A Theoretical Framework for Inference and Learning in Predictive Coding Networks

Understand

Predictive coding (PC) is an influential theory in computational neuroscience, which argues that the cortex forms unsupervised world models by implementing a hierarchical process of prediction error minimization.

• PC networks (PCNs) are trained in two phases.
• First, neural activities are updated to optimize the network's response to external stimuli.
• Second, synaptic weights are updated to consolidate this change in activity -- an algorithm called \emph{prospective configuration}.