Paul Masset

McGill University in early Autumn

Delighted to share that we are currently hiring for a tenure-track position (open rank) in Clinical Psychology in the Department of Psychology at McGill University. Come join a great department! Link to apply: mcgill.wd3.myworkdayjobs.com/en-US/mcgill....

This is one of the most outstanding examples of circuit understanding I've seen in a long time. The unification of theory and experiment is beautiful.

When Malcolm presented this in my lab, the audience was cheering at the end, and one person shouted (non-ironically) "You did it!"

Finally preprinted! TL;DR: thalamic head-direction neurons can shape their activity regardless of input. Big thanks to @apeyrache.bsky.social and master experimentalist @sskromne.bsky.social .

Stoked to see this paper finally out!

It answers two big questions: where visual objects are encoded in the brain, and how head-direction cells get oriented using visual landmarks.

Super fun collaboration with @mace-lab.bsky.social and Stuart Trenholm.

www.science.org/doi/10.1126/...

Assistant Professor (Neuroscience, Tenure Track) - Lethbridge (City), Alberta (CA) job with University of Lethbridge | 39049 Assistant Professor (tenure track) of the Department of Neuroscience at the University of Lethbridge, Alberta, Canada

For anyone on the neuroscience job market, our ads are up. We are looking for anyone working in systems neuroscience of animals to join us in southern Alberta 🇨🇦
#academicsky #neuroskyence #neuroethology #academicjobs #Canada
neurojobs.sfn.org/job/39049/as...

Come join us at University of Toronto. We're hiring a Professor of computational cognitive neuroscience.

#neuroAI #compneuro jobs.utoronto.ca/job/Toronto-...

Frontiers | Summary statistics of learning link changing neural representations to behavior How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical phy...

Since I'm back on BlueSky - with @frostedblakess.bsky.social and @cpehlevan.bsky.social we wrote a brief perspective on how ideas about summary statistics from the statistical physics of learning could potentially help inform neural data analysis... (1/2)

Mice navigate scent trails using predictive policies Animals actively sense their environment to extract features of interest to guide behaviors. For mammals, odors are prominent environmental features which are sampled by active modulation of sniffing ...

Thrilled to share this work, long time in the making! Carried through creatively by @siddjakes.bsky.social after initial design & piloting by @trackingskills.bsky.social, with help from @trackingactions.bsky.social. Modeling in collaboration with Massimo Vergassola & Nicola Rigolli.

Having fun facilitating this amazing group of young Profs at the #Neuro4Pros workshop with @kordinglab.bsky.social and @meganakpeters.bsky.social, @yaelniv.bsky.social, and Hannah Bayer.

Watch out world: this is the next generation of leaders!

compneurosci.com/Neuro4Pros/i...

For anyone interested in an academic job in 🇨🇦, we will soon be advertising an Assistant Prof position in Systems Neuroscience (rodents and other animal models) at ULethbridge. There are many pros and cons of working here, so feel free to DM me to find out more
#neuroskyence #academicsky

Tonic dopamine and biases in value learning linked through a biologically inspired reinforcement learning model Nature Communications - Accurate future predictions are essential for guiding behavior, and disruptions in this process are associated with psychiatric disorders. Here the authors show that changes...

My first paper with @naoshigeuchida.bsky.social is finally out in @natcomms.nature.com ! rdcu.be/eACGf

TL;DR: asymmetric learning rates can be induced by shifts in tonic dopamine giving rise to pessimistic/optimistic biases in agents or animals undergoing reinforcement learning .

🚨 I’m excited to say that my CIHR Project Grant was funded! My NHP lab is now full-speed-ahead, and I’m hiring experimentalists (postdoc, PhD student, and/or a tech/manager). We’ll do multi-region ephys during reaching/grasping in macaques, with behavioral and spinal perturbations.

Reconciling flexibility and efficiency: medial entorhinal cortex represents a compositional cognitive map Nature Communications - How the brain creates compositional cognitive maps that support both flexible and efficient planning remains poorly understood. Here, authors propose a...

New paper with @nathanieldaw.bsky.social in Nature Communications: an RL model that builds a successor map compositionally. The new model plans as well as the best models, and it links components of the map used for planning to neural codes in the medial entorhinal cortex.
rdcu.be/eAofi

Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics Bukwich and Campbell et al. show that mice integrate elapsed time and reward intake, scaled by a latent patience variable, to decide when to leave virtual “patches.” Frontal cortex ramping activity ma...

Our paper on foraging is now published in Neuron! Read it here:

www.cell.com/neuron/fullt...

This project was co-led by Michael Bukwich (not on Bluesky) and me, with major contributions from all co-authors. Huge thanks to the whole team!

A landmark volume, The Handbook of Dopamine, is now online:
www.sciencedirect.com/handbook/han...

Big kudos to the editors, Stephanie Cragg and Mark Walton, for putting this together.

A neural manifold view of the brain - Nature Neuroscience Recent advances in neuroscience have revealed how neural population activity underlying behavior can be well described by topological objects called neural manifolds. Understanding how nature, nurture...

Check out our new review/perspective (w/ @juangallego.bsky.social & Devika Narain) on neural manifolds in the brain! It was a lot of fun to think through these ideas over the past couple of years, and I'm excited it's finally out in the world!

🔗: www.nature.com/articles/s41...
📄: rdcu.be/ex8hW

👨‍💻 Open PI position in our institute 👨‍💻 !! If you are an expert in Computational Neuroscience and want to start your lab in Bordeaux, contact us !
www.fens.org/careers/job-...
@neuromagendie.bsky.social
@neurobordeaux.bsky.social

Discovering cognitive strategies with tiny recurrent neural networks - Nature Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the underlying cog...

Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s415...

Excited to share the first 2(!) preprints from the Kebschull Lab. The amazing @hyopilkim.bsky.social developed MAPseq2 & POINTseq and used them to map the dopaminergic cells of VTA and SNc. tl;dr MAPseq2=10x cheaper, 4x better; POINTseq=easy cell type specific barcoding; DA neurons=very cool.

Thrilled to announce I'll be starting my own neuro-theory lab, as an Assistant Professor at @yaleneuro.bsky.social @wutsaiyale.bsky.social this Fall!

My group will study offline learning in the sleeping brain: how neural activity self-organizes during sleep and the computations it performs. 🧵

A flexible hippocampal population code for experience relative to reward - Nature Neuroscience Sosa et al. find that hippocampal neural activity in mice encodes both environmental location and experience relative to rewards, spanning distances far from reward, through parallel and flexible popu...

It's officially published!! In my main postdoc work with @markplitt.bsky.social and @lgiocomo.bsky.social, we found that the hippocampus simultaneously encodes an animal's spatial position and its experience relative to reward in parallel population codes. 🧵

www.nature.com/articles/s41...

Our work, out at Cell, shows that the brain’s dopamine signals teach each individual a unique learning trajectory. Collaborative experiment-theory effort, led by Sam Liebana in the lab. The first experiment my lab started just shy of 6y ago & v excited to see it out: www.cell.com/cell/fulltex...

And one more again at McGill in artificial intelligence in Psychiatry

can-acn.org/assistant-pr...

Manitokan are images set up where one can bring a gift or receive a gift. 1930s Rocky Boy Reservation, Montana, Montana State University photograph. Colourized with AI

Preprint Alert 🚀

Multi-agent reinforcement learning (MARL) often assumes that agents know when other agents cooperate with them. But for humans, this isn’t always the case. For example, plains indigenous groups used to leave resources for others to use at effigies called Manitokan.
1/8

To conclude our study establishes a new paradigm to understand the functional role of prediction error computation in dopaminergic neurons and opens new avenues to develop mechanistic explanations for deficits in intertemporal choice in disease and inspire the design of new RL algorithms.

By recording single neurons across both tasks, we show that the discount factor at the single neuron level is correlated across tasks suggesting that discounting is a cell-specific property, constraining implementations of dopamine-based multi-timescale RL.

Next, we show that heterogeneity in ramping activity across dopamine neurons when mice approach a reward in a 1-D VR track can also be understood as a signature of diverse discount factors across neurons.

First, we quantify the discount factor by measuring the relative response to cues predicting rewards at different delays and show that reward timing information can be decoded from the vectorized prediction error.

Next, we show in two behavioral tasks that single dopamine neurons exhibit a diversity of discount factors.

We first explore four computational advantages of artificial multi-timescale RL agents:
- disentangling reward timing and reward magnitude
- learn values with non-exponential temporal discounts
- infer temporal information before convergence
- implement a state-dependent discount factor