17 Feb Researchers are using machine learning to understand how brain cells work
For something so small, neurons can be quite complex — not only because there are billions of them in a brain, but because their function can be influenced by many factors, like their shape and genetic makeup.
A research team led by Daifeng Wang, a Waisman Center professor of biostatistics and medical informatics and computer sciences at the University of Wisconsin–Madison, is adapting machine learning and artificial intelligence techniques to better understand how a variety of traits together affect the way neurons work and behave.
Called manifold learning, the approach may help researchers better understand and even predict brain disorders by looking at specific neuronal properties. The Wang lab recently published its findings in two studies.
In the first study, reported in November 2021 in the journal Communications Biology, the researchers showed they could apply manifold learning to predict the features of neurons. Applying existing machine learning techniques, which use computer algorithms to analyze large amounts of data and automatically make predictions, they found they could classify cells based on their genes and their electrophysiological behavior. This behavior encompasses the electrical activity of neurons, which is crucial for communication between neurons and, ultimately, the brain’s function.