🚨 🆕 Preprint 🚨

How does the brain represent natural images?

Using MEG + multivariate analysis, we disentangle contributions of retinotopy, spatial frequency, shape, and texture

Together, our results reveal how visual features jointly and dynamically support human object recognition.

link 👇

🧠✨ Exciting new research alert! ✨🧠

Did you know that catecholamines can reduce choice history biases in perceptual decision-making? 🧐🔍

Paper: journals.plos.org/plosbiology/...

With @donnerlab.bsky.social and @swammerdamuva.bsky.social

Contents of visual predictions oscillate at alpha frequencies Predictions of future events have a major impact on how we process sensory signals. However, it remains unclear how the brain keeps predictions online in anticipation of future inputs. Here, we combin...

@dotproduct.bsky.social's first first author paper is finally out in @sfnjournals.bsky.social! Her findings show that content-specific predictions fluctuate with alpha frequencies, suggesting a more specific role for alpha oscillations than we may have thought. With @jhaarsma.bsky.social. 🧠🟦 🧠🤖

Long time in the making: our preprint of survey study on the diversity with how people seem to experience #mentalimagery. Suggests #aphantasia should be redefined as absence of depictive thought, not merely "not seeing". Some more take home msg:
#psychskysci #neuroscience

doi.org/10.1101/2025...

Really enjoyed my weekend read on 𝐚𝐜𝐭𝐢𝐯𝐞 𝐟𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠: local recurrence amplifies natural input patterns and suppresses stray activity. This review beautifully argues that sensory cortex itself is a site of memory and prediction. Food for thought on hallucinations!

#neuroskyence #neuroscience

✨ Meet our speakers! ✨

Among our speakers this year at #SNS2025 we have Marlene Cohen (@marlenecohen.bsky.social), from University of Chicago

Read the abstract here 💬 👇
meg.medizin.uni-tuebingen.de/sns_2025/abs...

#neuroskyence

✨ Meet our speakers! ✨

Among our speakers this year at #SNS2025 we have Floris de Lange (@predictivebrain.bsky.social)

Read the abstract here 💬 👇
meg.medizin.uni-tuebingen.de/sns_2025/abs...

#neuroskyence

✨ Meet our speakers! ✨

Among our speakers this year at #SNS2025 we have Tim Kietzmann (@timkietzmann.bsky.social)

Read the abstract here 💬 👇
meg.medizin.uni-tuebingen.de/sns_2025/abs...

#neuroskyence #compneurosky #NeuroAI

An array of 9 purple discs on a blue background. Figure from Hinnerk Schulz-Hildebrandt.

A nice shift in perceived colour between central and peripheral vision. The fixated disc looks purple while the others look blue.

The effect presumably comes from the absence of S-cones in the fovea.

From Hinnerk Schulz-Hildebrandt:
arxiv.org/pdf/2509.115...

✨ Meet our speakers! ✨

Among our speakers this year at #SNS2025 we also have Sylvia Schröder (@sylviaschroeder.bsky.social), from University of Sussex

Read the abstract here 💬 👇
meg.medizin.uni-tuebingen.de/sns_2025/abs...

#neuroskyscience

a woman in front of a white board with the words take your time written on it ALT: a woman in front of a white board with the words take your time written on it

📢 Deadline extended! 📢

The registration deadline for #SNS2025 has been extended to Sunday, September 28th!

Register here 👉 meg.medizin.uni-tuebingen.de/sns_2025/reg...

PS: Students of the GTC (Graduate Training Center for Neuroscience) in Tübingen can earn 1 CP for presenting a poster! 👀

✨ Meet our speakers! ✨

Among our speakers this year at #SNS2025 we have Simone Ebert (@simoneebert.bsky.social) & Jan Lause (@janlause.bsky.social), from Hertie AI Institute, University of Tübingen

Read the abstract here 💬 👇
meg.medizin.uni-tuebingen.de/sns_2025/abs...

#neuroskyscience

✨ Meet our speakers! ✨

Next speaker to present is Arthur Lefevre, from University of Lyon

Read the abstract here 💬 👇
meg.medizin.uni-tuebingen.de/sns_2025/abs...

#neuroscience #neuroskyscience

✨ Meet our speakers! ✨

Next speaker to present is Mara Wolter, PhD student at University of Tübingen in Yulia Oganian lab

Read the abstract here 💬 👇
meg.medizin.uni-tuebingen.de/sns_2025/abs...

🔵 Tübingen SNS2025 🔵

Over the next few days, we’ll be introducing you to the brilliant scientists who will be deliver their talks at #SNS2025!

✨ Get ready to meet our speakers! ✨

We are starting with Liina Pylkkänen, from New York University

💬 meg.medizin.uni-tuebingen.de/sns_2025/abs...

Great work from a great team. Congrats guys!🎉

A brain-wide map of neural activity during complex behaviour - Nature The International Brain Laboratory presents a brain-wide electrophysiological map obtained from pooling data from 12 laboratories that performed the same standardized perceptual decision-making task i...

The two key studies of the International Brain Laboratory @intlbrainlab.bsky.social are out today!

A brain-wide map of neural activity during complex behaviour
www.nature.com/articles/s41...

Brain-wide representations of prior information in mouse decision-making
www.nature.com/articles/s41...

From bench to bot: Why AI-powered writing may not deliver on its promise Efficiency isn’t everything. The cognitive work of struggling with prose may be a crucial part of what drives scientific progress.

“Competent prose generated by a machine, I’ve come to realize, might not be what science actually needs.” ?

By Tim Requarth

#neuroskyence

www.thetransmitter.org/from-bench-t...

Noradrenaline drives learning across scales of time and neurobiological organisation The noradrenergic system plays a diverse role in learning, from optimising learning behaviour to modulating plasticity. Work bridging across micro- and macroscale levels is revealing how noradrenaline...

Excellent new review by @claireocallaghan.bsky.social on how noradrenaline drives learning across multiple scales of neurobiological organization - from cells to networks www.cell.com/trends/cogni...

Not long to go now! For those of you who enjoy a more intimate conference with a chance to get to know your favourite speakers I would highly recommend this right here. Reach out if you have any questions :)

Can't wait to see this fantastic line-up 🤩

Can humans use artificial limbs for body augmentation as flexibly as their own hands?
🚨 Our new interdisciplinary study put this question to the test with the Third Thumb (@daniclode.bsky.social), a robotic extra digit you control with your toes!
www.biorxiv.org/content/10.1...
🧵1/10

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.

🚨🚨🚨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!

It's gotta be a Zelda playlist for me - those games trained me to problem-solve to that music 😆

Two examples of how contextual information can bias visual perception. Top: Luminance illusion created by shadows (source: https://persci.mit.edu/gallery/checkershadow). Square B looks brighter than square A but has the same luminance, i.e., they have identical grayscale values in the picture. Bottom: Perception of object motion is biased by self-motion. The combination of leftward self-motion and up-left object motion in the world produces retinal motion that is up-right. If the animal partially subtracts the optic flow vector (orange dashed arrow) generated by self-motion (yellow arrow) from the image motion on the retina (black arrow), they may have a biased perception of object motion (red arrow) that lies between retinal and world coordinates (green arrow).

Rewarding animals to accurately report their subjective #percept is challenging. This study formalizes this problem and overcomes it with a #Bayesian method for estimating an animal’s subjective percept in real time during the experiment @plosbiology.org 🧪 plos.io/3HaxiuB

🚨 New WP! 📄 "Publish or Procreate: The Effect of Motherhood on Research Performance" (w/ @valentinatartari.bsky.social
👩‍🔬👨‍🔬 We investigate how parenthood affects scientific productivity and impact — and find that the impact is far from equal for mothers and fathers.

Press release on our new paper from @hih-tuebingen.bsky.social 🧠🥳
Link: www.nature.com/articles/s42...
Thread: bsky.app/profile/migh...
#neuroskyence #compneurosky #magnetoencephalography

The members of the Cluster of Excellence "Machine Learning: New Perspectives for Science" raise their glasses and celebrate securing another funding period.

We're super happy: Our Cluster of Excellence will continue to receive funding from the German Research Foundation @dfg.de ! Here’s to 7 more years of exciting research at the intersection of #machinelearning and science! Find out more: uni-tuebingen.de/en/research/... #ExcellenceStrategy

Communication of perceptual predictions from the hippocampus to the deep layers of the parahippocampal cortex High-resolution neuroimaging reveals stimulus-specific predictions sent from hippocampus to the neocortex during perception.

Our study using layer fMRI to study the direction of communication between the hippocampus and cortex during perceptual predictions is finally out in Science Advances! Predicted-but-omitted shapes are represented in CA2/3 and correlate specifically with deep layers of PHC, suggesting feedback. 🧠🟦