Researchers have developed computational models of neural circuits that mimic the sensory act of smelling.
They found the models also manifest certain properties analogous to those observed in olfactory sensory processing in insect brains.
In order to build a model that mimicked the process of smelling, the researchers first had to mathematically describe the process of sensory detection. Then they built computational models of neural circuits that would best satisfy those mathematical constraints.
They found that the model developed emergent properties—properties that are more than the sum of their parts, so to speak—similar to properties seen in an insect’s antennal lobe, which is important for its sense of smell.
“What we did was to ask, as engineers, how might we think about building a brain network that enables the detection of different smells. In pursuing this question, what came out was something that looked remarkably biological in nature,” says ShiNung Ching, an associate professor in the electrical and system engineering department at the McKelvey School of Engineering at Washington University in St. Louis.
“This was exciting since it gave us a new hypothesis, grounded in engineering theory, about how the brain achieves this type of sensory processing.”
The team plans to extend this framework to study olfactory processing in other organisms, as well as other forms of neural information processing.
The research appears in The Journal of Neuroscience.
