Researchers Rig Worms' Brains to Predict Smells With Machine Learning
Scientists are using worms to better understand the human brain: Particularly how it processes the sense of smell. So far they have achieved a pretty impressive task: They are able to recognize what the worm smelled a few seconds ago just by looking at its brain.
“We found some unexpected things when we started looking at the effect of these sensory stimuli on individual cells and connections within the worms’ brains,” said Salk Associate Professor Sreekanth Chalasani, member of the Molecular Neurobiology Laboratory and senior author of the new work.
Chalasani wanted to study how the brain processes information from the outside world on the cellular level. However, this was very hard to do on the human brain since it has 86 billion cells. The microscopic worm, Caenorhabditis elegans, has only 302 neurons making it easy to monitor.
Chalasani and his team engineered C. elegans to give each of their 302 neurons a fluorescent sensor that would light up when the neuron was triggered and followed the worms under a microscope as they were exposed to five chemicals: benzaldehyde, diacetyl, isoamyl alcohol, 2-nonanone, and sodium chloride.
These five chemicals smell like almond, buttered popcorn, banana, cheese, and salt to humans.
But the researchers had a hard time differentiating between the effects of the different smells. They then turned to a mathematical approach. The approach called graph theory analyzed the collective interactions between pairs of cells.
Finally, they paired their new approach with machine learning in order to be able to differentiate between even more discreet interactions. They found that this new algorithm was able to clearly differentiate the neural response to salt and benzaldehyde but often confused the other three chemicals.
Now, the researchers hope to use the lessons from this study to gain a deeper understanding of how humans encode information in the brain and what goes wrong in the cases of sensory processing disorders such as attention deficit hyperactivity disorders (ADHD), and autism. The study is published in the journal PLOS Computational Biology.
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