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Kornemann, L.

Publications and source records attributed to Kornemann, L..

2 recordsLinked to original sources

Human escape follows a structured movement pattern shaped by threat and context

Evading danger is critical to survival. In non-human animals, escape strategies are shaped by neural, biomechanical, and ecological constraints, resulting in species-specific patterns. In humans, ethical and practical constraints have until recently hindered investigation of escape movements, such that its organising principles are commonly extrapolated from other, mostly quadruped, species. Here, we use freely moving virtual reality (fm-VR) in a large physical space to present biologically relevant threats. We discover that human escape behaviour is organized within a constrained action space shaped by threat and context, revealing a small set of stereotyped kinematic patterns that are not predicted from those reported in other mammals. Pattern selection is shaped by environment and individual preference, and is not predicted by behaviour in safe conditions. The dominant kinematic pattern includes head orientation toward the threat, body rotation beyond this angle until facing away, and escape initiation with ipsilateral foot. Alternative variants include turning away from the threat, backward movement, and misdirected flight. Certain alternative escape patterns and kinematic features reduce success, whereas specific preparatory adjustments enhance it. Our results provide a foundation for probing the neural mechanisms of human escape, and enable investigation of potential disruptions in clinical conditions.

animal behavior and cognition↗

Unlocking the Secrets of the Primate Visual Cortex: A CNN-Based Approach Traces the Origins of Major Organizational Principles to Retinal Sampling

Primate visual cortex exhibits key organizational principles: Cortical magnification, eccentricity-dependent receptive field size and spatial frequency tuning as well as radial bias. We provide compelling evidence that these principles arise from the interplay of the non-uniform distribution of retinal ganglion cells (RGCs), and a quasi-uniform convergence rate from the retina to the cortex. We show that convolutional neural networks (CNNs) outfitted with a retinal sampling layer, which resamples images according to retinal ganglion cell density, develop these organizational principles. Surprisingly, our results indicate that radial bias is spatial-frequency dependent and only manifests for high spatial frequencies. For low spatial frequencies, the bias shifts towards orthogonal orientations. These findings introduce a novel hypothesis about the origin of radial bias. Quasi-uniform convergence limits the range of spatial frequencies (in retinal space) that can be resolved, while retinal sampling determines the spatial frequency content throughout the retina.

neuroscience↗