"the extremely strong desire is to just punish universities [..] It’s not based on any kind of coherent policy agenda. It’s just a desire to inflict pain."

This, is critical to understand. Pointing out "it's hurting us" won't do, since it's the whole point.

www.nytimes.com/2025/02/14/o...

OK folks, I have now created a google form that allows people to sign to show their support for my open letter to the President of the Royal Society. Please sign and share widely: forms.gle/miDciq35oxyw...

Wikipedia is one of the last major bastions of verified information. Which, of course, is why the oligarchs want to destroy it.

You can donate to them here.

donate.wikimedia.org/w/index.php?...

Congratulations to @juliencorbo.bsky.social and all the co-authors for this amazing study! 🎉🏅🤯. Don’t miss Julien’s thread 🧵 👇🏻 that summarizes very well the main points of the paper.

Please check out the paper (and the supplementary figures) there is a ton more to this story that could not fit in a post!
#neuroskyence #visualcortex #sensory #perception

What shapes the discrete domains? Surround suppression in orientation space! Neurons preferring orientations flanking task stimuli are less responsive, creating a W-shaped modulation reshaping representations. Distortions not present in Naïve 🐭, their orientation representation space was more “flat”

Does activity in these domains drive behavior? We computed a "Go evidence" ratio: relative activation of Go & NoGo domains. It explained ~90% of the variance of perceptual decisions, as if V1 outputs probabilistic Go/NoGo estimates! Suggests discrete, domain-based integration of V1►sensory decisions

When task is difficult (NoGo° close to Go°), V1 doesn't encode stim orientation faithfully. Discrete domains emerge: "Go" (~45°) & a "NoGo" (~90°) in which the activity is constrained. Surprisingly NoGo resp. are bimodal-hinting at categorical processing. Not observed in Naïve 🐭 passively viewing

Behavioral thresholds vs. neuronal capacity: Mice trained in a Go/NoGo orientation task showed discrimination threshold between 15°–30°. But V1's neural activity contained more information; shallow neural nets trained on V1 data vastly outperformed behavior
➡️ at 20° angle, SNN D'>4, Mouse D'< 1.7

Through task training, V1 transitions from continuous to categorical representations that predict the 🐭 behavioral performance. Task-driven excitability modulation splits the evoked activity into discrete domains, suggests a probabilistic encoding of the stimuli category (Go/NoGo). More details in 🧵

New paper out! 🚨 📰 With @batuhanerkat.bsky.social, John McClure, @hussainyk1.bsky.social, @polacklab.bsky.social we reveal how discretized representations in V1 predict suboptimal orientation discrimination. 🧪🧠🐭 This work reconciles neuro and psychometric curves
www.nature.com/articles/s41...

That sounds cool! What's the paper ref? We didn't look at correlations per se but learning reduces the trial to trial variability of the responses, which I'm sure goes along with more correlated ensembles.

Probably one of the most known already, but Brain inspired never fails to inspire me @braininspired.bsky.social

The wonders of population coding! Recent paper by @juliencorbo.bsky.social et al. @polacklab.bsky.social -> visual cortex in trained mice encodes orientation differently from in naive mice... almost more "messily"(?)... but behavior is good. #neuroskyence #matlab 🧪 🧠
doi.org/10.1038/s414...

Am I too late to the party?