With talks by @kaytrue.bsky.social, Carl-Philipp Heisenberg, @neuronarain.bsky.social, Matz Liebel, Giorgio Scita, Sebastian Deindl, Louise Jawerth, Ewa Paluch

A hackathon reveals that disagreement in neuroscience runs deeper than most researchers suspect—even in electrophysiology, a field that prides itself on hard data, write @mattiachini.bsky.social and Gaelle Chapuis. #neuroskyence www.thetransmitter.org/reproducibil...

18 teams analyzed a neuro dataset and got different answers

Disagreement in neuroscience runs deeper than most researchers suspect—even in electrophysiology, a field that prides itself on hard data.

thetransmitter.org

New paper with @paulm-k.bsky.social and Mark Stokes on how we switch internal attention between working memory contents. Beta-band oscillations tracked the magnitude and success of these switches. www.jneurosci.org/content/46/1... #neuroskyence

Human Beta Oscillations Reflect Magnitude and Fidelity of Priority Shifts in Working Memory

Flexible prioritization in working memory (WM) is supported by neural oscillations in frontal and sensory brain areas, but the roles of different oscillations remain poorly understood. Recordings in h...

jneurosci.org

@fluketc.bsky.social and @dudman.bsky.social have hit it out the park again. In my (perhaps biased) opinion this - along with their previous papers - is field-defining work. www.science.org/doi/10.1126/...

Reward magnitude determines reinforcement learning efficiency

Standard animal learning studies minimize individual reward magnitudes to maximize the repetitions of reinforced behaviors. We investigated how reward magnitude influences initial learning across five...

science.org

And a nice summary by Richard Antonello can be found here: elifesciences.org/articles/111...

Language Models: Does the brain really know what word is coming next?

Apparent neural encoding of future words may arise from the statistical structure of language itself, rather than from predictive computations in the brain.

elifesciences.org

ines-schoenmann.bsky.social@ines-schoenmann.bsky.social · 3mo ago

New peer-reviewed paper w/ @mheilbron.bsky.social, @predictivebrain.bsky.social & Jakub Szewczyk! Pre-onset brain encoding has been taken as evidence that brains–like LLMs–predict upcoming words. We show that the same signatures arise in systems that cannot predict. (elifesciences.org) (1/8)

After rereading the cool hidden brain paper (www.frontiersin.org/journals/sys...) I decided to make a new version. Code and everything available here: github.com/koerding/neu... Most popular: hippocampus. Why are diagonal band, medial geniculate nucleus, and interposed nucleus so unpopular?

What are the real promises and looming perils of neural foundation models? 🧠 I put my thoughts on (virtual) paper for @thetransmitter.bsky.social following a very energised workshop at @cosynemeeting.bsky.social 2025. It's also my first piece for them 😊

The Transmitter @thetransmitter.bsky.social · 4mo ago

These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity, writes @juangallego.bsky.social. But will we be able to understand them? #neuroskyence www.thetransmitter.org/neuroai/why-...

Our work on how neural circuits in the cerebellum encode prior probabilities led by Julius Koppen is out now in Nature Neuroscience www.nature.com/articles/s41... Big thanks to Julius Koppen & the whole team! And dedicated to all of us who found inspiration in Bayesian theories of the brain!

Neural circuits encode prior knowledge of temporal statistics - Nature Neuroscience

This study shows that cerebellar circuits learn and encode prior probabilities of event timing. Cell-type-specific neural activity reflects environmental statistics and guides predictive motor behavio...

nature.com

These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity, writes @juangallego.bsky.social. But will we be able to understand them? #neuroskyence www.thetransmitter.org/neuroai/why-...

Why neural foundation models work, and what they might—and might not—teach us about the brain

These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?

thetransmitter.org

New paper hot off the (pre-)press! We dig into the evolutionary origins of neural computations for behavioral control across mice, monkeys, and humans: www.biorxiv.org/content/10.6.... As our lab's first foray into comparative analysis of neural dynamics, I’m super excited about this work! 1/18

Excited and thrilled and humbled that our work is now out at Nature Human Behaviour linking Memorability with Time Perception! I hope you all find it of interest 🙂 Memorability shapes perceived time (and vice versa) #academicsky #neuroskyence #psychscisky #science

Memorability shapes perceived time (and vice versa) - Nature Human Behaviour

In this Article, Ma et al. show, across a series of experiments, that time and memorability (the probability of recalling a visual stimulus) mutually influence one another, suggesting that time is a f...

nature.com

Neural circuits encode prior knowledge of temporal statistics www.nature.com/articles/s41...

Neural circuits encode prior knowledge of temporal statistics - Nature Neuroscience

This study shows that cerebellar circuits learn and encode prior probabilities of event timing. Cell-type-specific neural activity reflects environmental statistics and guides predictive motor behavio...

nature.com