Katarzyna Raczy

@katarzynaraczy.bsky.social

Cognitive Neuroscientist, interested in neuroplasticity after permanent and transient congenital visual deprivation 🧠 Neural mechanisms of tactile reading 👆 and numerical cognition 🔢

Curious how YOUR brain adapts to body augmentations? 🧠 On 21 March we will be at the Donders Institute Open Day (De Brein Show) in Nijmegen. Our lab @bbt-lab.bsky.social will be part of the demos, where you can try our finger-extending exoskeleton 🦾 More info 👉 www.ru.nl/donders-inst...

Donders Institute Open Dag | 21 maart | Radboud Universiteit

Een dag vol interactieve belevenissen van spannende escape rooms en illusies tot live demonstraties, talks en experimenten – voor jong en oud.

ru.nl

(please repost) If you're looking for a #neuroscience PhD program - and interested in human brain plasticity and reorganization (neuroimaging in people born with blindness, deafness or without hands), my lab is accepting students this cycle. Email me!

Home Page - Interdisciplinary Program in Neuroscience

The Georgetown Difference We are invested in providing a transformative experience through holistic training, accessible resources, and personalized career strategies to help you reach your aspiration...

neuroscience.georgetown.edu

What a fantastic time at IMRF! 🎉 Huge thanks to the brilliant speakers for making our symposium on “Blindness as a Window into Brain Organisation” a big success! Big thanks to the organisers and the scientific community for this stellar event! Great to see so many friends and colleagues! #IMRF2025

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How does the brain integrate artificial body extensions? Using a custom-built finger-extending exoskeleton, we show that wearable augmentations are quickly integrated into body representation, with proprioceptive space adapting to the device’s structure and function. www.biorxiv.org/content/10.1...

Dynamics of sensorimotor plasticity during exoskeletal finger augmentation

How does the brain integrate artificial body extensions into its somatosensory representation? While prior work has shown that tool use alters body representation, little is known about how artificial...

biorxiv.org

I'm particularly happy to see this preprint out! Lenny compellingly demonstrates, in a data-driven way, a coding scheme unifying distributed dimensions and category selectivity. The idea:Higher visual cortex comprises many partially overlapping tuning maps that include but go beyond category tuning.

Lenny van Dyck@levandyck.bsky.social · last yr.

How is high-level visual cortex organized? In a new preprint with @martinhebart.bsky.social & @kathadobs.bsky.social, we show that category-selective areas encode a rich, multidimensional feature space 🌈 www.biorxiv.org/content/10.1... #neuroskyence 🧵 1/n

🚨🚨🚨PREPRINT ALERT🚨🚨🚨 Neural dynamics across cortical layers are key to brain computations - but non-invasively, we’ve been limited to rough "deep vs. superficial" distinctions. What if we told you that it is possible to achieve full (TRUE!) laminar (I, II, III, IV, V, VI) precision with MEG!

Overview of the simulation strategy and analysis. a) Pial and white matter boundaries
surfaces are extracted from anatomical MRI volumes. b) Intermediate equidistant surfaces are
generated between the pial and white matter surfaces (labeled as superficial (S) and deep (D)
respectively). c) Surfaces are downsampled together, maintaining vertex correspondence across
layers. Dipole orientations are constrained using vectors linking corresponding vertices (link vectors).
d) The thickness of cortical laminae varies across the cortical depth (70–72), which is evenly sampled
by the equidistant source surface layers. e) Each colored line represents the model evidence (relative
to the worst model, ΔF) over source layer models, for a signal simulated at a particular layer (the
simulated layer is indicated by the line color). The source layer model with the maximal ΔF is
indicated by “˄”. f) Result matrix summarizing ΔF across simulated source locations, with peak
relative model evidence marked with “˄”. g) Error is calculated from the result matrix as the absolute
distance in mm or layers from the simulated source (*) to the peak ΔF (˄). h) Bias is calculated as the
relative position of a peak ΔF(˄) to a simulated source (*) in layers or mm.

Very much looking forward to connecting with friends and colleagues and to sharing some great ideas! 🤩🧠

International Multisensory Research Forum@imrf.bsky.social · last yr.

Hi pals, our preliminary conference schedule + symposium schedule are online imrf2025.sciencesconf.org/resource/pag... Also, this year we are planning some exciting pre-conf methods-focused workshops! More details and info how to sign up will be added soon imrf2025.sciencesconf.org/resource/pag...