Humans of HBI

We want to understand how we learn from our mistakes. Recognizing what we did wrong is an essential part of learning – just imagine learning a new song, but without realizing you had played a wrong note. This is true not only for music, but for much of what we learn throughout our lives – from walking and talking to recognizing objects and faces. Our goal is to uncover how the brain detects errors and uses this information to guide learning.

I build "virtual model organisms” with artificial bodies and artificial brains that are trained to behave like real model organisms, such as insects and fish, in simulated worlds. The activity of these artificial brains can be studied in silico to help us understand how neural systems turn sensory information into decisions and complex natural behavior. Such "digital twin" models can complement real-world experiments that are challenging, expensive, or tedious – giving us a faster entry point to begin exploring the most promising hypotheses in the lab.

When you look at a neuron, the tiny cell body is just the tip of the iceberg. A tangle of branches called dendrites comprise a large volume of the neuron, acting like antenna for synaptic inputs. We do not fully understand how these long, thin, ion-channel-rich ‘noodles’ contribute to neural computation. I’m using optical imaging to explore this question, watching in real time the fast electrical signaling occurring in dendrites as they process information.

In the past, primary brain tumors were viewed as diseases that merely invade and destroy brain tissue. However, researchers now recognize that these tumors actually integrate into the brain’s circuitry as they grow and progress. Building on these insights, my research aims to uncover how brain tumors integrate into neural networks and influence cognitive processes such as learning and behavior.

Smoke, fresh-baked pie, rotten food, a floral cologne — at least once in your life (probably more), you’ve smelled one of these and tried to figure out where it came from. In each case, you can imagine an odor trace that travels through space and time until it reaches your nose, and your olfactory system detects it. But in natural environments, wind and other factors break up these odor traces (or “plumes,” as we call them) into signals that are sparse and highly fluctuating — nothing like the steady, delicious gradient of pie smell when you walk into a bakery. My work aims to understand how animals integrate this noisy odor information over time to figure out where an odor is coming from — and more specifically, which brain areas are involved, and how neurons in those areas weigh odor information to make these estimations.