arXiv Machine Learning By Moumita Das, Dipanjan Ray, Sourabh Bhattacharya

Recursive Gaussian Processes and the Bayesian Brain

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arXiv:2608. 00503v1 Announce Type: cross Abstract: Predictive coding offers a powerful framework for cortical computation, yet scalable implementations that respect both Bayesian exactness and neurobiological constraints remain scarce.

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arXiv Machine Learning
Sep 11

Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1

The article explores how biological learning and decision-making, often modeled as Bayesian processes, can be replicated in computing systems by leveraging noisy neural and synaptic dynamics for stochastic sampling. It proposes a biologically grounded framework where internal energy functions capture uncertainty over latent states and model parameters, enabling predictive coding networks to perform Markov chain Monte Carlo sampling. By drawing parallels between intrinsic biological noise and electrical noise in emerging probabilistic analogue memory technologies, the authors argue that analogue in‑memory computing hardware offers a massively scalable and energy‑efficient solution for probabilistic inference.

By Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado, Devendra Vyas, Tommaso Salvatori