Smell is often dismissed as somehow less important than our other four senses. But you shouldn鈥檛 look down your nose at it. After all, in the wild, it could mean the difference between life and death. This week At the Lab, CSHL Associate Professor Saket Navlakha offers insight into how we learn to distinguish between similar odors.
Read the related story: Smells like learning
Transcript
Nick Wurm: You鈥檙e now At the Lab with 黑料吃瓜资源. My name is Nick Wurm. And this week At the Lab, 鈥淲hat鈥檚 that smell?鈥�
{A nose sniffs the air.}
NW: It sounds funny, but in many ways, that question is central to the human experience. Imagine you鈥檙e in the wild. Something smells awful. It repulses you. You don鈥檛 eat it, and you live to smell another day.
NW: Now, that鈥檚 a basic example, but our odor palates can actually become quite refined. CSHL Associate Professor Saket Navlakha offers wine tasting as an analogy.
Saket Navlakha: People can鈥檛 discriminate between two red wines today and then can discriminate between them after three months of taking wine tasting classes. What is actually happening in your brain potentially that allows you to do that? We鈥檙e providing one answer to that question.
NW: Navlakha is a quantitative biologist. He looks at big questions in life and nature like complex math problems. And he uses computer science to seek elegant solutions.
NW: In this case, Navlakha and his team developed a computer model based on how fruit flies鈥� brains respond to certain odors.
{Flies buzz.}
NW: The team found some neurons in the fruit fly brain respond differently to two dissimilar odors but the same to similar scents. The researchers called these neurons reliable cells. This small group of cells helps the flies quickly distinguish between odors that are very different from one another鈥攍ike rotten and fresh fruits.
NW: On the other hand, another much larger group of neurons responds more unpredictably when the fly encounters similar smells. The researchers called these neurons unreliable cells. They think these neurons might help us learn to identify specific scents鈥攍ike the notes in a glass of wine.
{A glass is poured.}
NW: But in case you鈥檙e wondering, no, this research isn鈥檛 just for wine aficionados. Remember, we鈥檙e dealing with computer models here. And as with all computers, data comes in, data goes out. Navlakha explains:
SN: Maybe you don鈥檛 want a machine-learning model to represent the same input the same way every time. In more continual learning systems, variability could actually be useful.
NW: Want to make AI more reliable? Then it needs to learn to be more discerning. And here鈥檚 a great place to start.
NW: Thanks for listening to At the Lab. Please subscribe wherever you get your podcasts. And visit us online at www.cshl.edu for more fascinating stories like this one. For 黑料吃瓜资源, I鈥檓 Nick Wurm. And I鈥檒l see you next time At the Lab.