A5029568066- Neural dynamics and brain function
- Advanced Memory and Neural Computing
- Functional Brain Connectivity Studies
- Laser-Plasma Interactions and Diagnostics
- CCD and CMOS Imaging Sensors
- Neural Networks and Applications
- Neuroscience and Neural Engineering
- Laser-Matter Interactions and Applications
- Laser Design and Applications
- Integrated Circuits and Semiconductor Failure Analysis
- Advanced MRI Techniques and Applications
- Laser-induced spectroscopy and plasma
- Electrochemical Analysis and Applications
- Advancements in Photolithography Techniques
- Dust and Plasma Wave Phenomena
- Banking Systems and Strategies
- Cell Image Analysis Techniques
- Visual Attention and Saliency Detection
- EEG and Brain-Computer Interfaces
- Computer Graphics and Visualization Techniques
- Magnetic confinement fusion research
- Electron and X-Ray Spectroscopy Techniques
- Medical Image Segmentation Techniques
- Semantic Web and Ontologies
- Geological Modeling and Analysis
RWTH Aachen University
2022-2024
Jülich Aachen Research Alliance
2020-2024
Forschungszentrum Jülich
2020-2024
Heinrich Heine University Düsseldorf
2016-2018
Abstract Although the structure of cortical networks provides necessary substrate for their neuronal activity, alone does not suffice to understand activity. Leveraging increasing availability human data, we developed a multi-scale, spiking network model cortex investigate relationship between and dynamics. In this model, each area in one hemisphere Desikan–Killiany parcellation is represented by $1\,\mathrm{mm^{2}}$ column with layered structure. The aggregates data across multiple...
Abstract The primate brain uses billions of interacting neurons to produce macroscopic dynamics and behavior, but current methods only allow neuroscientists investigate a subset the neural activity. Computational modeling offers an alternative testbed for scientific hypotheses, by allowing full control system. Here, we test hypothesis that local cortical circuits are organized into joint clusters excitatory inhibitory investigating influence this organizational principle on resting-state...
We introduce a first full analytical bubble and blow-out model for radially inhomogeneous plasma in quasi-static approximation. For both cases we calculate the accelerating focusing fields. In our also assume thin electron layer that surrounds wake field configuration within. Our theory holds arbitrary radial density profiles reduces to known models limit of homogeneous plasma. From previous study hollow channels with smooth boundaries laser-driven acceleration regime know pancake-like laser...
Modern computational neuroscience strives to develop complex network models explain dynamics and function of brains in health disease. This process goes hand with advancements the theory neuronal networks increasing availability detailed anatomical data on brain connectivity. Large-scale that study interactions between multiple areas intricate connectivity investigate phenomena long time scales such as system-level learning require progress simulation speed. The corresponding development...
Simulation is a third pillar next to experiment and theory in the study of complex dynamic systems such as biological neural networks. Contemporary brain-scale networks correspond directed random graphs few million nodes, each with an in-degree out-degree several thousands edges, where nodes edges fundamental units, neurons synapses, respectively. The activity neuronal also sparse. Each neuron occasionally transmits brief signal, called spike, via its outgoing synapses corresponding target...
Generic simulation code for spiking neuronal networks spends the major part of time in phase where spikes have arrived at a compute node and need to be delivered their target neurons. These were emitted over last interval between communication steps by source neurons distributed across many nodes are inherently irregular unsorted with respect targets. For finding those targets, dispatched three-dimensional data structure decisions on thread synapse type made way. With growing network size,...
Spiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and understanding relationship between these brain function. Furthermore, continuous development parallel computing technologies growing availability computational resources leading to era large-scale simulations capable describing regions ever larger dimensions at increasing detail. Recently, possibility use MPI-based codes on GPU-equipped clusters...
Abstract Although the structure of cortical networks provides necessary substrate for their neuronal activity, alone does not suffice to understand it. Leveraging increasing availability human data, we developed a multi-scale, spiking network model cortex investigate relationship between and dynamics. In this model, each area in one hemisphere Desikan-Killiany parcellation is represented by 1 mm 2 column with layered structure. The aggregates data across multiple modalities, including...
In this work, we study electron side-injection and trapping in the blow-out regime deep plasma channels. We analyze maximum angle of injection, for which at least 90% injected electrons are trapped. discuss dependence on electrons' initial energy their injection positions. scope a semi-analytical model, show that position is less critical factor if into Photonic integrated circuit simulations analytical approximations support our results from model. Furthermore, discussion temporal evolution...
In this work we study electron side-injection and trapping in the blow-out regime deep plasma channels. We analyze maximum angle of injection, for which at least 90\% injected electrons are trapped. discuss dependence on electrons' initial energy their injection positions. scope a semi-analytical model show that position is less critical factor if into PIC simulations analytical approximations support our results from model.
We are entering an age of `big' computational neuroscience, in which neural network models increasing size and numbers underlying data sets. Consolidating the zoo into large-scale simultaneously consistent with a wide range is only possible through effort large teams, can be spread across multiple research institutions. To ensure that neuroscientists build on each other's work, it important to make publicly available as well-documented code. This chapter describes such open-source model,...