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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"

📝 www.biorxiv.org/content/10.6...

Applying for an ERC grant in the 2027 competitions: what you need to know The ERC plans to launch the grant competitions under its 2027 Work Programme between July 2026 and June 2027, with the calls for proposals introducing several changes to the eligibility rules for appl...

The #ERC just announced new rules!
erc.europa.eu/news-events/...

If you got a B score before, you’re blocked for 2 years (instead of 1). A C score means 3-year exclusion.

This applies to the 2027 call for now, but I’d expect this to stick. Important for anyone planning to apply this round!

New preprint! 🧠
How do RNNs learn abstract rules from sequences, independent of specific stimuli?

By Vezha Boboeva, with Alberto Pezzotta & George Dimitriadis

"From sequences to schemas: low-rank recurrent dynamics underlie abstract relational representations"
www.biorxiv.org/content/10.6...

celebrate your long distance science best friends 🎈🥳🍾

👏🏼👏🏼👏🏼 👑

👏🏼👏🏼👏🏼

I think the goal of training at multiple levels is to circumvent that by establishing parallels and divergences that come from studying one mechanism across multiple measurements and bounding the scope of “saviorism”

Any tips for someone on that journey? Working on model 2 right now (and in that order) :)

Acetylcholine demixes heterogeneous dopamine signals for learning and moving Nature Neuroscience - Jang et al. measured dopamine and acetylcholine release in the striatum of rats performing a decision-making task and found that the relative timing of cholinergic and...

Thrilled to share our new paper, which shows that the relative timing of cholinergic and dopamine release dynamically gates whether dopamine acts as an RPE for in vivo plasticity and reinforcement learning. www.nature.com/articles/s41...

Cosyne 2026 - Cosyne Tutorial: Comparative Analysis of Neural Population Codes YouTube video by Cosyne Talks

Cosyne invited me to give a long tutorial (4 hours!) on methods to quantify differences high-d neural recordings across animals, brain regions, deep neural nets, etc.

The recording is up on youtube. I hope it inspires more research on this fundamental topic!

www.youtube.com/watch?v=n44x...

Multipanel figure illustrating the key components of the paper: 1) biophysical reservoir computing where a network of biophysically detailed excitatory and inhibitory neurons are randomly connected, receive a brief input, and produce a sustained spiking pattern in response, 2) an illustration of the task the biophysical reservoir computer is trained on: a simplified working memory task where the network must produce distinct fixed point attractors in response to different inputs.

Happy to share a new preprint from my PhD thesis! “A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks” 🧠💻

📝 www.biorxiv.org/content/10.6...

@jonescompneurolab.bsky.social

Such a deep and rich culture is Iran. So cultured and thoughtful their people- so heartbreaking 💔

Hopeless in Tehran Essays on neuroscience

Today I received a note from a grad student who lives in Tehran. Her note gives you firsthand experience of what it’s like to live in a city that is being bombed, and what it’s like to be young and feel despair about your future.

rezashadmehr.blogspot.com/2026/03/hope...

Science Advances’ special issue on women’s health highlights a growing wave of research focusing on women’s unique biological and psychological experiences. Articles cover a range of studies from the interplay between menopause and Alzheimer’s risk to the menstrual cycle’s influence on the brain-heart connection. It also describes the opportunities pregnancy provides to research aspects of both physical and mental health. By prioritizing women’s unique physiological and psychological experiences, research can expand the frontiers of knowledge in ways that benefit everyone.

In #ScienceAdvances last year, a special issue on #WomensHealth highlighted a growing wave of research focusing on women’s unique biological and psychological experiences.

Learn more on #InternationalWomensDay: https://scim.ag/4b5vlLq

AI is rapidly changing how we write code — but how should we use it in research?

To kick off our 2026 “How-To” we welcome Russ Poldrack (@russpoldrack.org; Stanford University):

🧠 Better Code, Better Science

📅 22 April 2026
🔗 Register: forms.gle/WRUdGQEpd5is...

#OHBM #OpenScience

Frontiers | Cross-population amplitude coupling in high-dimensional oscillatory neural time series Neural oscillations have long been considered important markers of interaction across brain regions, yet identifying coordinated oscillatory activity from hi...

The brain oscillates and we should figure out why. Development of new analytics will be key:
Cross-population amplitude coupling in high-dimensional oscillatory neural time series
www.frontiersin.org/journals/com...
#neuroscience

US applications for prestigious European research grants surge Rush for funds to relocate laboratories to Europe is latest sign of a US brain drain.

I've written two @nature.com stories this week looking at whether EU efforts to lure US scientists are working 🧪

They are: the number of US applicants wanting to take up ERC grants has doubled. www.nature.com/articles/d41...

As promised: a detailed figure-by-figure thread on our @pnas.org paper:

doi.org/10.1073/pnas...

We use signal correlations and noise correlations in chronic imaging data to show that representational drift is shaped by a balance between Hebbian and stochastic changes.

Let’s dive in 👇

🧠🧪 1/9

Building compositional tasks with shared neural subspaces Nature - The brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations.

Thrilled that my paper is out in the @nature.com. We explored how the brain builds complex tasks by compositionally combining simpler sub-task representations. The brain flexibly performs multiple tasks by dynamically reusing neural subspaces for sensory inputs and motor actions

rdcu.be/eRVUk

Iranians are experiencing a collective trauma. Thousands have been killed/injured in recent events, the economy is crippled & the threat of a wider conflict is real. This is especially difficult for those living in Iran, as many have lost (or fear losing) loved ones. www.nature.com/articles/d41...

Hey y’all! My institute started a new set of training programs for people interested in careers in the neurotech space.

Spread the word!

If you'd like to study Computational Neuroscience in Berlin, don't miss this info event on Wednesday! 👉 bit.ly/4aX4Yc8 #CompNeuro #BernsteinNetwork

very excited to share some of the final bits of work from my PhD! 🎉 we find that phasic DA in the accumbens simultaneously tracks persistent motivation towards reward cues in addition to new learning (but maybe not RPE?!) in sign-tracking rats. check out the preprint below!

She is in fact, wonderful :)

Different learning algorithms achieve shared optimal outcomes in humans, rats, and mice Animals must exploit environmental regularities to make adaptive decisions, yet the learning algorithms that enabels this flexibility remain unclear. A central question across neuroscience, cognitive science, and machine learning, is whether learning relies on generative or discriminative strategies. Generative learners build internal models the sensory world itself, capturing its statistical structure; discriminative learners map stimuli directly onto choices, ignoring input statistics. These strategies rely on fundamentally different internal representations and entail distinct computational trade-offs: generative learning supports flexible generalisation and transfer, whereas discriminative learning is efficient but task-specific. We compared humans, rats, and mice performing the same auditory categorisation task, where category boundaries and rewards were fixed but sensory statistics varied. All species adapted their behaviour near-optimally, consistent with a normative observer constrained by sensory and decision noise. Yet their underlying algorithms diverged: humans predominantly relied on generative representations, mice on discriminative boundary-tracking, and rats spanned both regimes. Crucially, end-point performance concealed these differences, only learning trajectories and trial-to-trial updates revealed the divergence. These results show that similar near-optimal behaviour can mask fundamentally different internal representations, establishing a comparative framework for uncovering the hidden strategies that support statistical learning. ### Competing Interest Statement The authors have declared no competing interest. Wellcome Trust, https://ror.org/029chgv08, 219880/Z/19/Z, 225438/Z/22/Z, 219627/Z/19/Z Gatsby Charitable Foundation, GAT3755 UK Research and Innovation, https://ror.org/001aqnf71, EP/Z000599/1

paper🚨
When we learn a category, do we learn the structure of the world, or just where to draw the line? In a cross-species study, we show that humans, rats & mice adapt optimally to changing sensory statistics, yet rely on fundamentally different learning algorithms.
www.biorxiv.org/content/10.1...

Good morning #SfN25! Looking for some posters to see this afternoon? Check out two by our lab: GG15 and HH1 🧠🧪👩🏻‍🔬

Chand lab is at SfN! (And I’m looking for postdocs on cells types and dynamics; reach out if you want to chat!)

The best (and only) uni merch I have ever purchased in my life 🦴

A sad day for Neuroscience in the Netherlands.