Inferring Inference

The paper proposes a mathematical framework to infer canonical distributed computations from large‑scale neural activity. It combines normative assumptions that the brain learns a structured internal model of its environment with an algorithmic view that inference is a nonlinear message‑passing process on a graph‑structured model. Using time‑series neural data from a perceptual inference task, the framework identifies latent variables, their neural representations, the interactions defining the brain’s internal model, and the message functions governing inference, demonstrating its feasibility on simulated neural recordings.

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