Light-seeking strategies in Zebrafish larvae are dissected using a virtual-reality assay, and these data are used to establish minimal stochastic and neural-circuits models that quantitatively capture this behavior.
A high-throughput behavioral paradigm and computational modeling are used to decompose olfactory navigation in walking Drosophila melanogaster into a set of quantitative relationships between sensory input and motor output.
For perceptual inference, human observers do not estimate sensory uncertainty instantaneously from the current sensory signals alone, but by combining past and current sensory inputs consistent with a Bayesian learner.
Analysis and modeling of group behavior of adult zebrafish shows that a specialized social interaction mechanism increases foraging efficiency and equality within groups, under a variety of environmental conditions.
Quantitative analysis of behavior coupled with computational modeling reveal the set of circuit-level principles that underlie cerebellar-dependent motor learning in smooth pursuit eye movements of monkeys across timescales.
In the Arabidopsis epidermis, the internal mechanical stress of a cell competes with the external stress to control microtubule behavior, providing a framework to understand the mechanical feedbacks that underlie plant morphogenesis.