Combined antigenic and genetic analysis shows that different strains of the human influenza virus display dramatically different rates of antigenic drift, and that these differences have a significant impact on the number of new infections in each flu season.

A mathematical model built around the assumption that the desire to maintain internal homeostasis drives the behavior of animals, by affecting their learning processes, can explain many real-world behaviors, including some that might otherwise appear irrational.

A computational model for the control of chemotaxis in the Drosophila larva clarifies the link between the peripheral encoding of naturalistic olfactory stimuli and action selection.

The cellular and molecular substrates that underlie the programmed behavioral response to a gustatory sex pheromone have been identified.

The activation of Drosophila Or7a receptors guides aggregation and egg-laying behaviors towards both green leaf volatiles and a male-specific food-odor-induced pheromone.

Computational modeling suggests that feedback between striatal cholinergic neurons and spiny neurons dynamically adjusts learning rates to optimize behavior in a variable world.

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

Changes in physiological arousal – as revealed by pupil dilation and heart rate – shape our confidence in decisions about uncertain perceptual information.

The evolution of insecticide-avoidance behaviour can be used to generate new spatial repellents to keep malaria vectors out of homes.

A single framework of bounded evidence accumulation resolves the seemingly paradoxical effects of noise on accuracy, reaction time and confidence.