Recursive Gaussian Processes Crack the Bayesian Brain Code
New RGP model prevents representational collapse while mimicking cortical microcircuits...
Researchers Moumita Das, Dipanjan Ray, and Sourabh Bhattacharya have published a paper on arXiv (2608.00503) connecting Recursive Gaussian Processes (RGPs) to the brain's Bayesian inference mechanisms. RGPs use a single Gaussian process g(t, ·) indexed by layer and input value, which prevents representational collapse that plagues standard deep Gaussian processes. The model introduces learnable cross-layer dependence via r_{1g}, enabling hierarchical Bayesian inference, uncertainty propagation, and precision-weighted prediction error.
The paper maps RGP components—shared GP, spike-and-slab variable selection, and MCMC dynamics—onto the canonical cortical microcircuit, providing a neurobiological substrate for these computations. Drawing on the free energy principle, the authors demonstrate that RGP inference minimizes variational free energy, formally linking Bayesian mechanics to neuronal dynamics. The framework generates testable predictions for laminar-specific dynamics and spectral asymmetries between feedforward and feedback processing, offering a principled computational tool and candidate model for the brain's predictive machinery.
- RGPs prevent representational collapse in deep Gaussian processes via shared GP indexed by layer and input
- Maps RGP components to cortical microcircuits, providing neurobiological substrate for Bayesian computations
- RGP inference minimizes variational free energy, linking Bayesian mechanics to neuronal dynamics
Why It Matters
Unifies machine learning and neuroscience, offering testable predictions for cortical function and scalable Bayesian inference.