Finally, if you're looking for a postdoc who can do it all, Vinh is currently finishing a round-the-world cycling trip and is interviewing for positions! Please reach out if interested.

If you prefer a talk to a paper, I walk through the full results and the story in this video: www.youtube.com/watch?v=rNQY...

While the complex spike error signal from a single pull can only provide miniscule LTD/LTP of parallel fibers onto a Purkinje cell, the combined effect across the population of Purks is robust enough to influence the very next joystick pull.

Imaging complex spikes in populations of Purkinje cells during learning reveals the answer: bands of cells respond similarly. Together, these cells encode both the magnitude and the direction of the behavioral error.

But how can mice learn and form a memory so quickly if climbing fibers only provide a binary error/no error signal to Purkinje cells (as in classic supervised learning models)?

Like humans wearing prism goggles, mice learn to adapt their behavior in only a few trials (dark blue). They retain a memory of this adaptation, which they need to "unlearn" when returned to control conditions (light blue).

We trained mice to use a joystick to retrieve water. To test their cerebellar learning, we imposed a perturbation (gain = 2x; lower panel) that they needed to overcome to retrieve the reward.

Academia may be a prestige economy but it wasn’t some choice of scientific leadership- we do it to ourselves, every day. Leadership rarely makes tenure criteria too down - it’s a committee of peers that sets it up for institutions. Leadership doesn’t sit on study sections - it’s peers doing it!

Almost as impressive as the brown trout you can find in the water just outside.

Brandon Stell puts it powerfully - and accurately: 'We value publications more than what's inside them.'
It's not something we wanted to hear, but it's something we needed to.

Learn more about the PubPeer Monthly Prize—made possible by the Einstein Foundation (@einsteinberlin.bsky.social‬)—and explore past winning comments to see what makes a strong entry so that you can win the next one!

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