Topic: PredNet

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding in

Our visual system continually adapts to its sensory environment. As a result, both the encoding accuracy of sensory information and perceptual behavior change according to the recent history of sensory input. Here we tested the hypothesis that adaptation to the recent history of sensory input optimally prepares the perceptual system for the future, i.e. establishes efficient sensory representations for the next expected sensory input. We first quantitatively characterized the changes in neural encoding accuracy induced by adaptation with a psychophysical discrimination experiment. We measu