I’m grateful to share that our paper has been published in Nature. This work formed the core of my PhD research at McGill University.

We show that hippocampal neurons that initially encode reward progressively reorganize to reflect predictive representations of reward during learning.

Our paper on the "Oneirogen hypothesis" is now up in its revised form on eLife!

This is the hypothesis that psychedelics induce a dream-like state, which we show via modelling could explain a variety of perceptual and learning effects from such drugs.

elifesciences.org/reviewed-pre...

🧠📈 🧪

Are you thinking about doing neuroscience outreach but want to make it more exciting or hands on?

Check out RetINaBox! (A collab led by the Trenholm lab)

We tried to bring the experience of experimental neuroscience to a classroom setting:

www.eneuro.org/content/13/1...

#neuroscience 🧪

🚨 New preprint alert!

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We propose a theory of how learning curriculum affects generalization through neural population dimensionality. Learning curriculum is a determining factor of neural dimensionality - where you start from determines where you end up.
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A 🧵:

tinyurl.com/yr8tawj3

🧠🤖 Computational Neuroscience summer school IMBIZO in Cape Town is open for applications again!
 
💻🧬 3 weeks of intense coursework & projects with support from expert tutors and faculty
 
📈Apply until July 1st!

🔗https://imbizo.africa/

Want to spend 3 weeks in South Africa for an unforgettable summer school experience? Imbizo 2026 (imbizo.africa) student applications are OPEN! Lectures, new friends, and Noordhoek beach await. Apply by July 1!

More info and apply: imbizo.africa/apply/

#Imbizo2026 #CompNeuro

Top-down feedback is ubiquitous in the brain and computationally distinct, but rarely modeled in deep neural networks. What happens when a DNN has biologically-inspired top-down feedback? 🧠📈

Our new paper explores this: elifesciences.org/reviewed-pre...

Excited to share our new pre-print on bioRxiv, in which we reveal that feedback-driven motor corrections are encoded in small, previously missed neural signals.

Are you training self-supervised/foundation models, and worried if they are learning good representations? We got you covered! 💪
🦖Introducing Reptrix, a #Python library to evaluate representation quality metrics for neural nets: github.com/BARL-SSL/rep...
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#DeepLearning

At #Cosyne2025? Come by my poster today (3-047) to hear how sequential predictive learning produces a continuous neural manifold with the ability to generate replay during sleep, and spatial representations that "sweep" ahead to future positions. All from sensory information alone!

Very excited for the upcoming Cosyne in Montreal! I’ll be presenting my poster [2-126] Brain-like neural dynamics for behavioral control develop through reinforcement learning, on the Friday session at 13:15.

Feel free to drop by! The related pre-print is also out:
www.biorxiv.org/content/10.1...

📢 We have a new #NeuroAI postdoctoral position in the lab!

If you have a strong background in #NeuroAI or computational neuroscience, I’d love to hear from you.

(Repost please)

🧠📈🤖

The problem with current SSL? It's hungry. Very hungry. 🤖

Training time: Weeks
Dataset size: Millions of images
Compute costs: 💸💸💸

Our #NeurIPS2024 poster makes SSL pipelines 2x faster and achieves similar accuracy at 50% pretraining cost! 💪🏼✨
🧵 1/8

Why does #compneuro need new learning methods? ANN models are usually trained with Gradient Descent (GD), which violates biological realities like Dale’s law and log-normal weights. Here we describe a superior learning algorithm for comp neuro: Exponentiated Gradients (EG)! 1/12 #neuroscience 🧪

1. Hi all: I’m here to advertise our new preprint: www.biorxiv.org/content/10.1..., with Fabrice Normandin, @tyrellturing.bsky.social, and @glajoie.bsky.social!