Hiring for research scientists in expansion chemistry and optical physiology at mindspan.org with @eboyden3.bsky.social, Yongxin Zhao, Xue Han. We will build tools to analyze the human brain. We are into compiling arxiv.org/abs/2603.25713. We are into cutting edge technology.

The identification results above are mathematically formalized and proven in the full paper: arxiv.org/abs/2605.03268 I thank my co-authors Jordan Matelsky, Martin Butz, Charley Wu, and Konrad Kording. @jordan.matelsky.com @thecharleywu.bsky.social @kordinglab.bsky.social

Partially Observed Structural Causal Models

Here we introduce Partially Observed Structural Causal Models (POSCMs) as an extension of structural causal models (SCMs) to settings where upstream contexts co-determine both the interaction structur...

arxiv.org

We use eccentricity (distance from the center of the retina) as a stand-in for β, and recover the sigmoidal BC (bipolar cell) → RGC (retinal ganglion cell) transfer curve (stand-in for mechanism f) with the help of β and V interventions.

Positive result: with observational + β-node + V-node interventions, the kernels α, {φ_i}, Γ are identifiable. We validate this in a retina simulator. Retina is a natural POSCM: cell type co-determines both wiring and synapses, and the laminar circuit supplies the ordering τ.

We provide both positive and negative identification results: Negative: With no β-level interventions, α, {φ_i}, and Γ are non-identifiable. V-level data cannot break a symmetry that lives in Phase I.

To enable edge interventions we introduce per-parent decomposition of mechanisms that we call edge-local messages:

We show the power of edge interventions in POSCMs using the distributive law of multiplication. x·(y+z) and x·y+x·z agree under every node intervention do(x,y,z) when the internal ops are latent. Perturb one channel of x: LHS 3·(1+1)=6, RHS 3·1+2·1=5. Graphs are separated.

The connectome-shifting, economic-link-rewiring interventions can be modeled in POSCMs as the Phase-I context interventions that alter the graph-generating measure: P(A_j | do(β_j = b)).

Generation happens in two phases: Phase I: A_ji ← α(β_j), then β_i ← φ_i(β_pa) Phase II: f_i ← Γ(β_i, Pa(i)), then V_i ← f_i(V_pa) The augmented graph β₁→A₁₂→β₂→… stays acyclic. SCMs are the degenerate case A ~ δ_A*, f ~ δ_f*.

POSCM's constituent parts (context β, structure A, mechanisms f, values V) are jointly generated and can be partially observed via noisy channels. Furthermore, context shapes wiring (A), wiring shapes context. To avoid circularity, we impose generation over an exogenous order τ.

UAI 2026 paper: A genetic perturbation can change the connectome, a new policy can rewire economic links between firms. In each case, interventions change the graph itself which standard SCMs treat as fixed. We tackle this in Partially Observed Structural Causal Models (POSCMs).

I think this is importantly work bringing causality closer to reality.

Turan Orujlu@turanorujlu.bsky.social · last mo.

UAI 2026 paper: A genetic perturbation can change the connectome, a new policy can rewire economic links between firms. In each case, interventions change the graph itself which standard SCMs treat as fixed. We tackle this in Partially Observed Structural Causal Models (POSCMs).

UAI 2026 paper: A genetic perturbation can change the connectome, a new policy can rewire economic links between firms. In each case, interventions change the graph itself which standard SCMs treat as fixed. We tackle this in Partially Observed Structural Causal Models (POSCMs).

Come join us!

Mark Ho@markkho.bsky.social · 3mo ago

How do human minds make sense of big, messy problems? 😵‍💫🌀 How do we distill complexity into something simple enough to solve? 🤔💡 We’ll be tackling these questions (and more!) at two workshops on task representations, abstractions, and construals #CogSci2026 #CCN2026 🧵 framing-the-problem.github.io

Excited to announce that I'll be starting my own lab in Tübingen this October! Hiring at all levels: Postdoc, PhD & RA. Want to work on computational cognitive science at scale? Apply: core-cognition.github.io Reposts and shares much appreciated 🙏

Some incredibly exciting news that still feels a bit surreal: I am starting my research group @mpicybernetics.bsky.social! The best part? We are hiring at all levels! Curious about the science we plan to tackle, read along :) #NeuroSky

I don't think our culture is ready for accepting this idea, and probably some percentage of humans never will be. My guess: hundreds of years from now a majority will agree with this, but many still won't, and it will correlate with religion and education. (See, e.g., Darwinian evolution) 🧠📈 🧪

Earl K. Miller@earlkmiller.bsky.social · 4mo ago

There Is No Hard Problem of Consciousness www.noemamag.com/there-is-no-... #neuroscience

arXiv📈🤖 Partially Observed Structural Causal Models By Orujlu, Matelsky, Butz et al

Turan Orujlu, Jordan Matelsky, Martin V. Butz, Charley M. Wu, Konrad P. Kording: Partially Observed Structural Causal Models https://arxiv.org/abs/2605.03268 https://arxiv.org/pdf/2605.03268 https://arxiv.org/html/2605.03268

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...

TranCIT: Transient Causal Interaction Toolbox

Nouri et al., (2025). TranCIT: Transient Causal Interaction Toolbox. Journal of Open Source Software, 10(116), 9302, https://doi.org/10.21105/joss.09302

joss.theoj.org

Salar Nouri@sanouri.bsky.social · 5mo ago

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...

Wanna compare dynamics across different systems? See our fast and furious method developed by us (@armanbehrad.bsky.social @mmdtaha.bsky.social, @neuroprinciplist.bsky.social) together with our wonderful collaborators! You can already use our code: github.com/CMC-lab/fast...

GitHub - CMC-lab/fastDSA: Fast DSA package

Fast DSA package. Contribute to CMC-lab/fastDSA development by creating an account on GitHub.

github.com

Arman Behrad@armanbehrad.bsky.social · 8mo ago

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 😉

My new lab in Singapore is seeking a full-time RA to help investigate how exploratory cognitive states shape environmental representations (cognitive maps). The position is funded until late 2027, and comes with a postdoc-level salary package. Please forward! careers.sutd.edu.sg/job/Singapor...

Proud of the work from hmc-lab.com & collaborators @ #CogSci2025 this year, but sad I cant be there myself @hanqizhou.bsky.social @davidnagy.bsky.social @alexthewitty.bsky.social @stepalminteri.bsky.social @brendenlake.bsky.social @kefang.bsky.social @rdhawkins.bsky.social @meanwhileina.bsky.social

SCMs often assume overly-dense causal graphs in dynamic settings 👉⚽🥎, since any object interaction is a potential causal edge, making them hard to scale. In joint work with @turanorujlu.bsky.social @cgumbsch.bsky.social & Martin Butz we propose a new Causal Process Model to tackle this. Thread👇

Reframing attention as a reinforcement learning problem for causal discovery

Formal frameworks of causality have operated largely parallel to modern trends in deep reinforcement learning (RL). However, there has been a revival of interest in formally grounding the representati...

arxiv.org

Turan Orujlu@turanorujlu.bsky.social · last yr.

Reframing attention as an RL problem for causal discovery AI models like GNNs & Transformers can struggle with dynamic causal reasoning. Our work introduces the Causal Process Model (CPM), which reframes attention as an RL problem. Agents dynamically build sparse causal graphs.

Reframing attention as an RL problem for causal discovery AI models like GNNs & Transformers can struggle with dynamic causal reasoning. Our work introduces the Causal Process Model (CPM), which reframes attention as an RL problem. Agents dynamically build sparse causal graphs.