New work w/ Zach Kelso and @madeleinecsnyder.bsky.social
www.biorxiv.org/content/10.6...
Our negative results on classical conditioning in planarian flatworms. This was surprising, given the long history of work (including sensational findings of memory transfer and retention through decapitation).
"Although animals in natural environments accumulate a wide range of experiences that allow them to calibrate threat assessment, most behavioral studies of anxiety rely on laboratory animals housed in static, impoverished conditions."
🔥
www.cell.com/current-biol...
New paper in collaboration with @mhburrell.bsky.social @naoshigeuchida.bsky.social and @gershbrain.bsky.social !
"Phasic dopamine drives conditioned responding beyond its role in learning" 🧵 (1/8)
www.biorxiv.org/content/10.6...
Pleased to share our new study showing how different cell types in the paratenial thalamus interact with the medial prefrontal cortex and other brain regions, all done by my graduate student Nigel Dao:
www.biorxiv.org/content/10.6...
Delighted to share our discoveries about one of the brain's neurotransmitter systems:
www.biorxiv.org/content/10.6...
Together with colleagues at the @alleninstitute.org, we have learned a lot about a tiny cluster of neurons in the brainstem locus coeruleus (LC) that releases norepinephrine (NE). 1
New this year: free trainee pre-conference on July 13 organized by @cehaeffner.bsky.social @adanyajohnson.bsky.social
Pre-conference tickets are free for main conference attendees but space is limited to 75 people so register early!
www.cpconf.org/faq-cpconf20...
How can machines achieve the kind of flexible learning mastered by our brains? That’s what our own @neurokim.bsky.social is trying to find out! Learn more about ARNI’s work on natural and artificial intelligence:
youtu.be/gM8YCaiAWrc?...
#BrainAwarenessWeek
@jhennig.bsky.social has shown that dopamine exerts a real-time effect on conditioned responding, beyond its role in learning:
www.biorxiv.org/content/10.6...
Another indication that dopamine is more than a learning signal!
A joint effort with @naoshigeuchida.bsky.social and @mhburrell.bsky.social.
Thrilled to share our new paper, which shows that the relative timing of cholinergic and dopamine release dynamically gates whether dopamine acts as an RPE for in vivo plasticity and reinforcement learning. www.nature.com/articles/s41...
🧵 New preprint led by @bingbrunton.bsky.social, @elliottabe.bsky.social, @lawrencehu.bsky.social
We gave a worm brain control of a fly body and it walked
What did we learn? Nothing, other than deep reinforcement learning is effective
We call it the digital sphinx
www.biorxiv.org/content/10.6...
A remarkable journey of resilience and transformation, from the chaotic corridors of group homes to the halls of Columbia and Stanford, EMERGENCE is a coming-of-age tale where heartbreak and humor meet the scientific wonder of modern artificial intelligence.
🔗 Preorder: tinyurl.com/fzcxb5ea
Mammals have hundreds of joints and muscles. Controlling them individually would be nearly impossible.
How does the nervous system organize such complexity into coherent actions?
Our new study explores this question through a natural behavior: jumping.
Awesome work! This is super cool. May I ask, what is the actual temporal resolution for this tagging method?
The deadline for applying to the Methods in Computational Neuroscience summer course at MBL in Woods Hole is approaching (March 16)! An exciting course with an amazing lineup of lecturers in a beautiful location www.mbl.edu/education/ad...
I tracked every keyword in 22 years of Cosyne abstracts to map how computational neuroscience evolved — from Bayesian brains to neural manifolds to LLMs — and where it's heading next.
Come check out our poster at Cosyne 2026, on Thursday! Plasticity vs dynamics, RNN with online learning rule, inference based on structure and many other ideas!
BioRiv paper:
www.biorxiv.org/content/10.6...
Every time you experience something new, your brain faces a decision: Should it update an existing memory or create a new one?
In our new paper in @sfnjournals.bsky.social #JNeurosci, we isolate that exact decision, moment-by-moment during learning 🧵
First preprint from the lab! Using intracellular recordings & analysis of 2-photon imaging data, we show that spiking & neuromodulatory input during experience drive a reorganization of visuomotor inputs in V1 layer 2/3 neurons, consistent with enhanced visuomotor cancellation - bioRxiv link below.
Broad peer review is crucial for a healthy scientific literature, but neuroscientists turn down review requests too often. Simple math suggests that small groups of scientists can significantly bias the literature, writes @jvoigts.bsky.social.
#neuroskyence
www.thetransmitter.org/publishing/l...
Additional details are provided in the preprint, so I would greatly appreciate any feedback you may have!
I would like to thank my mentors, @naoshigeuchida.bsky.social and @gershbrain.bsky.social, for guiding me throughout this project; @jhennig.bsky.social for his key contributions to the modeling; and the entire Uchida lab for their support!
3. Finally, going beyond anti-correlated structure, we show that mice can infer value based on different types of structure (e.g., negative correlation, positive correlation, or independence). Here, structure refers to how the values of two cues are correlated over time.
2. On the theoretical side, we implement a value RNN but with continual online plasticity, and show that it displays a transition from plasticity to dynamics based value update. So a single learning rule can produce both types of value learning.
1. Our answer: Meta-RL. We show in mice that there is a transition from plasticity-based value update to recurrent dynamics-based value update. A key brain for this transition seems to be BLA! This is a putative biological implementation of meta-RL proposed by many previous works.
🚨🚨New Preprint Alert!🚨🚨
www.biorxiv.org/content/10.6...
Animal learning is painfully slow (at least initially). Yet, well trained animals can learn very fast, sometimes displaying few-shot inference. How does this transition occur?
