Author

Thomas Dalgaty

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Preprint Sep 2026

Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 2

This report extends our previous work (Part 1), which introduced an energy-based model for learning and decision-making under uncertainty. The model leverages stochastic Langevin dynamics to continuously evolve approximate probability distributions over neuron states and model weights. However, as noted in Part 1 and c...

Thomas Dalgaty, Eiji Kawasaki, M. de Prado et al. · 0 citations

#artificial intelligence Preprint Sep 2026

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

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 abs...

Thomas Dalgaty, Eiji Kawasaki, M. de Prado et al. · 1 citation