Learning probability distributions of sensory inputs with Monte Carlo predictive coding.

ABSTRACT: It has been suggested that the brain employs probabilistic generative models to optimally interpret sensory information. This hypothesis has been formalised in distinct frameworks, focusing on explaining separate phenomena. On one hand, classic predictive coding theory proposed how the probabilistic models can be learned by networks of neurons employing local synaptic plasticity. On the other hand, neural sampling theories have demonstrated how stochastic dynamics enable neural circuits to represent the posterior distributions of latent states of the environment. These frameworks were brought together by variational filtering that introduced neural sampling to predictive coding. Here, we consider a variant of variational filtering for static inputs, to which we refer as Monte Carlo predict

SUBMITTER: Oliviers G 

PROVIDER: S-EPMC11524488 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature