Hi @pepedocs
I think the best place to start if you want a mathematical description of Monty is this preprint: [2507.04494] Thousand-Brains Systems: Sensorimotor Intelligence for Rapid, Robust Learning and Inference It formalizes Monty’s learning and inference algorithm in detail.
If you prefer a slightly higher-level description, the “How Monty Works “ section in our documentation may be a good place to start. In particular, the page on the evidence-based learning module (which is the current default).
You could call the learning “Hebbian-like” as observed locations and features are associated instantaneously. We also have object models that take into account the frequency of observing features at certain locations and hence store the most consistently observed features at locations (more details in the page on object models).
It’s not really Bayesian network inference, as we have no global normalization, but instead rely on local evidence accumulation to make it more biologically plausible.
I hope this helps!
- Viviane