Computational modeling offers an explanation for why animals learn more quickly or slowly when their environment becomes more variable or stable.

Changes to sensory predictions are encoded by beta oscillations, surprise due to prediction violations by gamma oscillations, and alpha oscillations may have a role in controlling the precision of predictions.

The P300, an electroencephalography (EEG) component known to be evoked by surprising events, predicts learning in a bidirectional manner that depends critically on the surrounding statistical context.

Brain responses in humans demonstrate that the analysis of crowded acoustic scenes is based on a mechanism that infers the predictability of sensory information and up-regulates processing for reliable signals.

Dendrites combine the inputs that they receive from other neurons using calculations that have been optimized for those particular input patterns.

Activation of the subthalamic nucleus (STN) pauses or disrupts behavior, while STN inhibition reduces the disruptive effects of surprise, indicating that STN activation is both sufficient and necessary for behavioral inhibition.

Recordings from serotonin-producing neurons in the brain reveal that these neurons are highly activated by sudden changes in previously familiar environments, potentially explaining why serotonin is important for learning to adapt to such changes.

A combination of genetic, anatomical and physiological techniques has revealed that the lateral horn, a region of the brain involved in olfaction in flies, has many more types of neurons than expected.