, int ...

Neural entrainment has become a popular technique to non-invasively manipulate brain rhythms via external, periodic stimulation. However, there is still debate regarding its underlying mechanisms and effects on brain activity. Here, we used EEG recordings ...

For high-dimensional Gaussian data, we investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large batch gradient descent steps, leading to an improvement in the approximation c ...

Electrochemical devices and systems are significant for the development of therapeutic drug monitoring (TDM) and personalized therapy. However, electrochemical sensors are usually not that much selective. Therefore, innovative machine learning (ML) approac ...

Implantable neuroprosthetic devices such as electrocorticography (ECoG) and intracortical microelectrodes have the potential to restore neurological functions in disabled individuals. They constitute the direct interface with the body and require long term ...

Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input length, require a lot of training data, and can be difficult to tune. In the pur ...

This work presents the characterization results and methods used to determine the reconstruction of a 22Na point source placed in a coincidence setup. For this purpose, a novel, fully-integrated, reconfigurable feed-forward artificial neural network was im ...

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular str ...

Relating behavior to brain activity in animals is a fundamental goal in neuroscience, with practical applications in building robust brain-machine interfaces. However, the domain gap between individuals is a major issue that prevents the training of genera ...

Autoencoders are the simplest neural network for unsupervised learning, and thus an ideal framework for studying feature learning. While a detailed understanding of the dynamics of linear autoencoders has recently been obtained, the study of non-linear aut ...

Walking on the Edge: Brain Connectivity Changes in Response to Virtual Height Challenges

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A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy

Improving the Fluency of Robot-Human Interaction Through Embodied Expression of Emotion via Foundation Models

Multivariate deep phenotyping reveals behavioral correlates of non-restorative sleep in 22q11.2 deletion syndrome

Reconfigurable Haptic Feedback: Comprehensive Design and Control Framework

Optimization frameworks for bespoke sensory encoding in neuroprosthetics

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons

Spectral tuning and after-effects in neural entrainment

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

Simultaneous Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors

In vivo recording of visually evoked potentials with novel full diamond ECoG implants

HyperMixer: An MLP-based Low Cost Alternative to Transformers

On-chip artificial neural network for PET source position reconstruction

Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

Overcoming the Domain Gap in Neural Action Representations

The dynamics of representation learning in shallow, non-linear autoencoders