Yale study reveals individual neurons operate as simple on-off switches

Brain cells manage some of the most complex processes in the human body, from speech and movement to thought itself. Yet a new study from Yale University shows that the way individual neurons operate is surprisingly straightforward.

Most of these highly connected nerve cells function essentially like tiny on-off switches. The findings confirm that early mathematical tracking models from the 1940s remain highly accurate today.

“I was so surprised,” said Christopher Lynn, an assistant professor of physics in Yale’s Faculty of Arts and Sciences and author of the study. “We have 100 billion nerve cells firing in our brain, and each one of them has 10,000 connections to other neurons. There was no reason to expect that a single neuron has such a simple description.”

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Lynn, who is also a member of Yale’s Quantitative Biology Institute and Wu Tsai Institute, published the research in the journal Nature Physics. For this project, he shifted his regular focus from large brain networks to the inner mechanics of individual cells.

To analyze the cells, Lynn built a computational model that split neuron activity into three distinct categories. He tracked simple interactions between one input and one output, complex interactions with multiple inputs, and the random noise caused by chemical spikes and synapses.

Neurons rely on electrical signals and small chemical releases to communicate across synapses. When Lynn applied his mathematical model to existing brain data from mice and the worm C. elegans, the simple interactions dominated the results.

Basic one-input, one-output interactions accounted for 90 percent of the neuronal activity in the mouse data. In the worm data, that same straightforward activity made up 60 to 70 percent of the total. Lynn noted that he had originally expected to see an equal split among the three types of activity.

The results align closely with the first mathematical model of a neuron, created by Warren S. McCulloch and Walter Pitts in 1943. That historic work later served as the foundation for modern machine learning and artificial neural networks.

The National Institutes of Health supported the study in part. Lynn plans to expand the research by comparing these findings against neural data from other animal species to see if neuron behavior changes based on biological complexity.

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Yale study reveals individual neurons operate as simple on-off switches