A controversial theory suggests that perception, motor control, memory and other brain functions all depend on comparisons between ongoing actual experiences and the brain’s modeled expectations.
Neuralink and the Brain's Magical Future — Wait But Why
I knew the future would be shocking but this is a whole other level.
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli | Keith Doelling
This is some very cool work by some awesome colleagues Jean-Rémi King, and Teon Brooks! Seriously not enough good things can be said about how cool it is. You should enjoy it and play with it. And kudos to Meta for open sourcing it. At the same time, I'm already seeing posts about how the model will replace fMRI experiments as researchers will simulate how the brain "really works" instead of running costly experiments. I think this goes WELL beyond what its creators intend. We are already seeing that use of AI in science allows you to explore charted ideas more thoroughly and much more rapidly but slows us down in finding novel ideas (https://lnkd.in/eMR2akqt). At the same time, there is growing concern that LLM performance will collapse as they are increasingly trained on their own output (https://lnkd.in/eavgfyuY). Leaving neuroscience to AI simulations risks following the same fate, where we generate seemingly new findings without gaining new meaning. A mechanistic understanding of how the brain works (if that is still your goal) will be found at the margins, in errors and idiosyncrasies of neural function. What TRIBE provides is a super useful and cool instantiation of our current understanding on how and where neural activity is instantiated in the brain. But it won't help us make groundbreaking new findings of how neural circuits lead to cognition and behavior. Experiments on real human brains, may be costly, but they will always be necessary!
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli | Keith Doelling
This is some very cool work by some awesome colleagues Jean-Rémi King, and Teon Brooks! Seriously not enough good things can be said about how cool it is. You should enjoy it and play with it. And kudos to Meta for open sourcing it. At the same time, I'm already seeing posts about how the model will replace fMRI experiments as researchers will simulate how the brain "really works" instead of running costly experiments. I think this goes WELL beyond what its creators intend. We are already seeing that use of AI in science allows you to explore charted ideas more thoroughly and much more rapidly but slows us down in finding novel ideas (https://lnkd.in/eMR2akqt). At the same time, there is growing concern that LLM performance will collapse as they are increasingly trained on their own output (https://lnkd.in/eavgfyuY). Leaving neuroscience to AI simulations risks following the same fate, where we generate seemingly new findings without gaining new meaning. A mechanistic understanding of how the brain works (if that is still your goal) will be found at the margins, in errors and idiosyncrasies of neural function. What TRIBE provides is a super useful and cool instantiation of our current understanding on how and where neural activity is instantiated in the brain. But it won't help us make groundbreaking new findings of how neural circuits lead to cognition and behavior. Experiments on real human brains, may be costly, but they will always be necessary!
How are we writing the future of humanity?
Consciousness spinning
To Remember, the Brain Must Actively Forget | Quanta Magazine
Researchers find evidence that neural systems actively remove memories, suggesting that forgetting may be the default mode of the brain.
The brain in the machine: How AI could help explain how we think | IBM
Scientists are using large AI models to predict patterns of brain activity at scale, a development that researchers say is pushing neuroscience toward a new kind of digital imaging.
Brainchildren
Minds are complex artifacts, partly biological and partly social; only a unified, multidisciplinary approach will yield a realistic theory of how they came i...
What Is Intelligence? Lessons from AI About Evolution, Computing, and Minds | Blaise Agüera y Arcas
560 – Ecological Cognition II: Resonance
560 What the heck does the brain do in Ecological Dynamics, if it isn’t computing, processing, or representing? An introduction to the concept of Resonance.Download link
Our Future Is Being Devoured By Feral Thought Experiments
Here's a chance to start taking it back.
The science of consciousness does not need another theory, it needs a minimal unifying model
Abstract. This article discusses a hypothesis recently put forward by Kanai et al., according to which information generation constitutes a functional basi
Introducing TRIBE v2: A Predictive Foundation Model Trained to Understand How the Human Brain Processes Complex Stimuli
Understanding how the human brain processes the world around us is one of the greatest open challenges in neuroscience. Breakthroughs here could transform how we understand and treat neurological conditions affecting hundreds of millions of people — and improve AI systems by directly guiding their development from neuroscientific principles.
I don't understand this. 52 years later, why try and replicate the original? It's obviously not going to be the same study. The world has changed; cars, traffic, and people have changed. If one is interested in false memory, why not design the best study you can? What can you conclude meaningfully?
Andrew Gelman et al.
A plan to replicate the Loftus and Palmer (1974) experiment on false memories statmodeling.stat.columbia.edu/2026/10/04/a-plan-to-replicat…
📣📣📣 Preprint Alert! 📣📣📣 Do predictive processes shape brain representations during learning? We investigated the effect of Predictive Coding by training identical recurrent networks with different optimization procedures and then comparing them to human EEG under the same visual learning task.
