Carsen Stringer

@computingnature.bsky.social

group leader @ HHMIJanelia, #neuroscience + AI 🔬 #cellpose | diversity and open-science for better science | she/her | https://mouseland.github.io

@vincentcostaphd.bsky.socialReposted by @computingnature.bsky.social

The game is implementation details and the depression you feel is that you actually have to grapple with biology if you want to make a unique contribution.

@okaysteve.bsky.socialReposted by @computingnature.bsky.social

🚨 new work from the lab!

"Hippocampal astrocytic sequences emerge during learning and memory recall" by @ryguy.io & @rsuthard.bsky.social

thank you to the entire team for your camaraderie and for muscling through science together 🧠 -- we welcome all feedback!

nature.com/articles/s41...

@hokuba.bsky.socialReposted by @computingnature.bsky.social

The Agrawal lab has published its first paper! "Sticking the landing: leg mechanosensory feedback coordinates the transition from flight to landing"

www.biorxiv.org/content/10.6...

This project was led by a technician in the lab, Tse-Wei (Wayne) Kuo. Brief 🧵on what we found

@mariamaly.bsky.socialReposted by @computingnature.bsky.social

How do keep a narrative active in our mind despite interruptions? The default mode network can maintain a representation of an interrupted story over a delay, even when the delay is filled with a demanding task. This representation is strikingly transformed from the initial experience!

@glajoie.bsky.socialReposted by @computingnature.bsky.social

In brain-computer interfacing, clever and adaptive decoders help brains learn complex tasks faster. These can impact the nature of solutions learned by the brain. Our new paper is one of the first attempts to study this effect. W/ @neuroamyo.bsky.social and team

www.nature.com/articles/s41...

@sstaszko.bsky.socialReposted by @computingnature.bsky.social

New preprint alert! 🚨 🧠

My postdoctoral work in Al Kaye’s lab has focused on understanding how animals navigate and learn from threats in their environment.

To study this, I developed a virtual visual cliff height threat task, inspired by Boone et al. 2021.

www.biorxiv.org/content/10.6...

@carandinilab.netReposted by @computingnature.bsky.social

If you thought "everything is everywhere" in the brain:
inactivations reveal a clear distinction of labor.

Sensory areas sense, colliculus promotes eligible actions (!), and prefrontal cortex integrates.

The role of superior colliculus in a logistic decision
www.biorxiv.org/content/10.6...

@mehr.nzReposted by @computingnature.bsky.social

here are some really gorgeous results from the Nock lab on predicting suicide before it's attempted — essential data for suicide prevention

@juliusgoat.bsky.socialReposted by @computingnature.bsky.social

Our society's unfathomable cruelty to homeless people is not just morally unjustifiable, but more expensive than housing people would be. Cruelty against the homeless is an unaffordable luxury. Abandoning homeless people to avoid offending the Protestant work ethic is an unaffordable luxury.

ProPublica@propublica.org

ProPublica spoke to dozens of people who had lived in homeless encampments when cities cleared them out in “sweeps.” We distributed notecards so people could tell us about the toll in their own words. ➡️ This is what they wrote:

Handwritten notecard. Prompt at the top asks: What object was most devastating for you to lose, and how have you been coping? Answer below: Dentures. Were thrown away and coping isn't somthing I can do. It’s made me feel ugly, unworthy, can’t go get a job with no teeth. So how can get off the streets until another pair can be made?
@nicolecrust.bsky.socialReposted by @computingnature.bsky.social

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?"
@computingnature.bsky.social

🚨🧠 Our paper is out! We introduce a simple computational model that generates macroscopic, long-timescale dynamics as seen in large-scale neural recordings. #neuroscience #dynamics @marius10p.bsky.social @zhong-lin.bsky.social @hhmijanelia.bsky.social Link: go.nature.com/4tRNjIu

Nature@nature.com

Nature research paper: A critical initialization for biological neural networks go.nature.com/4tRNjIu

go.nature.comA critical initialization for biological neural networks - NatureBasic properties of symmetric random matrices can explain the emergence of macroscopic patterns in neural networks, suggesting that initialization conditions of connected neural populations may confer computational advantages across the mouse brain.