Pietro Berkes

TL;DR: The authors used a Bayesian model of sensory cortical processing to compare stimulus-evoked and spontaneous neural activities to inferences and prior expectations in an internal model and predicted that they should match if the model is statistically optimal.

TL;DR: It is argued that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly.

TL;DR: This study investigates temporal slowness as a learning principle for receptive fields using slow feature analysis, a new algorithm to determine functions that extract slowly varying signals from the input data.

TL;DR: A theory in which the cortex performs probabilistic inference such that population activity patterns represent statistical samples from the inferred probability distribution is developed, predicting that perceptual uncertainty is directly encoded by the variability, rather than the average, of cortical responses.

TL;DR: This work shows how to use knowledge of the psychophysical task to make testable predictions for the influence of feedback signals on early sensory representations and demonstrates a normative way to integrate sensory and cognitive components into physiologically testable models of perceptual decision-making.