A5089543782- Neural dynamics and brain function
- Visual perception and processing mechanisms
- Face Recognition and Perception
- Visual Attention and Saliency Detection
- Functional Brain Connectivity Studies
- Neural and Behavioral Psychology Studies
- EEG and Brain-Computer Interfaces
- Multisensory perception and integration
- Tactile and Sensory Interactions
- Neural Networks and Applications
- Memory and Neural Mechanisms
- Cell Image Analysis Techniques
- Domain Adaptation and Few-Shot Learning
- Olfactory and Sensory Function Studies
- CCD and CMOS Imaging Sensors
- Transcranial Magnetic Stimulation Studies
- Action Observation and Synchronization
- Advanced Neural Network Applications
- Cognitive Science and Education Research
- Advanced Vision and Imaging
- Gaze Tracking and Assistive Technology
- Child and Animal Learning Development
- Advanced MRI Techniques and Applications
- Multimodal Machine Learning Applications
- Explainable Artificial Intelligence (XAI)
Freie Universität Berlin
2016-2025
Humboldt-Universität zu Berlin
2014-2025
Bernstein Center for Computational Neuroscience Berlin
2014-2025
Einstein Center for Neurosciences Berlin
2019-2024
Charité - Universitätsmedizin Berlin
2010-2024
Smith-Kettlewell Eye Research Institute
2024
Massachusetts Institute of Technology
2014-2023
National Institute of Mental Health
2023
Institute for Research in Fundamental Sciences
2023
University of Tehran
2023
Abstract The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient object recognition in humans. However, stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) brain representations with an artificial deep network (DNN) tuned to statistics real-world recognition. We showed that DNN captured stages human processing both time space from early areas towards dorsal...
The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing commonly viewed studied as a feedforward process. Here, we measure model rapid representational dynamics across multiple stages ventral stream using time-resolved imaging deep learning. We observe substantial transformations during first 300 ms within ventral-stream regions. Categorical divisions emerge in...
Visual imagery allows us to vividly imagine scenes in the absence of visual stimulation. The likeness perception suggests that they might share neural mechanisms brain. Here, we directly investigated whether and cortical representations. Specifically, used a combination functional magnetic resonance imaging (fMRI) multivariate pattern classification assess encode "category" objects their "location" similar fashion. Our results indicate fMRI response patterns for different categories imagined...
Human scene recognition is a rapid multistep process evolving over time from single image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data unravel the course of this cortical process. Following an early signal for lower-level visual analysis scenes at ~100 ms, we found marker real-world size, i.e. processing, ~250 ms indexing neural representations robust changes in unrelated properties and viewing conditions. For quantitative model how...
Despite the importance of an observer’s goals in determining how a visual object is categorized, surprisingly little known about humans process task context which objects occur and it may interact with processing objects. Using magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) multivariate techniques, we studied spatial temporal dynamics processing. Our results reveal sequence separate but overlapping task-related processes spread across frontoparietal...
Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern the brain. Current brain imaging techniques isolation cannot resolve brain's dynamics, because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed an integration approach that uses representational similarities to combine measurements of magnetoencephalography (MEG) and functional magnetic resonance (fMRI)...
Summary Different theories explain how subjective experience arises from brain activity 1,2 . These have independently accrued evidence, yet, confirmation bias and dependence on design choices hamper progress in the field 3 Here, we present an open science adversarial collaboration which directly juxtaposes Integrated Information Theory (IIT) 4,5 Global Neuronal Workspace (GNWT) 6–10 , employing a theory-neutral consortium approach 11,12 We investigate neural correlates of content duration...
To behave adaptively with sufficient flexibility, biological organisms must cognize beyond immediate reaction to a physically present stimulus. For this, humans use visual mental imagery [1Dijkstra N. Bosch S.E. van Gerven M.A.J. Shared neural mechanisms of perception and imagery.Trends Cogn. Sci. 2019; 23: 423-434Abstract Full Text PDF PubMed Scopus (41) Google Scholar, 2Pearson J. The human imagination: the cognitive neuroscience imagery.Nat. Rev. Neurosci. 20: 624-634Crossref (69)...
Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the from feedback direction. Here, we used ultra-rapid serial presentation suppress sustained activity that blurs boundaries steps, enabling us resolve two distinct stages with MEG multivariate pattern classification. The first stage was rapid activation cascade bottom-up sweep, which terminated early as stimuli were presented at...
The degree to which we perceive real-world objects as similar or dissimilar structures our perception and guides categorization behavior. Here, investigated the neural representations enabling perceived similarity using behavioral judgments, fMRI MEG. As different object dimensions co-occur partly correlate, understand relationship between brain activity it is necessary assess unique role of multiple dimensions. We thus behaviorally assessed in relation shape, function, color background....
Deep neural networks (DNNs) are promising models of the cortical computations supporting human object recognition. However, despite their ability to explain a significant portion variance in data, agreement between and brain representational dynamics is far from perfect. We address this issue by asking which features currently unaccounted for time series estimated multiple areas ventral stream via source-reconstructed magnetoencephalography data acquired participants (nine females, six...
Different theories explain how subjective experience arises from brain activity1,2. These have independently accrued evidence, but not been directly compared3. Here we present an open science adversarial collaboration juxtaposing integrated information theory (IIT)4,5 and global neuronal workspace (GNWT)6-10 via a theory-neutral consortium11-13. The proponents the consortium developed preregistered experimental design, divergent predictions, expected outcomes interpretation thereof12. Human...
Animacy and real-world size are properties that describe any object thus bring basic order into our perception of the visual world. Here, we investigated how human brain processes animacy. For this, applied representational similarity to fMRI MEG data yield a view activity with high spatial temporal resolutions, respectively. Analysis revealed distributed partly overlapping set cortical regions extending from occipital ventral medial cortex represented animacy size. Within this set,...