جست‌وجوی استاد و پژوهشگر

Dani S. Bassettمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Brain Connectivity
• +۵ مورد دیگر

Dani S. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania with primary appointment in the Department of Bioengineering (School of Engineering and Applied Science) and secondary appointments in Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry. They serve as an external professor at the Santa Fe Institute and lead a research group focused on complex systems and network science. B.S. in Physics, Penn State University (2004) Ph.D. in Physics, University of Cambridge as Churchill Scholar and NIH Health Sciences Scholar (2009) Postdoctoral position at UC Santa Barbara and Junior Research Fellow at Sage Center for the Study of the Mind Their research integrates complex systems science, statistical mechanics, and applied mathematics to study network dynamics in physical and biological systems. Key areas include brain connectivity mechanisms, cognitive processes, neurological disease modeling, granular matter physics, and collective human curiosity. Bassett employs advanced methodologies including algebraic topology, network control theory, and multilayer network analysis to investigate how network architecture influences system function across diverse domains. Recent publications reveal a strong trend toward interdisciplinary network science applications, particularly in modeling human curiosity through Wikipedia navigation patterns and analyzing brain network reconfiguration during cognitive development. Their work bridges physics, neuroscience, and behavioral science with emphasis on topological network properties and dynamical processes. American Psychological Association's Rising Star (2012) MacArthur Fellow Genius Grant (2014) Lagrange Prize in Complex Systems Science (2017) Erdos-Renyi Prize in Network Science (2018) American Physical Society Fellow (2021) Web of Science Highly Cited Researcher (3 consecutive years) Bassett's research is supported by major agencies including NSF, NIH, DoD, ONR, and private foundations (MacArthur, Sloan, Paul Allen). Their lab actively recruits students from physics, engineering, neuroscience, and computer science backgrounds, emphasizing diversity in academic perspectives. Current projects include the 'Curious Minds' initiative exploring collective knowledge building and network-based models of neurological disorders. Bassett co-authored the MIT Press book 'Curious Minds: The Power of Connection' with philosopher Perry Zurn.

Lorenz Fenkمشاهده پروفایل

پژوهشگر

• Behaviour
• Biophysics
• Circuits
• +۵ مورد دیگر

Dr. Lorenz Fenk is a Max Planck Research Group Leader at the Max Planck Institute for Biological Intelligence , leading the Neural Dynamics and Evolution department. His research focuses on neural systems function, natural behavior, and evolutionary neuroscience. Education: PhD in Neurobiology/Genetics (2016), University of Cambridge, UK Diploma (Mag. rer. nat.) in Biology/Anthropology (2010), University of Vienna, Austria His work combines quantitative methods and modern tools to study unconventional model systems like reptiles. Research themes include: Mechanisms of neural circuit dynamics during sleep Interhemispheric competition in reptiles Evolutionary aspects of sleep and cognition Internal brain activity and its link to memory/perception Recent publications demonstrate interdisciplinary trends in sleep research, neural computation, and evolutionary biology. Studies span vertebrate ultradian rhythms, reptilian brainstem circuits, and environmental modulation of behavior in Caenorhabditis elegans . Dr. Fenk's current affiliation is with the Max Planck Institute for Biological Intelligence, where he employs molecular, neurophysiological, and computational approaches to unravel cognitive processes and flexible behavior.

Andreas Herzمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Coding
• Spatial Navigation
• +۳ مورد دیگر

Prof. Dr. Andreas Herz is a Chair in Computational Neuroscience at the Faculty of Biology, Ludwig-Maximilians-Universität München (LMU). His research focuses on understanding neural mechanisms underlying spatial navigation, temporal cognition, and sensory processing. He leads the Computational Neuroscience group and collaborates with the Bernstein Center for Computational Neuroscience Munich. Research Interests: Dr. Herz investigates how neural systems encode spatial and temporal information, including grid cells, head-direction systems, and neural coding strategies. His work bridges experimental neurophysiology with theoretical modeling, addressing topics like cognitive maps, neural variability, and synaptic plasticity. Teaching & Mentorship: He oversees advanced courses on computational neuroscience and neurophysiology, fostering interdisciplinary training for graduate students. His research group includes prominent collaborators like Dr. Martin Stemmler and PD Dr. Kay Thurley, focusing on projects involving neural network dynamics and behavioral neuroscience. Publications & Impact: Over 85 peer-reviewed articles highlight his contributions to understanding entorhinal-hippocampal circuits, dendritic processing, and neural representation of space/time. Key work includes studies on grid cell variability, zebrafish spatial memory, and cytoskeletal organization in dendritic spines.

Hermann Cuntzمشاهده پروفایل

پژوهشگر

• Neuroscience
• Computational Neuroscience
• Neuroinformatics
• +۴ مورد دیگر

Dr. Hermann Cuntz is an Independent Group Leader at the Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society. He is also a Research Fellow at the Frankfurt Institute for Advanced Studies (FIAS) since 2014 and affiliated with the Goethe University Frankfurt via the Institute of Clinical Neuroanatomy . His email address is hermann.neuro@gmail.com , and he is based in Frankfurt am Main, Germany. Research Focus: Dr. Cuntz investigates principles of neuronal wiring, aiming to decode the "connection code" of the brain. His work bridges morphology and function using computational tools, mathematical laws, and morphological modeling. Key areas include dendritic constancy , structural plasticity , connectomics , and neuroinformatics , with applications in understanding neurodegenerative diseases like Alzheimer’s. Education: PhD from the University of California at Berkeley and Max Planck Institute of Neurobiology (2000-2004). Diploma in biology from Eberhard Karls Universität Tübingen (1994-2000). Recent Publications highlight research trends in dendritic structure, pattern separation, synaptic spine distribution, and cortical folding. His lab develops the TREES Toolbox , a MATLAB-based framework for neuronal morphology analysis. Scientific Awards: Bernstein Award (2013-2019) DFG Eigene Stelle (2014-2016) Feodor Lynen Fellowship (Alexander von Humboldt, 2006-2008) Minerva Fellowship (2004-2005) Wellcome Image Award (2011) 1st Prize Poster Competition, UCL Neuroscience Symposium (2010) Advising and Grants: Dr. Cuntz mentors numerous PhD and Master’s students, including Marcel Beining , Mariuss Schneider , and Marvin Weigand . His lab receives funding from the DFG , Bernstein Award , and collaborations with institutions like the 3R-Center Giessen and Interdisciplinary Centre for 3Rs (ICAR3R) . Labs and Collaborations: The Cuntz Lab specializes in computational neuroanatomy, with alumni working globally in academia and industry. Key collaborators include Prof. Peter Jedlicka (Justus Liebig University), Prof. Gaia Tavosanis (DZNE Bonn), and Prof. Alexander Borst (MPI Neurobiology).

Julijana Gjorgjievaمشاهده پروفایل

استاد

• Computational Neuroscience
• Theoretical Neuroscience
• Neural Circuit Development
• +۴ مورد دیگر

Prof. Julijana Gjorgjieva is a tenured W3 Professor of Computational Neuroscience at the School of Life Sciences Weihenstephan, Technical University of Munich (TUM). She leads an independent research group at the Max Planck Institute for Brain Research and is affiliated with the Bernstein Center for Computational Neuroscience. Her research focuses on the principles governing neural circuit development, balancing learning plasticity with functional stability through computational and theoretical approaches. Key interests include synaptic organization, energy-efficient neural computation, and evolutionary optimality principles. Education & Career: B.Sc. Mathematics, Harvey Mudd College (2006) M.A.St. in Applied Mathematics, University of Cambridge (2007) Ph.D. Applied Mathematics, University of Cambridge (2011) Postdoctoral Fellowships: Harvard University (2011-2014), Brandeis University (2014-2016) Max Planck Research Group Leader (2016-2022) W2/W3 Professor at TUM since 2016 Research Interests: Computational neuroscience, theoretical modeling of neural circuits, synaptic plasticity mechanisms, homeostatic regulation, and the interplay of development and evolution in shaping brain architecture. She employs mathematical frameworks to study how circuits achieve robustness while enabling adaptive learning. Awards: Heinz Maier-Leibnitz Prize (2022) Eric Kandel Young Neuroscientist Prize (2021) ERC Starting Grant (2018) Multiple postdoctoral and early-career fellowships Grants & Funding: Includes DFG Collaborative Research Center on Neural Homeostasis, HFSP grants, and EU Horizon 2020 initiatives. Active in mentoring and promoting computational neuroscience through programs like Neuromatch Academy. Labs & Collaborations: Leads a multidisciplinary lab integrating experimental and theoretical approaches. Collaborates with institutions such as the Max Planck Society and international computational neuroscience networks.

• Neural coding
• Spatial navigation
• Memory systems
• +۴ مورد دیگر

Dr. Eric T. Reifenstein is a neuroscience researcher at Humboldt-Universität zu Berlin's Faculty of Life Sciences, Institute of Biology, specializing in neural mechanisms of spatial navigation and memory. As a key contributor to SFB 1315 (Entorhinal Cortex as Interface between Memory and Space), his work bridges computational modeling and human electrophysiology. His research focuses on egocentric spatial mapping and sequence learning mechanisms , with particular expertise in human single-neuron recordings during virtual navigation tasks. Reifenstein investigates how the brain encodes self-centered spatial representations and how synaptic learning rules enable temporal sequence memorization. His publication trends reveal a strong emphasis on translational neuroscience , connecting rodent studies to human cognition through innovative virtual reality paradigms. Key methodological approaches include single-neuron recordings, computational modeling of neural networks, and behavioral analysis of spatial memory. Scientific contributions include: Identification of egocentric bearing cells in human parahippocampal cortex Mathematical analysis of phase precession in sequence learning Vectorial representation models of egocentric space Reifenstein actively collaborates with leading researchers including Prof. Richard Kempter and Joshua Jacobs, contributing to major neuroscience journals like Neuron and eLife. His work has implications for understanding memory disorders and developing neural prosthetics.

• Gene Therapy
• Vascular Biology
• Neurology
• +۳ مورد دیگر

Jakob Körbelin is a Principal Investigator at the University Medical Center Hamburg-Eppendorf (UKE), affiliated with the Faculty of Medicine and the II. Medical Clinic and Polyclinic. His research focuses on vascular biology, gene therapy, and neurological disorders, particularly targeting the blood-brain barrier using adeno-associated viral (AAV) vectors. He leads studies on pulmonary hypertension, neurovascular interactions, and genetic diseases like Niemann-Pick type C2. His work bridges basic science and translational medicine, emphasizing AAV engineering and therapeutic applications. Key research interests include endothelial cell biology, neuroinflammation, and the pathophysiology of vascular diseases. Notably, he received the UCCH Hubertus Wald Young Investigator Award 2013 for his contributions. His lab explores mechanisms of vascular dysfunction, gene delivery optimization, and the impact of viral vectors in treating rare diseases. Collaborations span molecular neurobiology, immunology, and translational oncology. Publications highlight breakthroughs in AAV-mediated therapies, such as reversing neurodegeneration in NPC2 models and identifying novel targets for pulmonary hypertension. Ongoing projects address microvascular brain pathology in SARS-CoV-2 infection and the role of transcription factors in vascular diseases.

• Medical Image Computing
• Tumor Growth Modeling
• Computational Physiology
• +۳ مورد دیگر

Bjoern Menze is a Professor and Rudolf Mößbauer Tenure Track Chair at the Technical University of Munich (TUM), leading the Image-based Biomedical Modeling Group within the Munich School of Bioengineering. His research focuses on medical image computing, tumor growth modeling, and computational physiology, with applications in clinical neuroimaging and personalized radiotherapy design. He holds a Ph.D. in Computer Science from Heidelberg University and has held positions at ETH Zurich, INRIA Sophia Antipolis, MIT, and Harvard Medical School. His academic journey includes a postdoc at MIT’s CSAIL and Harvard Medical School, followed by roles at ETH Zurich and INRIA. His work bridges biomedical imaging with machine learning, emphasizing model-driven analysis of physiological processes. He has been a visiting professor at Maastricht University and contributes to initiatives like the Center for Translational Cancer Research at TUM. Key research areas include tumor growth modeling, quantitative imaging biomarkers, and integrating mathematical models with clinical data. His awards include the MICCAI Young Scientist Award (2014), Leopoldina Fellowship (2009), and DFG Research Fellowship (2008). He advises on medical AI, leads interdisciplinary projects, and publishes extensively in top journals like Nature Neuroscience and IEEE Transactions on Medical Imaging. His lab’s work spans applications such as glioblastoma radiotherapy optimization, whole-body bone lesion detection, and neural connectivity imaging. Collaborations include institutions like Harvard, MIT, and ETH Zurich. He emphasizes translating computational methods into clinical practice for personalized healthcare solutions.

• Complex Systems Modeling
• Computational Mathematics
• Applied Mathematics
• +۷ مورد دیگر

Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS

• Computational Neuroscience
• Visual System
• Sensory Coding
• +۷ مورد دیگر

Thomas Wachtler-Kulla is a Professor in the Department of Biology II at Ludwig-Maximilians-University Munich, where he leads the Computational Neuroscience research group. He is a GSN full member and serves as the GSN Ombudsperson, offering neutral and confidential counseling for students. He is also the Group Leader and Scientific Director at the German Neuroinformatics Node (G-Node), contributing significantly to neuroscience data infrastructure. His research focuses on how the brain processes sensory signals, particularly in the visual system, aiming to understand neural coding, perceptual stability, and color vision under natural conditions. He employs neurophysiology, psychophysics, and computational modeling to investigate sensory processing, eye movement compensation, and efficient coding mechanisms. At G-Node, he develops software and hardware tools for organizing, storing, analyzing, and sharing neurophysiological data, promoting reproducible research. His recent work spans Bayesian models of hue perception, data management frameworks like DataLad and odML, and studies on honeybee neuroethology. He actively supervises graduate students and contributes to major initiatives such as NFDI-Neuro and the International Neuroinformatics Coordinating Facility, advancing standards for open and FAIR neuroscience. Scientific Contributions and Leadership: Scientific Director, German Neuroinformatics Node (G-Node) GSN Ombudsperson for student conflict resolution Key contributor to NFDI-Neuro and INCF Developer of tools for metadata management and data sharing He advises several current and former graduate students and is deeply involved in shaping data policies and infrastructure for the neuroscience community, ensuring scientific rigor and accessibility.

• Neuroscience
• Neurobiology
• Computational Neuroscience
• +۶ مورد دیگر

Prof. Dr. Fred Wolf is a leading scientist affiliated with the Campus Institute for Dynamics of Biological Networks (CIDBN) at Georg-August-Universität Göttingen. His research focuses on the intersection of neuroscience, computational biology, and epithelial morphogenesis, utilizing advanced imaging techniques and theoretical models to study neural circuits and tissue dynamics.

Richard Kempterمشاهده پروفایل

استاد

• Theoretical Neuroscience
• Computational Neuroscience
• Neural basis of learning and memory
• +۷ مورد دیگر

Richard Kempter is a Full Professor at the Humboldt-Universität zu Berlin, where he leads the Theoretical Neuroscience research group within the Institute for Theoretical Biology, Department of Biology. His research focuses on the neural basis of learning and memory through computational and mathematical modeling of synapses, neurons, and neural networks. He is affiliated with several major research centers including the Bernstein Center for Computational Neuroscience, the Einstein Center for Neurosciences Berlin, and the CRC 1315 Memory Consolidation. Professor Kempter's research interests span theoretical and computational neuroscience with a particular focus on the neural mechanisms underlying learning and memory. His work employs biophysical modeling and mathematical analysis to study synaptic short- and long-term plasticity, the dynamics of single neurons, and the interaction of neurons in recurrently coupled networks. A key aspect of his research investigates how neural systems maintain a balance between learning susceptibility and stability against pathological activity patterns, with model systems including the hippocampus and early auditory system. His research group has made significant contributions to understanding hippocampal sharp wave-ripple events, phase precession in spatial navigation, auditory processing in barn owls, and memory consolidation mechanisms. The group's work combines theoretical approaches with computer simulations to unravel the computational principles of neural circuits, showing particular interest in how neural tissue remains susceptible to learning while maintaining robust stability against pathological activity patterns. Scholarship of the State of Bavaria (03/1994-12/1995) Emmy Noether Fellowship Part I (09/1999-08/2001), funded by the Deutsche Forschungsgemeinschaft Emmy Noether Fellowship Part II (01/2003-09/2008) Guest Professor , HU Berlin, Department of Biology (10/2008-03/2010) Professor Kempter has advised numerous PhD and Master's students throughout his career, with many continuing in neuroscience research. His group maintains strong connections with experimental laboratories to bridge computational models with empirical findings, particularly in hippocampal function and auditory processing. The Theoretical Neuroscience Lab participates in collaborative projects investigating memory consolidation and neural coding principles, contributing significantly to our understanding of how neural circuits implement computational principles underlying learning and memory.

Katrin Vogtمشاهده پروفایل

پژوهشگر

• Behavioral Neuroscience
• Neural Circuits
• Sensory Processing
• +۷ مورد دیگر

Katrin Vogt is a Group Leader at the University of Konstanz and an Affiliated Scientist at the Max Planck Institute of Animal Behavior. She serves on the IMPRS Board and Faculty, focusing on behavioral neuroscience in Drosophila larvae. Her research explores how social context and internal states (e.g., hunger) modulate neural circuits and behavior, utilizing genetic tools like optogenetics, RNAi, and CRISPR. Key Research Areas: Behavioral flexibility under internal state changes Neural integration of sensory and state signals in the antennal lobe Role of serotonin (CSD neuron) in modulating output pathways Computational modeling of state-dependent circuit dynamics Notable Achievements: Discovered state-dependent olfactory valence switching (e.g., geranyl acetate shifts from aversion to attraction under food deprivation) Elucidated glutamatergic inhibition mechanisms in picky local interneurons Identified 5-HT7 receptor's role in upregulating uniglomerular projection neuron activity Recent Publications: 2025: PLoS Biology on multimodal sensory neurons 2024: Current Biology commentary on behavioral neuroscience 2023: Current Biology on multisensory memory merging Academic Affiliations: University of Konstanz (Group Leader, Department of Collective Behavior) Max Planck Institute of Animal Behavior (Affiliated Scientist) IMPRS for Organismal Biology (Faculty Member) Scientific Awards: DFG Research Fellowship (Project No. 345729665) Students & Collaborators: PhD students: Hari P. Narayanan, Akhila Mudunuri Research assistants: Nora Tutas, Julius Klein, Constantin Dyroff DAAD summer student: Élyse Zadigue-Dubé Recent graduates: Amelie Edmaier (BSc 2023), Constantin Dyroff (BSc 2023)

• Theoretical Neuroscience
• Statistical Physics of Neural Networks
• Dynamics of Neuronal Networks
• +۶ مورد دیگر

Prof. Dr. Moritz Helias is a University Professor and leads the Theory of Multi-Scale Neuronal Networks group at the Institute for Advanced Simulation (IAS-6), Computational and Systems Neuroscience, Forschungszentrum Jülich. His research bridges biological and artificial neural networks, focusing on dynamics, information processing, and the physics of AI. The group is part of a larger interdisciplinary institute that integrates theory, simulation, and data analysis to understand the brain. Institution: Forschungszentrum Jülich School: Institute for Advanced Simulation Department: IAS-6, Computational and Systems Neuroscience Position: Professor and Group Leader Email: m.helias@fz-juelich.de His research interests lie at the intersection of statistical physics and neuroscience. He investigates how structure shapes dynamics in both biological and artificial networks, aiming to uncover general principles of information processing. Using methods from statistical physics, his work enables a unified framework for understanding collective phenomena, learning, and generalization. Key areas include spiking neural networks, renormalized field theory, and the theoretical foundations of AI. The recent publications reflect a strong trend toward multi-scale modeling of neural systems, integrating statistical physics with neuroscience. Topics include spiking network dynamics, mean-field theory, renormalization, and applications of machine learning in physics. The work spans biological realism and artificial intelligence, with implications for neuromorphic computing and brain-inspired AI architectures. While no scientific awards are listed in the provided texts, his group actively contributes to open science through tools like NEST and theoretical frameworks that influence both neuroscience and AI. Prof. Helias supervises a research group focused on theoretical and computational approaches, contributing to collaborative projects involving large-scale simulations and data analysis. His team works closely with experimentalists and theorists to validate models and advance understanding of brain function. The group is also involved in developing simulation technologies and theoretical tools that support reproducible neuroscience. The Theory of Multi-Scale Neuronal Networks group is embedded within a vibrant research environment at IAS-6, collaborating with teams in statistical neuroscience, computational neurophysics, and future simulation architectures. This fosters a loop between data, theory, and simulation, enabling cutting-edge research on brain function and artificial intelligence.

Gerhard Wolberمشاهده پروفایل

استاد

• Computational Drug Design
• G-Protein Coupled Receptors (GPCRs)
• Cytochrome P450 Enzymes
• +۳ مورد دیگر

Professor Gerhard Wolber leads the Molecular Drug Design research group at the Institute of Pharmacy , Freie Universitaet Berlin. His work focuses on computational approaches to drug discovery, with expertise in G-protein coupled receptors (GPCRs) , cytochrome P450 enzymes , Toll-like receptors , and viral protease inhibitors . He supervises a team of 13 PhD candidates 3 researchers 2 Master's students engaged in projects ranging from calcium channel blockers to CYP enzyme modulators for cancer therapy. Recent publications highlight his lab's contributions to pan-coronavirus drug discovery, TLR8 antagonism, and calcium channel inhibition. The team employs advanced methodologies including Molecular dynamics simulations Fragment-based de novo design Bayesian neural networks DFT calculations 3D pharmacophore modeling to bridge computational predictions with experimental validation. Notable projects include Virtual screening for TREM2-targeted glioblastoma therapeutics Allosteric communication path analysis via MDPath Immune checkpoint inhibitors for cancer immunotherapy Biased GPCR ligand development demonstrating a multidisciplinary approach to contemporary drug design challenges.