A5079206365- Neural dynamics and brain function
- Visual perception and processing mechanisms
- Visual Attention and Saliency Detection
- Cell Image Analysis Techniques
- Face Recognition and Perception
- Neuroscience and Neuropharmacology Research
- Neurobiology and Insect Physiology Research
- Olfactory and Sensory Function Studies
- CCD and CMOS Imaging Sensors
- EEG and Brain-Computer Interfaces
- Retinal Development and Disorders
- Photoreceptor and optogenetics research
- Neuroinflammation and Neurodegeneration Mechanisms
- Receptor Mechanisms and Signaling
- Neuroscience and Neural Engineering
- Image Processing Techniques and Applications
- Functional Brain Connectivity Studies
- Advanced Fluorescence Microscopy Techniques
University of Tübingen
2018-2024
University of Göttingen
2021-2024
Bernstein Center for Computational Neuroscience Tübingen
2022-2024
Max Planck Institute for Intelligent Systems
2020-2024
Stanford University
2024
Hertie Institute for Clinical Brain Research
2018-2022
Senckenberg Centre for Human Evolution and Palaeoenvironment
2018-2021
Max Planck Institute for Plasma Physics
2017
Max Planck Society
2017
Despite strong evidence to the contrary in literature, microsaccades are overwhelmingly described as involuntary eye movements. Here we show both human subjects and monkeys that individual of any direction can easily be triggered: (1) on demand, based an arbitrary instruction, (2) without special training, (3) visual guidance by a stimulus, (4) spatially temporally accurate manner. Subjects voluntarily generated instructed "memory-guided" readily, similarly how they made normal...
Abstract Deciphering the brain’s structure-function relationship is key to understanding neuronal mechanisms underlying perception and cognition. The cortical column, a vertical organization of neurons with similar functions, classic example primate neocortex organization. While columns have been identified in primary sensory areas using parametric stimuli, their prevalence across higher-level cortex debated. A hurdle identifying difficulty characterizing complex nonlinear tuning, especially...
Responses to natural stimuli in area V4—a mid-level of the visual ventral stream—are well predicted by features from convolutional neural networks (CNNs) trained on image classification. This result has been taken as evidence for functional role V4 object However, we currently do not know if and what extent plays a solving other computational objectives. Here, investigated normative accounts (and V1 comparison) predicting macaque single-neuron responses images representations extracted 23...
Abstract Minimally invasive, high-bandwidth brain-computer-interface (BCI) devices can revolutionize human applications. With orders-of-magnitude improvements in volumetric efficiency over other BCI technologies, we developed a 50-μm-thick, mechanically flexible micro-electrocorticography (μECoG) BCI, integrating 256×256 electrodes, signal processing, data telemetry, and wireless powering on single complementary metal-oxide-semiconductor (CMOS) substrate containing 65,536 recording 16,384...
Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent - where center and surround share same orientation are more inhibitory than when orientations orthogonal, potentially serving redundancy reduction predictive coding. Understanding these center-surround interactions in relation to natural image statistics...
Abstract Responses to natural stimuli in area V4 – a mid-level of the visual ventral stream are well predicted by features from convolutional neural networks (CNNs) trained on image classification. This result has been taken as evidence for functional role object However, we currently do not know if and what extent plays solving other computational objectives. Here, investigated normative accounts (and V1 comparison) predicting macaque single-neuron responses images representations extracted...
A bstract Deep neural networks (DNN) have set new standards at predicting responses of populations to visual input. Most such DNNs consist a convolutional network (core) shared across all neurons which learns representation computation in cortex and neuron-specific readout that linearly combines the relevant features this representation. The goal paper is test whether indeed generally characteristic for cortex, i.e. gener-alizes between animals species, what factors contribute obtaining...
The foveal visual image region provides the human system with highest acuity. However, it is unclear whether such a high fidelity representational advantage maintained when locations are committed to short-term memory. Here, we describe paradoxically large distortion in target location recall by humans. We briefly presented small, but contrast, points of light at eccentricities ranging from 0.1 12°, while subjects their line sight on stable target. After brief memory period, indicated...
In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal activity have become an established method to study tuning properties biological and artificial visual systems. However, as we move up the hierarchy, complexity computations increases. Consequently, it becomes more challenging model activity, requiring complex models. this study, introduce a new attention readout for convolutional data-driven core neurons in macaque V4 that outperforms state-of-the-art...
The neural underpinning of the biological visual system is challenging to study experimentally, in particular as neuronal activity becomes increasingly nonlinear with respect input. Artificial networks (ANNs) can serve a variety goals for improving our understanding this complex system, not only serving predictive digital twins sensory cortex novel hypothesis generation silico, but also incorporating bio-inspired architectural motifs progressively bridge gap between and machine vision. mouse...
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists create predictive models bridge machine vision. During Sensorium 2022 competition, we introduced benchmarks for vision with static However, animals operate excel in dynamic environments, making it...
Deep neural networks set the state-of-the-art across many tasks in computer vision, but their generalization ability to image distortions is surprisingly fragile. In contrast, mammalian visual system robust a wide range of perturbations. Recent work suggests that this can be explained by useful inductive biases encoded representations stimuli throughout cortex. Here, we successfully leveraged these with multi-task learning approach: jointly trained deep network perform classification and...
Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception cognition. In retina, can be identified by carefully selected stimuli, but this requires expert domain knowledge biases procedure towards previously known types. visual cortex, it still unknown what exist how to identify them. Thus, unbiased identification of in retina new approaches are needed. Here we propose an optimization-based clustering approach using...
A key feature of neurons in the primary visual cortex (V1) primates is their orientation selectivity. Recent studies using deep neural network models showed that most exciting input (MEI) for mouse V1 exhibit complex spatial structures predict non-uniform selectivity across receptive field (RF), contrast to classical Gabor filter model. Using local patches drifting gratings, we identified heterogeneous tuning varied up 90° sub-regions RF. This heterogeneity correlated with deviations from...
Sensory processing changes with behavioral context to increase computational flexibility. In the visual system, active states enhance sensory responses but typically leave preferred stimuli of neurons unchanged. Here we find that state does modulate stimulus selectivity in mouse cortex colored natural scenes. Using population imaging, behavior, pharmacology, and deep neural networks, identified a shift color towards ultraviolet exclusively caused by pupil dilation, resulting dynamic switch...