TBF, “Please verify all ring magics with a web search for non-corruption” works better.

1) This is quite cool 2) Is “polysemanticity” just the AI word for “mixed selectivity”? Or is there some difference I’m missing

IMO a key skill of a good scientist is moving comfortably between the specific & general e.g., relentless in understand the nerdy details of the data (including its coding) WHILE holding onto the big picture of the hypotheses being tested with the data

god I love being a researcher so much, what a dream

I maintain my stance: We use "mechanism" to indicate those theories that seem to give us real OOD predictive power + the ability to intervene on the system and control it.

Time to bump this up the ol’ reading list. academic.oup.com/book/36363

Many regions code for impact factor. Very cool!

This is not funny. Brutalist buildings only do this when they’re in extreme distress…

A classic understated, dry cartoon joke.

Worms fish and flies really coming out on top today.

My slightly sobering take on this timeless (and important) question in cognitive/brain science is that full observability, perturbability, etc can be useful (though often overkill) but they are overrated (or far too prioritized compared to other things we might care about to make progress)

What would you do if you had full access to observe and manipulate a brain during development, learning, and behavior?

Overheard at lunch: “You’re exhausting my tokens”

“This might be the largest product recall in memory—we sold a bogus idea of math to billions of people, and the bill is coming due.”

TFW you’re Claude today.

Fascinating short history, including how "Isaac Newton’s famous Waste Book is a rare example of a physical continuity between the two cultures of notetaking: humanist and scientific." www.asimov.press/p/lab-notebo...

LLM consciousness discourse is a great way to see the diverse range of conditions for consciousness that different people assume

Anything to avoid the metric system 🌖🐕🚶🌭

This is unfortunately an issue that isnt going away I'm not against AI for science code implementation - apart from being faster it probably also just makes fewer mistakes than most PhD students (it's definitely better than me). The challenge is how (and in what form) to train people to oversee it

The horse op-ed is an instant classic. I can't tell you how much joy this piece gives me. It should be taught in every introductory writing class in no small part because the horse arguments are so compelling. "I have noticed that human children get to eat cake. But I am bigger than the children."

Turns out the real treasure was the stupid shit we did along the way.

2026 sucks but at least we can watch space TV

The science ( knowing *which* models to fit, *why*, and what the answer means) still requires expertise. It didn't happen automatically. But the implementation did. What are we actually training our students *for*? And are we teaching the right things? We really need to start answering this. 3/3

I think part of the reason why people find this controversial is that they read into it "therefore there is no difference". But as the prof for intro to philosophy said in the first year of my BA: "The fact that there are unclear cases does not mean there aren't clear cases"

The hardest part of science is posing the right question, not answering it.

Recognizing LLM-generated text isn’t just a skill—it’s a life sentence.

If you haven't claude coded up a cute little side project yet you should absolutely check that out ASAP.

I love how in the space of ~10 years the field has moved from “the brain can’t do gradient descent” to “which of the many possible ways of implementing gradient descent is the brain doing, in which circuits?”

This is the most insider shade ever and I’m here for it. #wormFly4pres2026🪱🪰

Biology is full of coconuts. 🥥

[please bear with me, posting through it]

We need to acknowledge that science has always emerged from a mess and a lot of misunderstanding. That is the constitutive character of the scientific process. Failures of understanding at the individual level can even be critical for collective progress. Science is a distributed activity.

Now that I think about it, I do have a similar experience with email vs slack for research communication… Curious if/how others address this in their labs. I don’t think Making Email Great Again is the solution?

Really stoked to see this project in (pre)print. On how to implement normalization-via-inhibition in ANNs, and what it might mean for inhibition in the brain. ⚖️ Great job @royeyono.bsky.social and the team 👏👏👏

You’d be surprised how much I learned about being an academic from being a musician in a past life…

Post your posters 😁

A cool insight here: CDM isn’t just hard to infer, it’s hard to train directly (because it can’t be specified as a loss on systems behavior). This means it either needs to emerge as a consequence of other losses, data, or architecture choices.

Is it just me or do the cosyne posters y’all are postering this year sound particularly cool? 😎🪼

How are the fluctuations in electric field organized across the whole brain? We analyzed the 3.4 M samples from the international brain lab using a combination of deep learning and graph theory. We found a surprising structure made of communities and landmarks. doi.org/10.1101/2025...

Don’t forget to look up.

For those traveling to #cosyne2026, sad to miss catching up this year, but here's two posters you should check out: 1) Complementary roles of spatial tuning and population geometry in place- vs. memory-guided navigation w/ @audiodrome.bsky.social @apeyrache.bsky.social @tyrellturing.bsky.social.

I tracked every keyword in 22 years of Cosyne abstracts to map how computational neuroscience evolved — from Bayesian brains to neural manifolds to LLMs — and where it's heading next.

The real berries were the friends we made along the way.

Already having #cosyne2026 FOMO and it hasn’t even started yet 😿

IMO there’s more theory in neuroscience than we give it credit for, but it doesn’t percolate through the field as much as you would hope/need for broader understanding.

I want to write a fun little post on what we've learned in neuroscience in the last 20 years. What are the most interesting results you can think of? Biggest trends?

Compositional representation of self, others, and gaze direction in *human* hippocampus - super cool. arxiv.org/abs/2603.04747

I'm not gonna touch most of this but two things: 1. The apprenticeship model of science was never supposed to be about the PI's workflow. The point isn't to have research assistants to help to publish stuff faster. Training the next generation researcher *is* the point. Because we care. Allegedly.

science is very very international which is very very good for science and very very good for the world it's worth remembering that this + the nature of networks means that when bad things happen in any single part of the world, all scientists are only 1-2 degrees separated from it

These reviews on neural manifolds are super relevant to today’s world of large-scale population recordings. How do we link circuits, population geometry, dynamics, and function? “A neural manifold view of the brain” from Perich, Narain, and Gallego 2025 www.nature.com/articles/s41... (1/2)