Genuinely very deep and interesting work, congratulations and thank you for your excellent contributions

Our work with Karl Friston on Self-Orthogonalizing Attractor Neural Networks is now out in Neurocomputing!

What does this theoretical model mean for our understanding of the brain? I’ve mapped out the key neuroscience implications below.

Read the thread for a neuroscience walk-through ↓

Attractor dynamics are a hallmark of brain function.
But are they just epiphenomena?

Starting from the free energy principle, we show that attractors can actually implement Bayesian priors in self-organizing networks, linking local neural dynamics directly to macro-scale probabilistic inference.

My DMs are open if you have any questions! Always happy to chat all things FEP adjacent

The free energy principle—a precis - Dialectical Systems 1. Introduction Here, we present the free energy principle (FEP) in a simple manner to a broad audience. Alongside other cornerstones of mathematical physics—for instance, variational principles such ...

I wouldn't recommend starting with the philosophical literature on the FEP, which is often problematic and also outdated. I'd recommend instead this precis (www.dialecticalsystems.eu/contribution...) as well as Sanjeev Namjoshi's new textbook on active inference (mitpress.mit.edu/978026205095...)

Great question! I would propose something like this: www.cambridge.org/core/journal...

old man yells at claude

A copy of "Fundamentals of Active Inference: Principles, Algorithms, and Applications of the Free Energy Principle for Engineers" by Sanjeev Namjoshi on a plain background.

Sanjeev Namjoshi's textbook "Fundamentals of Active Inference" provides a comprehensive, up-to-date introduction to active inference and the free energy principle for an engineering-focused audience: mitpress.mit.edu/978026205095...

I wish you were right about that, but there are still surprising numbers of radical enactive and ecological psychology types out there, in this year of our Lord 2026, who seem to strongly believe this...

What is the brain for? Active inference is widely discussed as a unifying framework for understanding brain function, yet its empirical status remains debated. Our review identifies core predictions across the action-perception cycle and evaluates their empirical support: osf.io/preprints/ps...

This is fire!!!

I really think at its heart philosophy is one giant battle, taking place over many eras and nations, between people who are basically pleasant bureaucrats and people who are sexy murder poets, and it’s both super important and super boring that the pleasant bureaucrats must win.

I am frustrated by the anti-AI obsession on this place. I understand people are annoyed by AI being imposed on us for trivial things and by the AI uber alles discourse but it really feels like older people complaining about a new technology.

Awesome work!

Self-orthogonalizing attractor neural networks emerging from the free energy principle Attractor dynamics are a hallmark of many complex systems, including the brain. Understanding how such self-organizing dynamics emerge from first principles is crucial for advancing our understanding ...

As many of you know, I’ve been fascinated by brain attractor dynamics lately.

Thrilled to share a new preprint on their link to orthogonal neural representations, co-authored with Karl Friston:
arxiv.org/abs/2505.22749
- with implications for both neuroscience & AI!

First in a series - stay tuned!

#1000 Karl Friston: The Free Energy Principle and Active Inference: From Physics to Mind YouTube video by The Dissenter

In episode 1000, I talk with Dr. Karl Friston about the Free Energy Principle and active inference, from #Physics to mind. #CognitiveScience #Science

youtu.be/2BzmKnDtCCI

Honored to speak at Ottawa about how Canada can lead in #NeuroAI. With world-class talent, trusted institutions, & sustainable infrastructure, we can build a federated approach to AI that protects mental health & strengthens our society. Thanks @braincanada.bsky.social for the invitation!

New preprint with super @manuelbaltieri.bsky.social !

Mathematical approaches to the study of agents

osf.io/preprints/ps...

Extropic | Home Building thermodynamic computing hardware that is radically more energy efficient than GPUs.

Beff shipped:
extropic.ai
A new era begins

Why Intelligence Can't Get Too Large (Karl Friston) YouTube video by Machine Learning Street Talk

Karl Friston in #mlst
Philosophy done right! So many references, obviously @drmichaellevin.bsky.social mentioned #academicsky #philosophy #neuroscience #strangeloop

youtu.be/PNYWi996Beg

Karl Friston & Mark Solms: Is it Possible to Engineer Artificial Consciousness? Spotify video

Super interesting, thought-provoking conversation between Mark Solms and Karl Friston open.spotify.com/episode/151a...

What drives behavior in living organisms? And how can we design artificial agents that learn interactively?

📢 To address these, the Sensorimotor AI Journal Club is launching the "RL Debate Series"👇

w/ @elisennesh.bsky.social, @noreward4u.bsky.social, @tommasosalvatori.bsky.social

🧵[1/5]

🧠🤖🧠📈

Sorry to hear about your negative experience! My pleasure, don't hesitate to write me if you have any questions or want to discuss specific points :)

Yes! While Warren and myself have our disagreements, I like his work on PCT. IMO all these approaches are complementary and play together nicely. Along with friends (namely @adw.bsky.social who bravely led the project), we penned this integrative review. Hope it's of interest:
osf.io/preprints/ps...

Variational ecology and the physics of sentient systems This paper addresses the challenges faced by multiscale formulations of the variational (free energy) approach to dynamics that obtain for large-scale…

There’s a lot of cool work on multi-scale applications of the FEP. See, e.g.:
- www.sciencedirect.com/science/arti...
- www.sciencedirect.com/science/arti...
- arxiv.org/abs/1906.10184, especially the chapter on States, particles, and fluctuations

3. Your point about top-down causation is key. IMO one of the most interesting aspects of multi-scale formulations of active inference is precisely how it handles multi-scale system dynamics, cashing out top-down influence in terms of constraints on system dynamics in a non-reductionist way

Dynamic Markov Blanket Detection for Macroscopic Physics Discovery The free energy principle (FEP), along with the associated constructs of Markov blankets and ontological potentials, have recently been presented as the core components of a generalized modeling metho...

2. Not much work has been done on active inference and the neural code. The key departure from RL is that active inference uses an alternative objective function (the free energy functional), which you can read as an "ontological potential function" specifying object type (arxiv.org/abs/2502.21217)

Great questions!
1. IMO active inference falls under the rubric of NeuroAI, (although I'd describe myself as a non-realist about these types of physics-inspired models, and as such I’d say the FEP isn’t a literal description of the brain, so it depends on the scope of NeuroAI, as your define it)

Filling the gaps in active inference Here we discuss key gaps in SOTA applications of active inference in AI - and how Noumenal Labs is working to fill them.

Love a good Feyerabendian sandbox. I'd argue that they're very closely related (and indeed, that the difference is often overblown by both proponents and critics), but they're also importantly distinct. We wrote a post on this that I hope you'll find interesting: www.noumenal.ai/post/filling...

🤔 How can we study #consciousness between people, at the social level? 🧠✨ New #preprint co-led by Anne Monnier & Lena Adel: “Now is the Time: Operationalizing Generative Neurophenomenology through Interpersonal Methods” 🧵(1/3)