I'm excited to share that my new postdoctoral position is going so well that I submitted a new paper at the end of my first week! A thread below

Our Fall/Winter ZIPS series is starting soon! We will kick it off on October 15th with talks from Drs. @sreejan_kumar and Luis Flores! See details below.

I'm recruiting PhD students to join my new lab in Fall 2026! The Shared Minds Lab at @USC will combine deep learning and ecological human neuroscience to better understand how we communicate our thoughts from one brain to another.

@matthieulc @marcelomattar @TrackingPlumes Tagging some people that may find this interesting: @modirshanechi, @markdhumphries, @LnccBrown, @neuro_kim, @somnirons, @KevinMizes

Thanks for reading! Special and huge thanks to my co-first author @matthieulc and senior authors @marcelomattar and Jonathon R. Howlett, as well as co-authors @TrackingPlumes and Lea Duncker! The work wouldn't be possible without all of them.

Second, it's known that we build compressed abstractions of our environments that allow us to generalize. What's maybe not known is that this process is intrinsically tied to forming habits and complex action plans!

What are the implications? First, sensory compression is not just in DLS. It's also in other areas such as Hippocampus and Cerebellum. So we predict that wherever there is sensory compression happening, there is also time encoding and support of time-sensitive behaviors.

This is because sensory compression produces intrinsic, task-independent time encoding dynamics and these dynamics act as a scaffold to implement timing of task-specific behaviors.

We then show it accounts for another result that shows something contradictory: the DLS actively uses sensory stimuli to time and execute motor habits.

We then show that this model accounts for seemingly paradoxical findings in time representations in the DLS. First, we show our model explains results that encoding of time in rat DLS is invariant to task relevancy and stimulus properties.

We then see that bottleneck models engage these stable neural trajectories that implicitly encode time by where you are in the trajectory.

We show that a model with a sensory bottleneck accounts for many behavioral effects that @gershbrain and @drlucylai characterize in their work on human action chunking, whereas a non-bottleneck baseline does not.

To test our hypothesis on the effect of sensory compression on action chunking and time coding, we developed an RNN model with sensory bottlenecks and trained it on RL tasks that involve chunking.

The DLS is known to be a "bottleneck" in sensorimotor processing. Millions of cortical neurons project onto orders of magnitude fewer striatal cells, producing highly favorable conditions for compression.

If these functions are co-located, one might believe there's a common mechanism for them. Our work suggests that this mechanism is sensory compression!

What's another function the DLS is involved in? Time encoding! According to a review paper by Edvard and May-Britt Moser (2014 Nobel prize winners), the brain tracks time through "stable neural trajectories" where cell populations fire predictably along a trajectory.

A region of the brain that's a big driver of action chunking is the Dorsolateral Striatum (DLS)

A primary way this manifests in behavior is through action chunking, where predictable action sequences become compressed into cohesive, reusable units. Think of typing a familiar password, phone number, or playing a well-practiced song on an instrument.

Why do we brush our teeth without having to think about it? Our brain can learn habits through repetition. Habits become automatized in that, once they’re formed slowly over many repetitions, we can execute them automatically without having to “think” about them.

Thanks for reading! Tagging some random AI people here that may find this interesting enough to read. @fchollet @AndrewLampinen @ayazdanb @scychan_brains @kaixhin @LakeBrenden @RTomMcCoy @lambdaviking @MLStreetTalk @udayaghai 12/12