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A taxonomy of surprise definitions
Going beyond this technical analysis, we propose a taxonomy of surprise definitions and classify them into four conceptual categories based on the quantity they measure: (i) 'prediction surprise' measures a mismatch between a prediction and an observation; (ii) 'change-point detection surprise' measures the probability of a change in the environment; (iii) 'confidence-corrected surprise' explicitly accounts for the effect of confidence; and (iv) 'information gain surprise' measures the belief-update upon a new observation.
Fitting summary statistics of neural data with a differentiable spiking network simulator
Fitting network models to neural activity is an important tool in neuroscience.
Learning in Volatile Environments with the Bayes Factor Surprise
Surprise-based learning allows agents to rapidly adapt to non-stationary stochastic environments characterized by sudden changes.
