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Life sciences · Preprint
arXiv · September 10, 2026
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This is a theoretical position paper proposing that biological neural noise and synaptic variability perform Bayesian inference via stochastic sampling, and that analogue in-memory computing hardware could replicate this mechanism for scalable probabilistic inference. No empirical validation, performance metrics, or comparative data are provided; the work raises a conceptual framework rather than testing or validating it.
Preprint.
Proposes that noisy neural and synaptic dynamics perform inference and learning via stochastic sampling from an internal energy function Suggests analogue in-memory computing hardware naturally parallels intrinsic noise in biological systems for probabilistic inference Connects predictive coding networks to uncertainty quantification via Markov chain Monte Carlo sampling
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A theoretical framework proposing biological parallels between neural noise and hardware noise for probabilistic inference; no empirical validation, clinical outcomes, or comparative performance data presented.
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Learning and decision-making in animals are often modeled as Bayesian processes, where sensory evidence is integrated with prior beliefs to guide behavior in the face of uncertainty. But what are the inherent neural dynamics that give rise to this ability, and how could they be replicated in computing systems? This abstract discusses a biologically grounded framework in which noisy neural and synaptic dynamics perform inference and learning via stochastic sampling from an internal energy function, capturing uncertainty over latent states and model parameters through neural and synaptic variability, respectively. This enables approaches such as predictive coding networks to account for epistemic uncertainty via Markov chain Monte Carlo sampling. Drawing a parallel between intrinsic noise in biological systems and electrical noise in emerging probabilistic analogue memory technologies, we highlight how analogue in-memory computing hardware naturally emerges as the solution for massively scalable and energy-efficient probabilistic inference.
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