Topic: sensory data

Navigation requires orienting oneself relative to landmarks in the environment, evaluating relevant sensory data, remembering goals, and convert all this information into motor commands that direct locomotion. I will present models, highly constrained by connectomic, physiological and behavioral data, for how these functions are accomplished in the fly brain.

In order to survive, animals constantly face decisions between competing interpretations for noisy and sparse sensory data. In statistics, multiple formal frameworks exist to tackle this problem, known as Model Selection (MS). However, despite the abundance of theoretical methods, the way in which MS is performed by the brain is poorly understood. Previous work showed that humans have a bias towards simpler models or interpretations of sensory data [1,2,3]. Human model selection strategies have been quantitatively described using a linear combination of the models Maximum Log-Likelihood ($L$)