Scientists have long pondered how the human brain processes smells. Some researchers argue that the brain uses a snapshot, like a painting or a photograph, at a given moment to capture the essential features of odor. Other experts claim that odor perception is in fact an extended, temporal process, with the brain keeping track of and integrating evolving olfactory patterns – which would make it more similar to a symphony rather than a static artwork. However, new research led by the University of Rochester suggests that our brain does both.
“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 study senior author Krishnan Padmanabhan, an associate professor of Neuroscience at Rochester. “Our work provides scientists with new tools to quantify and interpret the patterns of activity of the brain.”
Professor Padmanabhan and his colleagues developed a computerized mathematical model to simulate the activity of the early olfactory system, a part of the brain responsible for smell processing. The computer simulations revealed that a specific set of connections – called centrifugal fibers – carry impulses from various parts of the central nervous system to the early sensory regions of the brain.
According to the scientists, these centrifugal fibers act as switches, toggling between different strategies of processing smells. When the fibers are in a specific state, the cells in the piriform cortex – the area of the brain in which odor perception emerges – rely on punctual patterns of neural activity, emerging within a given instant of time. When the fibers are in a different state though, the cells in the piriform cortex rely on a pattern of brain activity developing across time.
These results suggest that the olfactory system is highly complex, employing different strategies to make sense of olfactory stimuli. “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. 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,” concluded Professor Padmanabhan.
The study is published in the journal Cell Reports.
By Andrei Ionescu, Earth.com Staff Writer