A5081999331- Neural dynamics and brain function
- Neuroscience and Neural Engineering
- Functional Brain Connectivity Studies
- Photoreceptor and optogenetics research
- Advanced Memory and Neural Computing
- EEG and Brain-Computer Interfaces
- Neurobiology and Insect Physiology Research
- Memory and Neural Mechanisms
- Neuroscience and Neuropharmacology Research
- Visual perception and processing mechanisms
- Zebrafish Biomedical Research Applications
- Conducting polymers and applications
- Single-cell and spatial transcriptomics
- Olfactory and Sensory Function Studies
- Cell Image Analysis Techniques
- Generative Adversarial Networks and Image Synthesis
- Advanced Neuroimaging Techniques and Applications
- Neural and Behavioral Psychology Studies
- Advanced Materials and Mechanics
- Advanced Graph Neural Networks
- Neural Networks and Applications
- Science Education and Perceptions
- Transcranial Magnetic Stimulation Studies
- Retinal Development and Disorders
- Chaos control and synchronization
RWTH Aachen University
2020-2025
Forschungszentrum Jülich
2020-2024
University of Bonn
2023-2024
University Hospital Bonn
2023-2024
University of Milano-Bicocca
2024
University of Naples Federico II
2024
Cold Spring Harbor Laboratory
2018-2023
Stadtwerke Jülich (Germany)
2022
University of Zurich
2012-2017
Institut des Sciences Cognitives Marc Jeannerod
2015
Abstract Planar microelectrode arrays (MEAs) for – in vitro or vivo neuronal signal recordings lack the spatial resolution and sufficient signal‐to‐noise ratio (SNR) required a detailed understanding of neural network function synaptic plasticity. To overcome these limitations, highly customizable three‐dimensional (3D) printing process is used combination with thin film technology self‐aligned template‐assisted electrochemical deposition to fabricate 3D‐printed‐based MEAs on stiff flexible...
It has long been assumed that the surface electroencephalography (EEG) signal depends on both amplitude and spatial synchronization of underlying neural activity, though isolating their respective contribution remains elusive. To address this, we made simultaneous EEG measurements along with intracortical recordings local field potentials (LFPs) in primary visual cortex behaving nonhuman primates. We found trial-by-trial fluctuations power could be explained by a linear combination LFP...
Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine relationship these signals resulting behavior, it is critical demix recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract different regions in individual mice a data-driven manner, while taking account mouse-specific preparation-specific differences. Here, we...
Abstract Understanding how cortical circuits generate complex behavior requires investigating the cell types that comprise them. Functional differences across pyramidal neuron (PyN) have been observed within areas, but it is not known whether these local extend throughout cortex, nor additional emerge when larger-scale dynamics are considered. We used genetic and retrograde labeling to target tract, intratelencephalic corticostriatal projection neurons measured their cortex-wide activity....
Current neuromodulatory strategies to enhance motor recovery after stroke often target large brain areas non-specifically and without sufficient understanding of their interaction with internal repair mechanisms. Here we developed a novel therapeutic approach by specifically activating corticospinal circuitry using optogenetics strokes in rats. Similar neuronal growth-promoting immunotherapy, optogenetic stimulation together intense, scheduled rehabilitation leads the restoration lost...
Rats and mice receive a constant bilateral stream of tactile information with their large mystacial vibrissae when navigating in environment. In two-alternative forced choice paradigm (2-AFC), head-fixed rats learned to discriminate vibrotactile frequencies applied simultaneously individual whiskers on the left right sides snout. Mice discriminated 90-Hz pulsatile stimuli from lower repetition (10-80 Hz) but identical kinematic properties each pulse. Psychometric curves displayed an average...
Stimulus-specific adaptation (SSA) to repetitive stimulation has been proposed separate behaviorally relevant features from a stream of continuous sensory information. However, the exact mechanisms giving rise SSA and cortical deviance detection are not well understood. We therefore used an oddball paradigm multicontact electrodes characterize single-neuron local field potential responses various deviant stimuli across rat somatosensory cortex. Changing different single-whisker stimulus...
When experts are immersed in a task, do their brains prioritize task-related activity? Most efforts to understand neural activity during well-learned tasks focus on cognitive computations and specific movements. We wondered whether task-performing animals explore broader movement landscape, how this impacts activity. characterized movements using video other sensors measured widefield two-photon imaging. Cortex-wide was dominated by movements, especially uninstructed reflecting unknown...
Abstract Natural scenes consist of complex feature distributions that shape neural responses and perception. However, in contrast to single features like stimulus orientations, the impact broadband remains unclear. We, therefore, presented visual stimuli with parametrically-controlled bandwidths orientations spatial frequencies awake mice while recording activity their primary cortex (V1). Increasing orientation but not frequency bandwidth strongly increased number response amplitude V1...
Abstract The computation of the brain relies on highly efficient communication among billions neurons. Such efficiency derives from brain’s plastic and reconfigurable nature, enabling complex computations maintenance vital functions with a remarkably low power consumption only ∼20 W. First efforts to leverage brain-inspired computational principles have led introduction artificial neural networks that revolutionized information processing daily life. relentless pursuit definitive computing...
Organic neuromorphic platforms have recently received growing interest for the implementation and integration of hybrid systems, acting as a bridge between biological tissue artificial computing architectures.
Chronically implanted neural probes are powerful tools to decode brain activity however, recording population and spiking over long periods remains a major challenge. Here, we designed fabricated flexible intracortical Michigan-style arrays with shank cross-section per electrode of 250 μm[Formula: see text] utilizing the polymer paryleneC goal improve immune acceptance. As unable penetrate due low buckling force threshold, tissue-friendly insertion system was developed by reducing effective...
Existing work demonstrates that animals alternate between engaged and disengaged states during perceptual decision-making. To understand the neural signature of these states, we performed cortex-wide measurements activity in mice making auditory decisions. The trial-averaged magnitude was similar two states. However, trial-to-trial variance higher disengagement. this increased variance, trained separate linear encoding models on data from each state. demonstrated although task variables...
Effectively modeling and quantifying behavior is essential for our understanding of the brain. Modeling in naturalistic settings social multi-subject tasks remains a significant challenge. different subjects performing same task requires partitioning behavioral data into features that are common across subjects, others distinct to each subject. interactions between multiple individuals freely-moving setting disentangling effects due individual as compared investigations. To achieve flexible...
Distance measures between graphs are important primitives for a variety of learning tasks. In this work, we describe an unsupervised, optimal transport based approach to define distance graphs. Our idea is derive representations as Gaussian mixture models, fitted distributions sampled node embeddings over the same space. The Wasserstein these then yields interpretable and easily computable measure, which can further be tailored comparison at hand by choosing appropriate embeddings. We...