Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1
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 is discussed.
Thomas Dalgaty, Eiji Kawasaki, M. de Prado et al.
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