I study the neural computations of sensorimotor networks, with a focus on odor coding and olfactory navigation in the fruit fly Drosophila. Surprisingly, the fruit fly has a ton in common in with humans in its genetic content and the way its brain (in particular, the olfactory circuit) is wired. Its tiny size makes it easy to design specific experiments that elucidate neural computations of sensorimotor tasks in more complex animals.

My recent work has focused on navigational decisions that animals use to find their way to the source of an odor (as in bees finding a flower). Insects can do this even in the dark, and it is not an easy task, since odor environments (called "plumes") in nature are wildly complex: they fluctuate rapidly and don't have a smooth structure that could be navigated easily. One of my recent works, published in eLife in 2020, found that flies use the timing of odor cues to bias navigational decisions, and they use this timing in different ways depending on which motor action they want to take -- such as turning, stopping. This was a significant discovery because we found that in turbulent odor plumes, i) flies navigated using a fundamental stochastic strategy, and ii) the intensity of the odor was largely ignored in shaping navigational decisions. My contribution to this project was designing models and navigational algorithms, and fitting these to data. The experiments were done by fellow postdoc Mahmut Demir.

More reccently, I've shown that flies can detect which direct odor signals are moving -- even when the odors are delivered without any sort of wind or airflow. This is possible using a technique called optogenetics, which allows us to use light as a sort of "virtual" odor signal. Interestingly, we found that flies detect the direction of odors using a computational algorithm that is equivalent to the way we detect motion visually. This was the first demonstration of this algorithm in the olfactory circuit.

I'm continuing to work on navigation problems and odor coding projects using a combination of experimentation, theory, and computational techniques. See my publications for more info.






Nirag Kadakia
Email: nirag.kadakia@yale.edu

Yale, Dept of MCDB


New Haven, CT 06511