A5044617206- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- Simulation Techniques and Applications
- Time Series Analysis and Forecasting
- Advanced MRI Techniques and Applications
- Neurobiology and Insect Physiology Research
- Neural Networks and Applications
- Advanced Neuroimaging Techniques and Applications
- Statistical Methods and Inference
- Forecasting Techniques and Applications
- Human Pose and Action Recognition
- Mental Health Research Topics
- Blind Source Separation Techniques
- Gaussian Processes and Bayesian Inference
- Neurobiology of Language and Bilingualism
- Gene Regulatory Network Analysis
- Gait Recognition and Analysis
- Action Observation and Synchronization
- Bioinformatics and Genomic Networks
- Fuzzy Systems and Optimization
- Language Development and Disorders
- Video Surveillance and Tracking Methods
- Target Tracking and Data Fusion in Sensor Networks
- Cardiac electrophysiology and arrhythmias
- Plant and Biological Electrophysiology Studies
BMW (Germany)
2020-2023
University of Freiburg
2008-2019
Pennsylvania State University
2014
University Medical Center Freiburg
2009-2014
Universität Ulm
1988-2012
Abstract Summary: Modeling of dynamical systems using ordinary differential equations is a popular approach in the field biology. Two most critical steps this are to construct models biochemical reaction networks for large datasets and complex experimental conditions perform efficient reliable parameter estimation model fitting. We present modeling environment MATLAB that pioneers these challenges. The numerically expensive parts calculations such as solving associated sensitivity system...
SUMMARY Pymetrozine is a neuroactive insecticide but its site of action in the nervous system unknown. Based on previous studies symptoms locust, feedback loop controlling femur–tibia joint middle leg was chosen to examine possible targets insecticide. The femoral chordotonal organ, which monitors position and movement, turned out be primary pymetrozine action, while interneurons,motoneurons central motor control circuitry general did not noticeably respond organs associated with wing hinge...
In a wide variety of research fields, dynamic modeling is employed as an instrument to learn and understand complex systems. The differential equations involved in this process are usually non-linear depend on many parameters whose values determine the characteristics emergent system. inverse problem, i.e., inference or estimation parameter from observed data, interest two points view. First, existence point view, dealing with question whether system able reproduce dynamics for any values....
Gesture recognition defines an important information channel in human-computer interaction. Intuitively, combining inputs from multiple modalities improves the rate. In this work, we explore multi-modal video-based gesture tasks by fusing spatio-temporal representation of relevant distinguishing features different modalities. We present a self-attention based transformer fusion architecture to distill knowledge two-stream convolutional neural networks (CNNs). For this, introduce convolutions...
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> To infer interactions from functional magnetic resonance imaging (fMRI) data, structural equation modeling (SEM) as well dynamic causal (DCM) has been suggested. Directed partial correlation (dPC) is a measure which detects Granger causality in multivariate systems. demonstrate the strengths limitations of directed we first applied it to simulated data tailored problem at hand. Second, after dPC...
One of the main modelling paradigms for complex physical systems are networks. When estimating network structure from measured signals, typically several assumptions such as stationarity made in estimation process. Violating these renders standard analysis techniques fruitless. We here propose a framework to estimate measurements arbitrary non-linear, non-stationary, stochastic processes. To this end, we rigorous mathematical theory that underlies framework. Based on theory, present highly...
A reliable inference of networks from observations the nodes' dynamics is a major challenge in physics. Interdependence measures such as correlation coefficient or more advanced methods based on, e.g., analytic phases signals are employed. For several these interdependence measures, multivariate counterparts exist that promise to enable distinguishing direct and indirect connections. Here, we demonstrate analytically how bivariate relate respective ones; this knowledge will turn be used...
The perception of proprioceptive signals that report the internal state body is one essential tasks nervous system and helps to continuously adapt movements changing circumstances. Despite impact feedback on motor activity it has rarely been studied in conditions which output sensory interact as they do behaving animals, i.e., closed-loop conditions. interaction activities, however, can create emergent properties may govern functional characteristics system. We here demonstrate a method use...
In many fields of research nonlinear dynamical systems are investigated. When more than one process is measured, besides the distinct properties individual processes, their interactions interest. Often linear methods such as coherence used for analysis. The estimation can lead to false conclusions when applied without fulfilling several key assumptions. We introduce a data driven method optimize choice parameters spectral estimation. Its applicability demonstrated based on analytical...
Purpose Blood flow causes induced voltages via the magnetohydrodynamic (MHD) effect distorting electrograms (EGMs) made during magnetic resonance imaging. To investigate MHD in this context occurring inside human heart were simulated an vitro model system a 1.5 T MR system. Methods The was developed to produce signals similar those produced by intracardiac and acquire them using standard clinical equipment. Additionally, new approach estimate distortions on is proposed based analytical...
Abstract In a wide variety of research elds, dynamic modeling is employed as an instrument to learn and understand complex systems. The differential equations involved in this process are usually non-linear depend on many parameters whose values decide upon the characteristics emergent system. inverse problem, i.e. inference or estimation parameter from observed data, interest two points view. First, existence point view, dealing with question whether system able reproduce dynamics for any...
Fine-grained temporal action segmentation in long, untrimmed RGB videos is a key topic visual human-machine interaction. Recent convolution based approaches either use encoder-decoder(ED) architecture or dilations with doubling factor consecutive layers to segment actions videos. However ED networks operate on low resolution and the successive cause gridding artifacts problem. We propose depthwise separable network (DS-TCN) that operates full reduced effects. The basic component of DS-TCN...
Reliable forecasts of extreme but rare events, such as earthquakes, financial crashes, and epileptic seizures, would render interventions precautions possible. Therefore, forecasting methods have been developed which intend to raise an alarm if event is about occur. In order statistically validate the performance a prediction system, it must be compared random predictor, raises alarms independent events. Such predictor can obtained by bootstrapping or analytically. We propose analytic...
We address the challenge of detecting time-variant interactions in multivariate systems.Inferring Granger-causal between processes promises to gain deeper insights into mechanisms underlying network phenomena, e.g., neurosciences.Renormalized partial directed coherence (rPDC) has been introduced as a means investigate Granger causality such systems.When using rPDC major is reliable estimation parameters vector autoregressive processes.For time-varying connections time-resolved coefficients...