Meet Sofiya Zbaranska, a neuroscience PhD student in our lab developing brain-inspired machine learning algorithms.

#neuroscience #AI @compneurolab.bsky.social

Inspiring work by our collaborator, Dr. Sheena Josselyn. Well-deserved recognition!

A virtual rodent predicts the structure of neural activity across behaviours - Nature We built an artificial neural network to control a biomechanically realistic virtual rodent, which, when trained to imitate real rats, predicts neural activity and variability across na...

Still one of my favourite papers from Google DeepMind www.nature.com/articles/s41...

To accompany my textbook (Computational Foundations of Cognitive Neuroscience) and the class I taught this semester, I'm open-sourcing my lectures slides:
gershmanlab.com/lectures.html
I'll continue to update these as I improve them.

A mathematical theory of evolution for self-designing AIs As artificial intelligence systems (AIs) become increasingly produced by recursive self-improvement, a form of evolution may emerge, with the traits of AI systems shaped by the success of earlier AIs ...

New preprint, on a very different topic: a mathematical theory of evolution for self-designing AI.

AI is increasingly designed by AI. What systems might emerge after generations of self-designing AIs competing for computing resources? ↓

arxiv.org/abs/2604.05142

Another successful thesis defence for @compneurolab.bsky.social. Stay tuned for the paper.

#neuroAI #compneuro @utoronto.ca

Great opportunity

Congrats, team. Proud to be part of this amazing community.

ARNI: Memory Driven Learning with Kim Stachenfeld YouTube video by Columbia Engineering

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

Brains, minds and machines: A new algorithm for decoding intelligence Algorithms developed by Professor Brokoslaw Laschowski (MIE) and his lab are being used to decode the brain and interface with machines

News story about the paper news.engineering.utoronto.ca/brains-minds...

Awesome work

Inspiring talk by Dr. Sergey Stavisky on brain-computer interfaces. Thanks for visiting University of Toronto.

#neuroAI #compneuro @utoronto.ca @uoftcompsci.bsky.social @vectorinstitute.ai

How does perceived risk shape adaptation and learning?

Our new work reveals that locomotor adaptation proactively navigates a "fall risk landscape" , modulating learning parameters that dictate optimality to prioritize safety.

(work with Inseung Kang and Kanishka Mitra)

doi.org/10.64898/202...

Representation Biases: Variance Is Not Always a Good Proxy for Importance A central approach in neuroscience is to analyze neural representations as a means to understand a system's function, through the use of methods like principal component analysis, regression, and repr...

Pleased to share that our paper "Representation Biases: Variance is Not Always a Good Proxy for Importance" is now out as Theory/New Concepts paper in eNeuro!
www.eneuro.org/content/13/3... 1/

JEDI: Jointly Embedded Inference of Neural Dynamics Animal brains flexibly and efficiently achieve many behavioral tasks with a single neural network. A core goal in modern neuroscience is to map the mechanisms of the brain's flexibility onto the dynam...

New paper! We introduce JEDI, Jointly Embedded Dynamics Inference for neural dynamics.
arxiv.org/abs/2603.10489. JEDI flexibly infers dynamical principles (across behaviors/contexts) from neural population data through RNNs constrained at single-neuron resolution to reproduce that data.

Follow @compneurolab.bsky.social for updates.

Sneak peek inside the Computational Neuroscience Lab.

#neuroAI #compneuro @utoronto.ca @uoftcompsci.bsky.social @vectorinstitute.ai

Absolutely. Nearly half my students over the past four years have been from Ukraine. Their resilience and commitment to education despite unimaginable challenges speak volumes about their character. Truly inspiring!

Congrats @mattperich.bsky.social and team

Dr. Sheena Josselyn awarded the 2025 Margolese National Brain Disorders Prize YouTube video by Faculty of Medicine at UBC

Congrats to our collaborator Dr. Sheena Josselyn for being recognized for her seminal research on memory encoding in the brain.

#neuroscience www.youtube.com/watch?v=jmpT...

Compact deep neural network models of the visual cortex - Nature Parsimonious deep neural network models can be used for prediction of visual neuron responses.

Nature research paper: Compact deep neural network models of the visual cortex

go.nature.com/3OKRXZU

I am totally pumped about this new work . "Task-trained RNNs" are a powerful and influential framework in neuroscience, but have lacked a firm theoretical footing. This work provides one, and makes direct contact with the classical theory of random RNNs:
www.biorxiv.org/content/10.6...

Great work @cpehlevan.bsky.social and team

Building neuroscience-inspired AI. Follow @compneurolab.bsky.social for updates.

#neuroAI #compneuro @utoronto.ca

Vectorized instructive signals in cortical dendrites - Nature Mice learning a neurofeedback brain–computer interface task show neuron-specific teaching signals in cortical dendrites, consistent with a vectorized solution for credit assignment in the brain.

This paper on how the brain may do gradient descent is very cool: www.nature.com/articles/s41...

Can we predict a thought before it happens?

To know what one neuron will do next, you have to know what the entire brain is doing right now.

In our latest @kempnerinstitute.bsky.social Deeper Learning blog, @duranrin.bsky.social introduces POCO, a tool paving the way for adaptive neurotechnology.

Interesting

Congrats @dlevenstein.bsky.social

U of T Engineering News Brains, minds & machines: A new algorithm for decoding intelligence [photo of Laschowski in a lab with a machine and a whiteboard with formulas]

🧠 Imagine being able to control machines by thinking.

@drlaschowski.bsky.social (MIE) and his Computational Neuroscience Lab are working to make it possible. They've developed a new algorithm that could make brain decoding more accurate and efficient.

Read the story: uofteng.ca/25h7c8

Our paper is out in @natneuro.nature.com!

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

We develop a geometric theory of how neural populations support generalization across many tasks.

@zuckermanbrain.bsky.social
@flatironinstitute.org
@kempnerinstitute.bsky.social

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