Learning probability distributions of sensory inputs with Monte Carlo predictive coding
by Gaspard Oliviers, Rafal Bogacz, Alexander Meulemans
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... Читать дальше...