A5076428004- Genetic Associations and Epidemiology
- Advanced Memory and Neural Computing
- EEG and Brain-Computer Interfaces
- Atherosclerosis and Cardiovascular Diseases
- Functional Brain Connectivity Studies
- Single-cell and spatial transcriptomics
- Neural Networks and Applications
- Parallel Computing and Optimization Techniques
- Vestibular and auditory disorders
- CCD and CMOS Imaging Sensors
- Neuroscience and Neural Engineering
Delft University of Technology
2024-2025
Erasmus MC
2024
Utrecht University
2021
University Medical Center Utrecht
2021
Heidelberg University
2021
University Hospital Heidelberg
2021
Genome-wide association studies (GWASs) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation susceptibility loci into biological mechanisms targets drug discovery remains challenging. Intersecting gene expression data has led to the identification candidate genes. However, previously studied tissues are often non-diseased heterogeneous in cell composition, hindering accurate prioritization. Therefore, we analysed...
Olivocerebellar learning is highly adaptable, unfolding over minutes to weeks depending on the task. However, stabilizing mechanisms of synaptic dynamics necessary for ongoing remain unclear. We constructed a model examine plasticity under stochastic input and investigate impact inferior olive (IO) reverberations Purkinje cell (PCs) activity plasticity. explored Upbound Downbound cerebellar micromodules, which are organized loops IO neurons, nuclei neurons microzones PCs characterized by...
Brain modeling can occur at different levels of abstraction, each aimed a purpose. The Virtual (TVB) is an open-source platform for constructing and simulating personalized brain-network models, favoring whole-brain macro-scales while reducing micro-level detail. Among other purposes, TVB used to build patient-specific, digital, brain twins that be in clinical settings, such as the study treatment epilepsy. However, fitting patient-specific models requires large number successive...
In recent years, significant strides in Artificial Intelligence (AI) have led to various practical applications, primarily centered around training and deployment of deep neural networks (DNNs). These however, require considerable computational resources, predominantly reliant on modern Graphics-Processing Units (GPUs). Yet, the quest for larger faster DNNs has spurred creation specialized AI chips efficient Machine-Learning (ML) software tools like TensorFlow PyTorch been developed striking...
Electrophysiological recordings of neural activity in a mouse's brain are very popular among neuroscientists for understanding function.One particular area interest is acquiring from the Purkinje cells cerebellum order to understand injuries and loss motor functions.However, current setups such experiments do not allow mouse move freely and, thus, capture its natural behaviour since they have wired connection between animal's head stage an acquisition device.In this work, we propose...
Realistic brain models contain many parameters. Traditionally, gradient-free methods are used for estimating these parameters, but gradient-based offer advantages including scalability. However, tied to existing simulators, which do not support gradient calculation. Here we show how extend -- within the public interface of such simulators neural also compute gradients with respect their We demonstrate that computed can be optimize a biophysically realistic multicompartmental neuron model...
Background Genome-wide association studies have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation susceptibility loci into biological mechanisms targets drug discovery remains challenging. Intersecting gene expression data has led to the identification candidate genes. However, previously studied tissues are often non-diseased heterogeneous in cell composition, hindering accurate prioritization. Therefore, we analyzed...