Data from: The representation of prediction error in auditory cortex

To survive, organisms must extract information from the past that is
relevant for their future. How this process is expressed at the neural
level remains unclear. We address this problem by developing a novel
approach from first principles. We show here how to generate
low-complexity representations of the past that produce optimal
predictions of future events. We then illustrate this framework by
studying the coding of ‘oddball’ sequences in auditory cortex. We find
that for many neurons in primary auditory cortex, trial-by-trial
fluctuations of neuronal responses correlate with the theoretical
prediction error calculated from the short-term past of the stimulation
sequence, under constraints on the complexity of the representation of
this past sequence. In some neurons, the effect of prediction error
accounted for more than 50% of response variability. Reliable predictions
often depended on a representation of the sequence of the last ten or more
stimuli, although the representation kept only few details of that
sequence.