Jianing Mu

@mujianing.bsky.social

She/her. Neuroscience PhD student @UC Berkeley. Formerly @UT Austin, Haverford College. https://mujn1461.github.io/jianingmu/

New preprint! We show low arousal states promote hippocampal ripple genesis in sleep and wake. This bridges rodent work, where ripples predominate during sleep, with human studies reporting ripples during active behavior, identifying low arousal as a common mechanism www.biorxiv.org/content/10.6...

Arousal state modulates human hippocampal ripples

Hippocampal ripples are transient, high-frequency oscillations linked to memory replay and consolidation. Ripples are well-characterized in rodents to occur during periods of behavioral inactivity (i....

biorxiv.org

We're recruiting postdocs to study how people build structured knowledge from experience & use it to act flexibly. Behavioral work, fMRI, computational & AI models, with a lifespan focus. You don't need experience with every method; fit & curiosity matter most. Please repost & share! 🧠

Preston Lab, University of Texas at Austin. Now recruiting 1–2 postdoctoral researchers in learning, memory, development, and flexible behavior. Methods: behavioral experiments, fMRI, computational modeling, and naturalistic stimuli. Apply with CV, research statement, and 3 references to apreston@utexas.edu. Photos show the lab group smiling together, members working at a table, and members at a celebration.

The preprint of my 1st project in grad school is up 🙌 We propose a simple, information-theoretic model of how humans remember narratives. We tested it with the help of open-source LLMs. Plz check out this thread for details ➡️ Many thanks to my wonderful advisors! It's been a fun adventure!!

Alexander Huth@alexanderhuth.bsky.social · last yr.

New paper with @mujianing.bsky.social & @prestonlab.bsky.social! We propose a simple model for human memory of narratives: we uniformly sample incoming information at a constant rate. This explains behavioral data much better than variable-rate sampling triggered by event segmentation or surprisal.