Adeel Razi

@adeelrazi.bsky.social

Computational Neuroscientist, NeuroAI, Causality. Monash, UCL, CIFAR. Lab: https://comp-neuro.github.io/

Why are there so many “foundation models” for brain if the goal is to have a single foundation model? I am often asked this question these days so I thought to put it out here. The short answer is that we are still in the exploration phase, not the consolidation phase. Theard 1/n 🧵👇

Wow. What a project 🤩 amid a stellar lineup of other possible collaborators including @smfleming.bsky.social @introspection.bsky.social @meganakpeters.bsky.social @adeelrazi.bsky.social - these projects sound so exciting! an incredible opportunity for early career researchers #neuroskyence

Ida Momennejad@neuroai.bsky.social · 3mo ago

📢Become an AI Sentience scholar. Apply by Tues April 28! @neddo.bsky.social & I are looking for a grad student/postdoc w expertise in survey studies/modeling & topic - 6 months part-time online collab - 8hrs/week June-Dec 2026 - Up to $5k stipend, $3k project costs neuromatch.io/ai-sentience... 1/n

🚨 JOB alert: 📢 We are looking for a PhD student to work on our international @wellcometrust.bsky.social project on information gathering in OCD and Schizophrenia! If you have a background in computational psychiatry / neuroimnaging and speak German, apply here: devcompsy.org/wp-content/u...

a penguin is sticking his head out of a hole next to a job application

ALT: a penguin is sticking his head out of a hole next to a job application

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"The Self-Evidencing Agent" - my new book - is out now with @mitpress.bsky.social Can be purchased, or just download the whole thing for free, via the 'Open Access' option. I'm grateful to @anilseth.bsky.social and Karl Friston for the generous endorsements. mitpress.mit.edu/978026255389...

The Self-Evidencing Agent

What is it to be a human individual, an agent? According to Jakob Hohwy, it is to “self-evidence,” to actively seek out sensory evidence for one&...

mitpress.mit.edu

📢Maths in the Brain Workshop 2026 in Melbourne. We will bring together researchers across Australia with a shared interest in understanding the brain from a quantitative perspective. This year's keynote is delivered by Professor James Cole, University College London. 1/4🧵

Thread 2: A way to see a stochastic differential equation is the equation below representing how X evolves in time. dW is a "stochastic increment" -- it is introducing "randomness". But a LOT changes because of that, and it is the reason the below representation is not actually a good one! (1/n)

\frac{dX(t)}{dt} = f(x) + g(x)\frac{dW(t)}{dt}

What do we actually mean when we say “generative model” in neuroimaging? The term is everywhere, in neuroscience and AI, but its meaning is often assumed rather than stated. In a new bioRxiv perspective, we asked how researchers really use and understand it. 🧵

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Our new paper, led by Brett Kagan and Valentina Baccetti, proposes a quantifiable, substrate-independent hierarchy of information processing to identify necessary conditions for agency, grounding agency in measurable dynamics rather than vague labels. arxiv.org/abs/2601.03498

A Quantifiable Information-Processing Hierarchy Provides a Necessary Condition for Detecting Agency

As intelligent systems are developed across diverse substrates - from machine learning models and neuromorphic hardware to in vitro neural cultures - understanding what gives a system agency has becom...

arxiv.org

1/ Why does RL struggle with social dilemmas? How can we ensure that AI learns to cooperate rather than compete? Introducing our new framework: MUPI (Embedded Universal Predictive Intelligence) which provides a theoretical basis for new cooperative solutions in RL. Preprint🧵👇 (Paper link below.)

Image of robots struggling with a social dilemma.

Can a Universal Basic Income (UBI) become feasible—even if AI fully automates existing jobs and creates no new ones? We derive a closed-form UBI threshold tied to AI capabilities that suggests it's potentially achievable by mid-century even under moderate AI growth assumptions:

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🧵Training binary or spiking neural networks is hard. Gradients vanish, surrogates are noisy, batchnorm is brittle. We propose a Bayesian approach based on KL divergence minimization—and it works. Paper: arxiv.org/abs/2505.17962 Work by James Walker & Moein Khajehnejad @neuro-ai.bsky.social 1/6

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks

We propose a Bayesian framework for training binary and spiking neural networks that achieves state-of-the-art performance without normalisation layers. Unlike commonly used surrogate gradient methods...

arxiv.org