Perceptual multistability: a multifaceted window into brain dysfunctions Perceptual multistability, observed across species and sensory modalities, offers valuable insights into numerous cognitive functions and dysfunctions. For instance, differences in temporal dynamics and information integration during percept formation often distinguish clinical from nonclinical populations. Computational psychiatry can elucidate these variations through two primary approaches: (i) Bayesian modeling, which treats perception as an unconscious inference, and (ii) an active, information-seeking perspective (e.g., reinforcement learning), which frames perceptual switches as internal actions. Our synthesis aims to leverage multistability to bridge these computational psychiatry subfields, linking human and animal studies as well as connecting behavior to underlying neural mechanisms. Perceptual multistability emerges as a promising noninvasive tool for clinical applications, facilitating translational research and enhancing our mechanistic understanding of cognitive processes and their impairments.

Online Now: Perceptual multistability: a multifaceted window into brain dysfunctions

If you need a method to infer causality from neural data, even when the signal is short, check our recent paper:

Paper: joss.theoj.org/papers/10.21...
Code: github.com/CMC-lab/Tran...

Thrilled to see the second paper of the lab out 🤩 Check it out if you need a method to infer causality from neural data, even when the signal is short!

Paper: joss.theoj.org/papers/10.21...
Code: github.com/CMC-lab/Tran...

Wanna infer causal interactions during brief, transient neural events? 🧠⚡

Many methods struggle with such non-stationarities, but we got a robust solution for you! 📦

My recent publication with @cmc-lab.bsky.social 🥳: TranCIT: Transient Causal Interaction Toolbox

📜 JOSS: bsky.app/profile/joss...

Many thanks to @klaus-tschira-stiftung.de and @gso-forresearchers.bsky.social for this wonderful opportunity to study the mechanisms underlying naturalistic decision processes in the context of foraging behavior!
We’ll also soon have openings for PhD students, stay tuned!

Portrait picture of Roxana Zeraati. Photo: Friedhelm Albrecht / University of Tübingen

🎉 @roxana-zeraati.bsky.social has been awarded the @klaus-tschira-stiftung.de Boost Fund!
Moving beyond short-term trials to naturalistic scenarios: Roxana will explore how we decide in changing environments.
🔗 www.kyb.tuebingen.mpg.de/zeraati-tsch...
#DecisionMaking #ComputationalNeuroscience

Enthusiastic to present our latest results for you and get your feedback.

Are you #Coayne2026? Wanna know how we can have a common notion of state in mind and machines and find them in an unsupervised fashion with temporal embeddings based on dynamic similarity check @armanbehrad.bsky.social and @maxschwabe.bsky.social’s poster today, 3-001!

3-001, poster session 3 (Sat, Mar. 14, 13:15): @armanbehrad.bsky.social on unsupervised method to find behaviorally relevant internal states from data. We tested it w/ RNN & monkey data doing cognitive tasks & RNN trained with deep RL on a naturalistic plum tracking task.

3-008 poster session 3 (Sat, Mar. 14, 13:15) [in collaboration w/ Zahra Monfared's lab zmonfared.github.io/DSAIlab/]: Anima will present how can we capture dynamics of transient activities such as well-known sharp wave ripples (SWR) based principled tools from dynamical system.

Are you at #Coayne2026? Did you enjoy our 1-031 posters on Thursday? Then don’t miss our 3-001 and 3-008 today 😉

If you are interested in analyzing sharp wave ripples with tools from dynamical system check out poster 3-008 at #Cosyne2026!

Can't wait to see these results at #cosyne2026

Don't miss @maxschwabe.bsky.social's poster this evening (our master's student and presenter awardee 🎉) on decomposing RNN dynamics during naturalistic decision-making! It was a joint work with @armanbehrad.bsky.social and @roxana-zeraati.bsky.social!

Looking forward to learn more at #Cosyne2026, and get feedback on our work (1-031, 3-001, and 3-008 also see thread 👇).

If you are interested in decomposing neural dynamics to understand how RNNs solve naturalistic decision-making tasks, visit 1-031 today at 20:30 and sai hi to @maxschwabe.bsky.social!

It was a blast to be at #MBBS2026 for the first time and presenting the first #brainbody research of the @cmc-lab.bsky.social! Learn a lot in Berlin (thanks to the organizers ❤️) and heading to #cosyne2026 for the second round of excitement 🤩

At #MBBS2026, Shervin Safavi @neuroprinciplist.bsky.social @cmc-lab.bsky.social presents their in-silico findings of #oscillator dynamics in #brainbody #embodied agents, stressing the role of #homeostatic state in #RL! ⭐️

#MBB #MindBrainBody #neuroskyence

Also let us introduce our new PhD student, Claire @clairesturgill.bsky.social, who joined us just 2 months ago, and excited to understand neural and computational mechanisms of internal decision proceess! Let's welcome her to Cosyne community 😍

3-008 poster session 3 (Sat, Mar. 14, 13:15) [in collaboration w/ Zahra Monfared's lab zmonfared.github.io/DSAIlab/]: Anima will present how can we capture dynamics of transient activities such as well-known sharp wave ripples (SWR) based principled tools from dynamical system.

3-001, poster session 3 (Sat, Mar. 14, 13:15): @armanbehrad.bsky.social on unsupervised method to find behaviorally relevant internal states from data. We tested it w/ RNN & monkey data doing cognitive tasks & RNN trained with deep RL on a naturalistic plum tracking task.

1-031, Poster session 1 (Thu, Mar. 12, 20:30): @maxschwabe.bsky.social (a Cosyne presenter awardee 🎉, a MASTER student) will present how decomposing neural dynamics to intrinsic and input-driven modes can tell us how naturalistic decision computations are realized in the RNNs.

CMC lab is heading to #Cosyne2026, with 3 wonderful posters (1-031, 3-001, and 3-008 also see thread 👇), 2 new enthusiastic team members (@clairesturgill.bsky.social and @maxschwabe.bsky.social ), and tons of excitement for discussions and new ideas!

This is very cool. Much faster DSA. Although we still don't know what DSA findings actually mean about brains.

In our Learning Club @cmc-lab.bsky.social today (Feb 19, Thu, 2pm CET), @neurostrow.bsky.social will tell us about his recent paper (👇). Want to attend? Send an empty email to virtual talk-link-request@cmclab.org to get the link!

Our next paper on comparing dynamical systems (with special interest to artificial and biological neural networks) is out!! Joint work with @annhuang42.bsky.social , as well as @satpreetsingh.bsky.social , @leokoz8.bsky.social , Ila Fiete, and @kanakarajanphd.bsky.social : arxiv.org/pdf/2510.25943

Built a domain-agnostic peak detection algorithm and now hunting for datasets with known/annotated peaks to test it on 👀
Any domain works—signals,bio,astro,finance, spectroscopy, etc.
Got data or know a benchmark? Would love pointers 🙌

#SignalProcessing #DataScience #TimeSeries #OpenData #Research

For the first Learning Club @cmc-lab.bsky.social in 2026, @marcusghosh.bsky.social on Jan 15, Thu, 2pm CET, will tell us about his recent paper (👇) [joint work w/ @neural-reckoning.org]. Want to attend, send an empty email to virtual talk-link-request@cmclab.org to get the link!

A diagram showing 128 neural network architectures.

How does the structure of a neural circuit shape its function?

@neuralreckoning.bsky.social & I explore this in our new preprint:

doi.org/10.1101/2025...

🤖🧠🧪

🧵1/9

Wanna compare dynamics across neural data, RNNs, or dynamical systems? We got a fast and furious method🏎️
The 1st preprint of my PhD 🥳 fast dynamical similarity analysis (fastDSA):
📜: arxiv.org/abs/2511.22828
💻: github.com/CMC-lab/fast...
I’ll be @cosynemeeting.bsky.social - happy to chat 😉