Two flagship papers from the International Brain Laboratory, now out in ‪@Nature.com‬: 🧠 Brain-wide map of neural activity during complex behaviour: doi.org/10.1038/s41586-025-09235-0 🧠 Brain-wide representations of prior information in mouse decision-making: doi.org/10.1038/s41586-025-09226-1 +

Princeton’s @ilanawitten.bsky.social, @engeltatiana.bsky.social, and @jpillowtime.bsky.social have created the first-ever brain-wide activity map during decision making in mice as part of an international group effort known as the @intlbrainlab.bsky.social. 🔗 pni.princeton.edu/news/2025/fi...

New work with @shiyanliang.bsky.social‬, @roxana-zeraati.bsky.social, @intlbrainlab.bsky.social, Anna Levina. We uncover the principles that organize single-neuron timescales across the entire brain, unifying regional specialization with universal brain-wide dynamics: www.biorxiv.org/content/10.1...

Out today in @nature.com: we show that individual neurons have diverse tuning to a decision variable computed by the entire population, revealing a unifying geometric principle for the encoding of sensory and dynamic cognitive variables. www.nature.com/articles/s41...

“Like a group of skiers descending a mountain, each [neuron] prefers a slightly different path, but all are shaped by the same slope,” says PNI's @engeltatiana.bsky.social‬ on her lab’s new ‪@nature.com‬ study revealing how the brain makes decisions. 📰: pni.princeton.edu/news/2025/al...

Into population dynamics? Coming to #CNS2025 but not quite ready to head home? Come join us! at the Symposium on "Neural Population Dynamics and Latent Representations"! 🧠 📆 July 10th 📍 Scuola Superiore Sant’Anna, Pisa (and online) 👉 Free registration: neurobridge-tne.github.io #compneuro

Our new paper with @chrismlangdon is just out in @natureneuro.bsky.social! We show that high-dimensional RNNs use low-dimensional circuit mechanisms for cognitive tasks and identify a latent inhibitory mechanism for context-dependent decisions in PFC data. www.nature.com/articles/s41...

The latent circuit model identifies low-dimensional mechanisms of task execution from heterogeneous neural responses. This approach revealed a latent inhibitory mechanism for context-dependent decisions in neural network models and prefrontal cortex 🧪🧠 www.nature.com/articles/s41...