Open call for a permanent MEG Lab Manager position for our new MEGIN scanner at CIMeC Trento.
You have extensive MEG experience, a degree in physics, engineering, neuroscience:
Apply here lavoraconnoi.unitn.it/bando-pta/co...
Deadline 29th April!
p.s. call in Italian but foreigners welcome!! 🌎
New Annual Review with @nathanieldaw.bsky.social: “Planning in the Brain: It's Not What You Think It Is.” We argue that the brain's 'planning' machinery is mostly used for learning from simulated experience, and that thinking prospectively at decision time is just one special case of this process.
We're happy to release NeuralSet: a simple, fast, scalable package for Neuro-AI
Supports:
🧠fMRI, EEG, MEG, iEEG, spikes… preprocessing
💬 text 🔊 audio ▶️ video 🏞️ image… embeddings
📦 pip install neuralset
🔍 facebookresearch.github.io/neuroai/neur...
đź“„ kingjr.github.io/files/neural...
🧵 Details👇
New work w/ Zach Kelso and @madeleinecsnyder.bsky.social
www.biorxiv.org/content/10.6...
Our negative results on classical conditioning in planarian flatworms. This was surprising, given the long history of work (including sensational findings of memory transfer and retention through decapitation).
Fun! Wonder how symbolic Claude’s data viz tool is (which might prove the point)
🤩
Biophysical modeling to develop and test mechanistic hypotheses underlying pharmacological EEG biomarkers. Top: Modeling EEG biomarkers begins with picking a specific brain signal that is reliably different between patient populations. An example of a hypothetical EEG biomarker is an auditory event related potential (ERP) that is suppressed in post-treatment (red) relative to pre-treatment (blue). Middle: Biophysical modeling allows for testing mechanistic hypotheses that explain how EEG biomarkers emerge and change with drugs. Hypotheses about which drug mechanisms lead to distinct brain activity patterns must be constructed, and corresponding model parameters identified. Bottom: The default HNN model is used as a starting point to test hypotheses by either manually altering the values of the chosen model parameters, or using automated optimization and inference algorithms. Differences in parameter values pre-to post-treatment correspond to model-based predictions.
Happy to share a new preprint from the @hnnsolver.bsky.social team!🧠💻🎉
"Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver"
đź“ť www.biorxiv.org/content/10.6...
My latest post to Neural Flows!
“Reorienting neuroscience around brain flows”
A perspective shift to enhance both activity-focused (e.g., manifold, representations) and connectivity-focused (e.g., connectome) neuroscience
open.substack.com/pub/neuralfl...
✨Excited to share that our new preprint, "Mapping developmental patterns of intrinsic timescale", is now available on bioRxiv!!
đź“š doi.org/10.64898/202...
I see that, hard for three reviewers to be confused about the same thing without it being a little bit your fault.
Huge upside to clarify / get clarity before writing a 40 page R&R.
Reviewer load is fair! But surely there are ways to add reasonable limit (eg time or message limits). Should also take into account that there may be less to read later. Maybe could even be done on OpenReview platform.
Ya, I think eLife handles this better than just about everyone. It’d be the easiest for them to implement, but agree the payoff would be bigger for other journals.
Though there is still a downside for, say, unfair reviews that label a paper as bad, when they could have been clarified (rare)
Having a hard time seeing a downside.
I’m sure it could even be done without intensive editor moderation.
TBH I’m mostly thinking about paper reviews, but think it would work better for grants too. A 30 min live presentation is much more stressful for many folks, and introduces too many biases.
For paper reviews, a few days of discussion is dwarfed by months of unnecessary revisions.
How does the brain decide which mental strategy to use when inferring others' beliefs?
Excited to (finally!) see my first first-author paper out @natneuro.nature.com
Summary below 🧵 #CogSci #CogNeuro
www.nature.com/articles/s41...
IMO even better would be to have eLife-reviewer-style instant messaging.
Easily anonymous, less biased, and allows for more thoughtful discussion. Allows the authors to clarify misunderstandings before doing the work under the threat of rejection. @behrenstimb.bsky.social
Network reconfiguration preserves prediction error signallingin the aging brain www.biorxiv.org/content/10.64898/2026.04...
How do we represent maps of social relationships in the mind & brain? To find out, we tracked 1st-year university students’ friendships, as well as students’ *beliefs* about who was friends with whom in their network.
Yang breaks down what we found in the quoted thread 👇🏻
Broader context below:
White matter pathways mediating dorsolateral prefrontal TMS therapy for depression
New @natneuro.nature.com paper led by Caio Seguin, Robin Cash, and Andrew Zalesky.
We map (indirect) pathways from DLPFC to SGC and link individual variation with response efficacy.
www.nature.com/articles/s41...
Can’t recommend scientific color maps strongly enough.
www.fabiocrameri.ch/colourmaps/
Agree that blue->white/black->red is a good default, but nothings set in stone.
Looks very cool!
Apparently Bambi is the brms substitute for python (for pyMC; blackjax backend looks a little slow).
Doesn’t look as comprehensive (eg didn’t see nlf replacement)
bambinos.github.io/bambi/notebo...
đź‘€ looks like the core Stan folks are pretty bullish on Jax as the software of choice for fitting Bayesian models
bob-carpenter.github.io/dj-paper/
New preprint from my lab! We study how reinforcement learning & selective attention interact. To do so, we built a set of models describing different ways that value & reward prediction error can modulate top-down attention. We compare model outcomes to monkey data from a color value learning task
TIL about SSD thanks :)
We got a similar artifact at Princeton, 650m from the train. Thought about mounting a camera at the station and regressing out the optic flow.
Can we really measure replay in humans using MEG with current methods? In our most recent paper we simulated replay under realistic conditions via a novel hybrid approach with astonishing results.
we're delighted that it has now been published @elife.bsky.social!
elifesciences.org/articles/108...
It's been a long time coming, but I'm very happy this work has finally come to fruition!
The Cognitive Tools Lab at Stanford (cogtoolslab.github.io) is recruiting two new research staff members to join in AY 26-27.
Full-Time Lab Manager: forms.gle/UVwfx5wbY9Km....
IRiSS Predoc Researcher: iriss.stanford.edu/predoc/2026-....
Please share widely in your networks, thank you!!
