Understanding Hierarchical Predictive Coding Models
Explore the concepts of Bayesian hierarchical models and hierarchical predictive coding, with a focus on predictive coding in the sensory cortex. Dive into the computational transformations and neural activity patterns associated with these models, providing insights into hierarchical information processing in the brain.
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Presentation Transcript
Hierarchical Predictive Coding right: forward chain left: backward chain red arrows: computational transformations
Predictive coding in sensory cortex a. Schematic predictive coding architecture, with PE units providing excitatory feedforward input, and P units providing inhibitory feedback b. Hypothesised neural activity in two populations of P and PE units, each representing a different hypothesis ( A and B , respectively). Stimulus A is predicted, and subsequently presented. Left panel illustrates schematic timecourse of activity, right panel provides integral over time (i.e., proxy of BOLD amplitude) c. Stimulus A is predicted, but B is presented. Activity is higher overall (particularly in PE units), but less unequivocal (in P units)
