Recursive Gaussian Processes and the Bayesian Brain
Predictive coding offers a powerful framework for cortical computation, yet scalable implementations that respect both Bayesian exactness and neurobiological constraints remain scarce. We bridge this gap by formally connecting predictive coding to Recursive Gaussian Processes (RGPs). RGPs employ a single Gaussian process \( g(t, \cdot) \) indexed by layer index and input value, preventing the representational collapse of standard deep Gaussian processes while allowing learnable cross-layer dependence via \( r_{1g} \). We demonstrate that RGPs intrinsically implement hierarchical Bayesian inference, uncertainty propagation, and precision-weighted prediction error. Critically, we map RGP co...
| Authors | Moumita Das, Dipanjan Ray, Sourabh Bhattacharya |
|---|---|
| Primary category | q-bio.NC |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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