Humans of HBI

In our bodies, the nervous system is the communication network, handling messages and quick decisions, while the immune system is the defense team, protecting us from invaders like bacteria. These two systems are constantly communicating and influencing the functions of each other, a concept termed “neuroimmune interactions”. In my work, I investigate the role of neuroimmune interactions in fighting bacterial infections in the inner ear, where our senses of hearing and balance originate.

I work in the admin office for the Neurobiology Department. In my job I wear a couple of hats. I do HR/onboarding, event planning/coordinating, and I assist with other general administrative functions of the department, including managing the communal rooms and department-wide communications.

We’ve evolved to avoid harm in dynamic and often stressful environments by rapidly altering the performance of key bodily functions. One important adaptation is in our hearing, where the auditory system must become more sensitive in times of need while also preventing itself from failure or being damaged. This is because sound, the very signal our ears detect, can be damaging when it is too loud for too long! My work aims to determine how the brain enables our auditory system to dynamically react to stressful signals in the world around us.

Imagine you’re decorating a house; as you decorate, each piece of furniture influences how you behave, and how they’re arranged influences how you interact with the whole. Now imagine instead that “you” are a cell in the brain, and the furniture is made up of proteins and sugars. What I study is how the furniture of the brain—those proteins and sugars—changes as an organism develops, and how that change influences how cells function, and how organisms behave.

I study how learning works in the brain. I draw inspiration from machine learning algorithms that are mathematically analogous to biological learning processes. Then I try to understand how elements of these learning algorithms can be implemented in biological hardware. My current work investigates how a machine learning algorithm called temporal difference learning may be accomplished through the interactions of multiple neurobiological parts: dopamine neurons, the neurons they release dopamine onto, and the intricate neural circuitry connecting them. I draw inspiration from theory but am mainly an experimentalist, working with mice and using the remarkable tools we now have to both control and record specific signals in their brains.