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Aidan J HornerView profile

Professor

• Cognitive Neuroscience
• Memory Research
• Neuroimaging
• +3 more

Aidan J Horner is a Professor in the Department of Psychology at the University of York. He holds a BSc in Psychology (2005) and MSc in Cognitive Neuroscience (2006) from the University of York, followed by a PhD in Cognitive Neuroscience from the University of Cambridge (2010). His career includes postdoctoral research at Otto-von-Guericke University (2010–2011) and University College London (2011–2016), and a visiting scholar position at Stanford University (2008). He returned to York as a Lecturer in 2016, advancing to Senior Lecturer and his current Professorship. Research Focus: Horner’s work examines how the brain encodes and retrieves long-term memories, particularly spatial and event-based information. He employs experimental psychology, virtual reality, neuroimaging (e.g., fMRI, MEG), and computational modeling to study hippocampal and cortical mechanisms underlying memory formation, consolidation, and forgetting. His recent studies explore the role of theta oscillations in memory binding, the impact of emotion on memory coherence, and forgetting dynamics. Publications & Awards: Over 50 peer-reviewed articles, including high-impact work in Current Biology , Nature Communications , and Cognition . Recognized with the Annual Cognitive Paper Prize Award (2022) for groundbreaking contributions to memory research. Grants & Projects: Lead investigator on ESRC-funded projects (e.g., "Promoting rapid and sustained learning of novel information" , 2018–2022). Collaborates with institutions like the York Neuroimaging Centre (YNiC) to advance neuroimaging techniques in cognitive studies. Labs & Teams: Affiliated with the York Neuroimaging Centre (YNiC), integrating neuroimaging with behavioral and computational approaches to memory systems. Active in interdisciplinary teams studying memory plasticity and cognitive neuroscience.

Jason RittView profile

Associate Professor

• Computational Neuroscience
• Data Science
• Neuroengineering
• +3 more

Jason Ritt is an Associate Professor of Brain Science (Research) and Scientific Director of Quantitative Neuroscience at the Robert J. and Nancy D. Carney Institute for Brain Science, Brown University. He holds affiliations with the Data Science Institute and collaborates across disciplines on quantitative research methods. Education : B.S., M.A., and Ph.D. in Neuroscience from Boston University (1997–2003). Research : Focuses on neural processing during active sensing and neuroengineering for neurostimulation. Combines electrophysiology, optogenetics, and theoretical approaches in rodent models. Develops closed-loop systems for studying sensory neural prosthetics and brain-machine interfaces. Key areas include synaptic diversity, neurocontrol algorithms, and sensory restoration. Teaching : Instructs NEUR 2100 NeuroPracticum, integrating hands-on neuroscience research training.

Kevin M. FranksView profile

Associate Professor

• Neurobiology
• Olfactory System
• Neural Circuits
• +3 more

Kevin M. Franks is an Associate Professor of Neurobiology at Duke University, where he investigates how the olfactory system forms neural representations of sensory environments. His work focuses on functional neural circuits in the olfactory bulb and piriform cortex, using techniques like in vivo recordings, optogenetics, and behavioral assays. His research explores Neural circuit dynamics and plasticity Odor coding mechanisms Role of recurrent circuitry Integration of sensory modalities Recent publications highlight his contributions to understanding cortical odor representations, developmental neural connectivity, and cross-modal interactions. Awards include the 2024 Don Tucker Finalist recognition. He teaches advanced neuroscience courses at Duke, including Neurobiology research and concepts in neuronal systems.

• Neural circuit mechanisms
• Adaptive learning
• State-dependent decision-making
• +4 more

Dr. Sabine Krabbe is a Group Leader at the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany, where she leads research on neural circuit mechanisms underlying adaptive learning and state-dependent decision-making. Her work integrates neuroscience, molecular biology, and behavioral approaches to understand how internal states influence behavior and how these processes are disrupted in neurological disorders. Dr. Krabbe's research focuses on the interactions between midbrain circuits of the substantia nigra and ventral tegmental area with their output structures such as the striatum and amygdala. She investigates how these networks integrate internal states with environmental cues to produce appropriate behavioral responses. Her laboratory employs state-of-the-art techniques including deep-brain calcium imaging at single-cell resolution in mice, opto- and pharmacogenetic manipulations, anatomical tracings, and molecular approaches to characterize neural circuit elements in detail. Her recent publications reveal significant insights into amygdala interneuron plasticity during fear learning, brain-wide representational drift in memory consolidation, and the molecular mechanisms underlying Parkinson's disease progression. Her work demonstrates how activity patterns within specific neural circuits change in early stages of neurodegenerative diseases and how this dysfunction contributes to cognitive deficits and emotional disturbances. Dr. Krabbe is actively involved in the neuroscience community, organizing the BonnBrain Conference 2026 and sharing research through social media platforms. She has established herself as an emerging leader in the field of systems neuroscience with a particular focus on the neural basis of emotional states and decision-making processes.

Stefano FusiView profile

Associate Professor

• Computational Neuroscience
• Theoretical Neuroscience
• Neural Circuit Modeling
• +2 more

Stefano Fusi is an Associate Professor of Neuroscience at Columbia University's Vagelos College of Physicians and Surgeons, with joint affiliations at the Mortimer B. Zuckerman Mind Brain Behavior Institute and Kavli Institute. His laboratory focuses on computational modeling of neural circuits and neuromorphic engineering. Education PhD in Physics, Hebrew University of Jerusalem (1999) BS in Physics, Sapienza University of Rome (1992) Research Focus Fusi investigates how biological complexity supports neural computation through three primary domains: theoretical analysis of neural circuit dynamics, representational geometry in learning systems, and hardware implementations of brain-inspired algorithms. His work bridges machine learning, neurophysiology, and theoretical physics, emphasizing high-dimensional representations and memory optimization. Recent publications demonstrate consistent focus on neural coding principles across hippocampus, prefrontal cortex, and sensory systems, with innovations in modeling working memory, stress responses, and cross-species computational paradigms. Collaborations & Labs Leads an interdisciplinary laboratory collaborating with Columbia experimental neuroscientists, MIT engineers, and Stanford computational researchers to validate theoretical models. Current projects include neuromorphic hardware development and neural decoding of emotional states.

• Inverse problems and reconstruction
• Neuroimaging
• Quantitative MRI
• +1 more

Daan Christiaens is a tenure track lecturer at KU Leuven's Faculty of Medicine and Faculty of Engineering Sciences. He is affiliated with the Department of Electrical Engineering (ESAT) and Department of Imaging & Pathology, serving as a member of the Medical Imaging Division and the KU Leuven Brain Institute (LBI). His academic responsibilities include membership in the Faculty Councils of Engineering Sciences and Medicine. His research focuses on: Inverse problems in medical imaging reconstruction Neuroimaging techniques for brain analysis Advanced quantitative MRI methodologies Diffusion-weighted imaging for microstructural assessment Dr. Christiaens' recent publications (2023-2025) demonstrate a consistent focus on diffusion MRI innovations, including novel reconstruction algorithms, neonatal brain development mapping, and clinical applications for neurodegenerative disorders. Key technical themes include motion correction, multi-shell modeling, and AI-enhanced image processing, while clinical applications span Alzheimer's disease, cerebral palsy, and autism research. He leads significant research projects including: MRI reconstruction with dynamic field monitoring (2024-2028) Compressed sensing for microstructure imaging (2022-2026) Neonatal diffusion MRI network connectivity analysis (2024-2028) As a core developer of the MRtrix3 software framework for medical image processing, he contributes to essential tools in neuroimaging research.

Surya GanguliView profile

Associate Professor

• Theoretical Neuroscience
• Computational Neuroscience
• Machine Learning
• +4 more

Surya Ganguli is an Associate Professor in the Department of Applied Physics at Stanford University, with courtesy appointments in Neurobiology and Electrical Engineering. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and is affiliated with the Stanford Neuroscience Institute , Bio-X , and Wu Tsai Neurosciences Institute . His research spans theoretical neuroscience, machine learning, and statistical mechanics. Ph.D. , UC Berkeley, Theoretical Physics (2004) M.A. , UC Berkeley, Physics (2000) M.A. , UC Berkeley, Mathematics (2004) M.Eng. , MIT, Electrical Engineering and Computer Science (1998) B.S. , MIT, Physics (1998) B.S. , MIT, Mathematics (1998) B.S. , MIT, Electrical Engineering and Computer Science (1998) His lab explores how higher-level cognitive phenomena emerge from neural network dynamics, focusing on perception, memory, attention, and decision-making . Research themes include statistical mechanics of learning , neural representational geometry , and biologically plausible learning rules . Current work examines nonlinear interactions in neural networks through the Schmidt Science Polymath Award (2023). Key article trends reveal expertise in neural coding limits (2022 Nature), synaptic plasticity models (2022 Neural Computation), and deep learning theory (2022 NeurIPS publications). His 2019 Annual Review chapter on statistical mechanics of deep learning established foundational insights into network criticality. Scientific Awards : NSF Career Award (2019) Simons Foundation Investigator (2016) McKnight Scholar Award (2015) James S. McDonnell Foundation Scholar (2014) Sloan Research Fellow (2013) As advisor, he mentors 10+ doctoral students across Applied Physics, Neurosciences, and Computer Science, including Vamshi Balanaga and Mason Kamb. His lab collaborates with experimental teams at Stanford and beyond, supported by grants from NSF , Simons Foundation , and Swartz Foundation . The Neural Dynamics & Computation Lab unites physicists, mathematicians, and neuroscientists to decode cognition through interdisciplinary methods.

• Neuroscience
• Synaptic Plasticity
• Neural Circuit Formation
• +4 more

Colin J Akerman is Professor of Neuroscience and Group Leader in the Department of Pharmacology at the University of Oxford, concurrently serving as Corange Fellow and Medical Tutor at Corpus Christi College. His research investigates fundamental mechanisms of synaptic circuit formation and plasticity, with direct implications for epilepsy, dementia, and schizophrenia through multidisciplinary approaches integrating electrophysiology, optical imaging, and computational modeling. His primary research interests encompass Synaptic Plasticity, Neural Circuit Formation, and Excitatory-Inhibitory Balance, with specific focus on neuronal progenitor influences on connectivity, chloride dynamics in inhibitory transmission, and learning mechanisms in disease contexts. The lab employs custom-built equipment and molecular tools to probe synaptic function across in vivo , in vitro , and in silico platforms, emphasizing how activity-dependent processes shape neural networks during development and disease. Recent publications (2023-2025) reveal strong thematic convergence on intracellular chloride regulation in sleep-wake cycles, cortical circuit assembly from embryonic progenitors, and innovative optical tools for neural monitoring. This work bridges molecular neuroscience with systems-level understanding of synaptic plasticity, particularly regarding ionic mechanisms in epilepsy and sleep homeostasis. No scientific awards or fellowships are explicitly documented in the source materials. Professor Akerman currently mentors four PhD students (Vourvoukelis, Selfe, Wang, Gemayel) and multiple postdoctoral researchers, having previously trained scientists now leading independent groups in Toronto, Edinburgh, Cape Town, Oxford, and London. His research is funded by the European Research Council, Innovative Medicines Initiative, and Wellcome Trust, supporting investigations into synaptic mechanisms underlying neurological disorders. The Akerman Group, established in 2008, operates as an integrative neuroscience hub within Oxford's Pharmacology Department. The 10-member team combines expertise in patch-clamp electrophysiology, optogenetics, multiphoton imaging, and computational modeling, with current projects spanning neuronal progenitor biology, inhibitory synaptic plasticity, and learning rule implementation in neural networks. The lab emphasizes technical innovation, regularly developing custom instrumentation and molecular tools for neural observation and manipulation.

• Biomedical Signal and Image Processing
• Cardiovascular and Neural Modeling
• Physiologically-interpretable Artificial Intelligence systems
• +5 more

Gaetano Valenza is an Associate Professor of Bioengineering at the University of Pisa, Italy, where he leads the Neuro-Cardiovascular Intelligence Lab at the Enrico Piaggio Research Centre. He holds affiliations with the Neuroscience Statistics Research Laboratory at MIT and has served as a Research Fellow at Harvard Medical School and Massachusetts General Hospital. His academic work spans bioengineering, computational physiology, and affective computing. His research focuses on statistical and nonlinear biomedical signal and image processing , cardiovascular and neural modeling , and physiologically interpretable artificial intelligence . He develops wearable systems for physiological monitoring, with applications in autonomic nervous system assessment, brain-heart interactions, and mental health. His work has led to novel metrics such as the Sympathetic and Parasympathetic activity indices derived from ECG. The 15 most recent publications reflect a consistent trend in brain-heart interplay , complexity analysis of physiological signals , explainable AI in healthcare , and virtual reality applications in mental health . His work integrates advanced signal processing, nonlinear dynamics, and machine learning to decode emotional and cognitive states from physiological data. Dr. Valenza is a Senior Member of IEEE and serves on several technical committees. He is an active editorial leader, currently serving as Associate Editor for IEEE-EMBC , Plos One , Complexity , and Scientific Reports , and has guest-edited special issues in Philosophical Transactions of the Royal Society A and IEEE Journal of Biomedical and Health Informatics . He has led or participated in numerous international research projects, including FP7 and H2020 initiatives such as NEVERMIND and EXPERIENCE. He teaches courses in Biostatistics, Probability & Biostatistics, and Advanced Image Processing at the University of Pisa. As lab head and project coordinator, he leads a multidisciplinary team working on neuro-cardiovascular intelligence, wearable systems, and AI-driven mental health interventions.

Hayriye CagnanView profile

Senior Lecturer

• Neuroscience
• Neuromodulation
• Brain Stimulation
• +4 more

Dr. Hayriye Cagnan is a Senior Lecturer (equivalent to Associate Professor) at Imperial College London and Associate Unit Member at the MRC Brain Network Dynamics Unit. She holds a PhD in Neuroscience from the University of Amsterdam and Philips Research Laboratories (2010), an MSc in Engineering and Physical Science in Medicine from Imperial College London, and a BS in Electrical and Electronics Engineering from Cornell University as a Fulbright Scholar. Her research develops theory-driven strategies for selective modulation of brain rhythms using: Theoretical disease models and stimulation paradigms Dynamic neuromodulation approaches Wearable devices for remote therapy optimization Advanced signal processing techniques Her recent publications (2015-2025) demonstrate consistent focus on tremor pathophysiology, neural oscillations in Parkinson's disease, and advanced brain stimulation technologies. Research employs computational modeling, EEG, magnetoencephalography, and both invasive/non-invasive stimulation techniques. Awards & Fellowships: Fulbright Scholarship (2000-2004) British Chevening Scholarship (2004) MRC Skills Development Fellowship (2015) MRC Career Development Award (2018) Dr. Cagnan leads an independent research group originally established at the MRC Brain Network Dynamics Unit in 2018, maintaining close collaborations with clinical neuroscientists and engineers to translate basic research into therapeutic applications.

Xiaoyao FanView profile

Assistant Professor

• Image Guidance
• Neurosurgery
• Brain Deformation
• +8 more

Xiaoyao Fan is an Assistant Professor of Engineering at Dartmouth College, specializing in image guidance systems for neurosurgery and spine surgery. His work focuses on improving intraoperative imaging accuracy through computational modeling, stereovision, and ultrasound technologies. He collaborates with the Center for Surgical Innovation (CSI) at Dartmouth-Hitchcock Medical Center (DHMC) and has contributed to over 400 surgical cases involving real-time imaging and feedback systems. Education: B.E. in Electrical Engineering, Tsinghua University (2007) Ph.D. in Biomedical Engineering, Dartmouth College (2012) Research Interests: His research emphasizes minimizing surgical errors via real-time brain deformation compensation, spine motion correction, and intraoperative imaging systems. Techniques include stereovision, 3D ultrasound, and machine learning for image registration and navigation. Key applications include open and minimally invasive neurosurgical procedures. Publications: His work spans stereovision systems for spinal surgery, brain shift compensation algorithms, and intraoperative ultrasound registration. Recent contributions address human feasibility and porcine model validation of surgical navigation tools. Grants & Labs: Collaborates with Medtronic on integrating updated imaging into navigation systems. Active in the CSI DHMC lab, focusing on clinical translation of real-time imaging solutions. Teaches ENGS 111: Digital Image Processing. Labs & Teams: Works within Dartmouth’s engineering and medical collaboration networks, advancing surgical precision through interdisciplinary research.

Ralf HaefnerView profile

Assistant Professor

• computational neuroscience
• perceptual decision-making
• causal inference
• +3 more

Ralf Haefner is an Assistant Professor in the Departments of Brain & Cognitive Sciences and Physics & Astronomy at the University of Rochester, holding this joint appointment since 2014. His interdisciplinary research bridges neuroscience and physics to investigate computational principles of perception and decision-making. Education and professional background: PhD, Oxford University, 1999 Visiting Research Fellow, Department of Neurobiology, Harvard Medical School Swartz Fellow, Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University Haefner's research program centers on computational neuroscience , with primary focus on how the brain forms perceptual beliefs and uses them for decisions through Bayesian modeling . He employs machine learning tools to construct mathematical models explaining neural responses and behavior, particularly in the visual domain. His work addresses neural representation of uncertainty, causal inference mechanisms, and probabilistic computation in cortical circuits. Analysis of recent publications (2023-2025) reveals three dominant trends: (1) causal inference frameworks applied to motion perception and segmentation, (2) Bayesian modeling of perceptual biases and confidence computations, and (3) integration of generative and discriminative neural computations. His work extends beyond traditional neuroscience into scientific methodology through 'Generative Adversarial Collaborations' for improving research discourse. Honors and Awards: Swartz Fellowship, Sloan-Swartz Center for Theoretical Neurobiology NSF CAREER Award (2022) for 'Approximate inference at the intersection of neuroscience and machine learning' Haefner secured significant research funding through his NSF CAREER award, which supports foundational work on probabilistic inference at the neuroscience-ML interface. While specific students aren't listed, his active publication record and lab infrastructure suggest ongoing mentorship of graduate students and postdocs. His research has clinical relevance as shown by studies on perceptual abnormalities in autism spectrum disorder, indicating translational potential for understanding neurological conditions.

Peter DoerschukView profile

Professor

• Biomedical Engineering
• Bioengineering
• Signal and Image Processing
• +15 more

Peter Doerschuk is a Professor in the Department of Electrical and Computer Engineering at Cornell University's College of Engineering. He joined Cornell in July 2006 after serving on the faculty at Purdue University in both Electrical and Computer Engineering and Biomedical Engineering. His educational background includes: B.S. in Electrical Engineering, MIT (1977) M.S. in Electrical Engineering, MIT (1979) Ph.D. in Electrical Engineering, MIT (1985) M.D., Harvard Medical School (1987) Peter Doerschuk's research focuses on biological and medical systems through the lens of computational nonlinear stochastic systems. His work spans biomedical imaging , signal and image processing , statistical modeling , and computational inverse problems in biophysics . He develops high-performance algorithms and software systems that integrate accurate physical models with computational efficiency. His research addresses problems across multiple spatial scales—from 3D virus reconstruction using electron microscopy to modeling whole-body ethanol pharmacokinetics. The recent publications highlight a strong trend in computational biomedical imaging and physiological modeling . Key areas include 3D reconstruction of heterogeneous biological structures, cryo-EM dynamics analysis, and physiologically based pharmacokinetic modeling. The work consistently combines advanced statistical and machine learning methods with domain-specific physical models, particularly in virology and neurovascular physiology. His scientific awards and honors include: Fellow, American Institute for Medical and Biological Engineering (AIMBE) University Faculty Scholar, Purdue University Motorola Excellence in Teaching Award Ernst A. Guillemin Thesis Prize (MIT) Department of Biomedical Engineering Faculty Service Award (Purdue) Dr. Doerschuk has advised graduate students, including Keyuan Xu, whose M.Eng. thesis at MIT received the prestigious Ernst A. Guillemin Thesis Prize. His research has been supported through academic grants and collaborations with institutions such as The Scripps Research Institute and Indiana University School of Medicine. He has developed parallel software systems for high-performance computing applications in biophysics and biomedical signal processing. His research has involved collaboration with multiple labs and teams, including work with Professor J. E. Johnson at The Scripps Research Institute on virus structure determination and with Professor S. J. O’Connor at Indiana University on ethanol pharmacokinetics modeling. These interdisciplinary teams integrate expertise in engineering, medicine, and computational science to solve complex biomedical problems.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +5 more

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

• Biomaterials
• Peripheral Nerve Injury
• Neuromuscular Modeling and Repair
• +2 more

Suradip Das is a Research Assistant Professor in the Department of Neurosurgery at the Perelman School of Medicine, University of Pennsylvania, where he serves as a Senior Research Investigator. His work bridges neural engineering and regenerative medicine to address critical challenges in nerve and muscle repair. His academic training includes: B.Tech in Biotechnology from Heritage Institute of Technology (2010) PhD in Biosciences and Bioengineering from Indian Institute of Technology Guwahati (2016) Dr. Das specializes in biomaterials development , peripheral nerve injury models , neuromuscular interface engineering , and stem cell-based regeneration . His research pioneers innervated tissue-engineered muscle constructs, demonstrating how motor neurons and endothelial cells synergistically enhance skeletal myocyte maturation. He innovates custom mechanobioreactors that apply tensile forces to guide nanofiber alignment for optimal myofiber formation, significantly advancing volumetric muscle loss treatments. Analysis of his 15 most recent publications reveals a dominant focus on neuromuscular regeneration (75% of articles), with emerging exploration of psychedelic compounds in neural repair. His work consistently integrates human iPSC-derived models , multi-cellular co-cultures , and large-animal validation to address translational gaps. Key trends include optogenetic control of motor units (2023), porcine nerve injury models (2020), and the critical role of pre-innervation in creating pro-regenerative microenvironments (2020-2022). As a core member of the Cullen Lab, Dr. Das collaborates on developing biofabricated neural microtissues for delayed nerve fusion and rapid functional recovery. His research directly informs clinical strategies for peripheral nerve repair and muscle regeneration through rigorous mechanistic studies and innovative engineering solutions.