Nirag Kadakia

The paper shows that Drosophila olfactory receptor neurons use proximity to a bifurcation point to efficiently encode temporal and intensity information from odor signals.

This study shows Drosophila use odor motion direction, detected via antennae signal correlations, to navigate odor plumes, similar to visual motion detection.

This paper develops a method for joint parameter and state inference in nonlinear neuron models using optimal control, chaotic synchronization, and state-space modeling, showing reliable parameter estimation from noisy data.

This study shows that Drosophila use both odor encounter frequency and intermittency to navigate turbulent plumes, with robust performance requiring both features but facing a tradeoff when used together.

This study shows that walking Drosophila use biased random walks within odor plumes, guided by the timing of odor encounters rather than reflexive behaviors.

The paper shows that Weber-Fechner gain scaling in Drosophila ORNs improves odor identity coding under varying intensity and mixing conditions.