UC Santa Barbara

When confronted with an ever-changing and often perilous environment, how an organism behaves in response to uncertain and incomplete sensory information can be a matter of life and death. Besides the need to assess individual sensory signals accurately, sensory systems must also be able to integrate signals from multiple sensory modalities (e.g. visual, auditory, haptic), some of which may produce conflicting information. Through studying the insect brain of the Drosophila larva, we sought to unwrap the mathematical principles behind how animals process sensory signals to guide their behavior, with a focus on olfaction.

In my dissertation, we employ computational models to investigate how the Drosophila larva transduces odors through its olfactory sensory neurons and combines these cues with other sensory modalities. We obtain three important clues towards understanding the neural implementation of sensory systems: 1. Drosophila larvae are capable of computing and combining the variance of sensory inputs to organize orientation behavior, suggesting that even relatively simple nervous systems can achieve probabilistic inference. 2. Upon prolonged increasing excitation, olfactory sensory neurons can counterintuitively transition from a spiking state to a silent state called depolarization block, which preserves sparsity in the neural code. 3. The bifurcation of spiking and silent states in olfactory sensory neurons driven by depolarization block allows Drosophila larvae to encode and discriminate different odors.