Dan Levenstein

@dlevenstein.bsky.social

Neuroscientist, in theory. Studying sleep and navigation in 🧠s and 💻s. Wu Tsai Investigator, Assistant Professor of Neuroscience at Yale. An emergent property of a few billion neurons, their interactions with each other and the world over ~1 century.

One theme in our conversation was how much building models is an "art": Laureline @laurelinelogiaco.bsky.social shares her approach to theory that connects to data, w/o being bound by it. And how good models involve *deliberate* choices about abstraction and detail, rely on intuition and judgement!

Stories of WiN@storiesofwin.bsky.social · 3d ago

Our latest profile is here! Dr. Laureline Logiaco (@laurelinelogiaco.bsky.social) studies multi-region computation for flexible and robust motor control. Click below to listen and learn more! #StoriesOfWiN #WomenInNeuroscience www.storiesofwin.org/profiles/202...

Took Barb’s cognitive neuroscience class during my masters, when I was considering a switch from biophysics to comp neuro. The grad student discussion section was held at the local pub and always came back to the importance of evolution, development, and large-scale neuroanatomy of not-just-cortex…

The Transmitter @thetransmitter.bsky.social · last wk.

Barbara Finlay, who died on 12 September, championed the idea that developmental constraints dictate how evolution sculpts brains, which she used to model the timing of hundreds of events and align them across mammalian species. By @callimcflurry.bsky.social www.thetransmitter.org/evolution/re...

4/7) It's only by tying the emergence of that manifold to the architecture, loss, learning rule, and *data/world* that the network was shaped by that we arrive at any additional knowledge gained by observing the manifold.

I've been trying to figure out how to explain this tendency of LLM writing to use terms it hasn't introduced as if you already know what it's talking about, but it's a dead giveaway that's a little more subtle than "delve" or em-dashes.

I had a feeling recently that seems to be corroborated by chats with colleagues, so I decided to share it here. A lot of systems neuroscientists driven by algorithmic-level questions are going through what looks and feels like a research depression. 🧵 (1/n) 🧠📈 #NeuroAI

I kinda get why people hate on fussy philosophical stuff, but on the other hand don't you think we live in a time where it'd be especially helpful to have rigorous conversations about things like personhood, meaning in life, the point of art, the point of pure mathematics, & operationalizing ethics?

There’s a Ted Chiang story where AI has a parallel AI-only scientific literature. In that case, AI was doing all the real science and humans were left to pick up the scraps and try to understand what they could… Not sure if I’d prefer that or slopfest.

Christophe 🔬 Leterrier@christlet.bsky.social · last mo.

Hallelujah! Papers written by an AI and reviewed by an AI can finally be read by an AI, fulfilling the prophecy of the Great AI Circlejerk

If there are any PhD hopefuls on here, I wanted to share a few thoughts on your timeline for the upcoming admissions cycle, with apps due starting mid-November. What should you be doing right now? ✔️ Generating a list of schools ✔️ Reaching out to possible recommenders 🧵!

The superposition of these two studies — both incredible — reflect a snapshot of "one big thing" systems neuroscience has yet to resolve. In mice: 1. Task-related signals are everywhere. 2. Absent large swaths of "everywhere," suprisingly sophisticated learning & behavior remains.

Post by Enny van Beest, "Brainwide representation of navigation"Post by Jieyu Zheng, "How fast can mice learn complex mazes?"

Yale Neuroscience is hiring another computational neuroscience position this year! Please share with anyone who might be interested, and feel free to reach out if you have any questions about the position, or growing neuro-AI community at Yale/WTI!

Yale Neuroscience@yaleneuro.bsky.social · 2mo ago

The Department of Neuroscience at Yale is hiring faculty members. We have 2 searches: one general (deadline Oct 15), one focused on computational neuroscience (deadline Nov 30). Apply! apply.interfolio.com/190736 & apply.interfolio.com/190740

We were actually discussing this at a workshop earlier this year. In particular: The dynamics of BTSP actually seem to potentially be counter to a gradient descent mechanism, and more in line with hashing.

I gave a talk in our ISRC CN3 summer school yesterday on computational modelling of learning and memory, at the end I threw in a list of what I consider the big open questions in the the theory of learning in neural circuits. What did I miss??

Slide with title text "Open directions in theory of learning in neural circuits". List of points:
- Does the brain learn by gradient descent? If so, how?
- How are memories stable if synaptic strengths − and brain representations − fluctuate over time?
- How do brains learn continually over a lifetime?
- How do brains learn so efficiently? (In terms of energy, data, wiring constraints)
- Why are synapses so molecularly complex, compared to ANN weights?