According to a new study published in Cell Reports, our minds either can capture a scent immediately or follow different patterns to understand the aroma.
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Life is filled with a range of scents and smells people encounter on an everyday basis. Whether it be the fragrance from your perfume or the aroma of freshly cooked food, the brain processes it all in a couple of specific ways. According to new research published in Cell Reports, our minds understand distinct fragrances like a painting (a snapshot of cell activity) and like a symphony (an evolving group of different cells that work together to understand the scent).

"These findings reveal a core principle of the nervous system, flexibility in the kinds of calculations the brain makes to represent aspects of the sensory world," said Krishnan Padmanabhan, PhD, an associate professor of Neuroscience and senior author of the study. "Our work provides scientists with new tools to quantify and interpret the patterns of activity of the brain."

The researchers uncovered these findings by creating a computer simulation of the olfactory system (the network the brain uses for smelling). The team discovered that centrifugal fibers (a set of connections that carry impulses from areas in the central nervous system to the early sensory regions of the brain) were key. Essentially, the centrifugal fibers are like a switch. When adjusted, they help the brain specifically understand different smells through different "strategies." Through one of the strategies, the brain uses a snapshot to immediately process the initial components of the smell, like a painting or a photo. In the other strategy, the brain follows the scent patterns as they change, processing when and which cells turn on and off, similar to a symphony.

"These mathematical models reveal critical aspects of how the olfactory system in the brain might work and could help build brain-inspired artificial computing systems," Padmanabhan said. "Computational approaches inspired by the circuits of the brain such as this have the potential to improve the safety of self-driving cars, or help computer vision algorithms more accurately identify and classify objects in an image."

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