Oh, the challenges of being a neuron, responsible for essential things like muscle contraction, gland secretion and sensitivity to touch, sound and light, yet constantly bombarded with signals from here, there and everywhere.
How on Earth are busy nerve cells supposed to pick out and respond to relevant signals amidst all that information overload?
Somehow neurons do manage to accomplish the daunting task, and they do it with more finesse than anyone ever realized, new research by University of Michigan mathematician Daniel Forger and coauthors demonstrates. Their findings -- which not only add to basic knowledge about how neurons work, but also suggest ways of better designing the brain implants used to treat diseases such as Parkinson's disease -- were published July 7 in the online, open-access journal PLoS Computational Biology.
Forger and coauthors David Paydarfar at the University of Massachusetts Medical School and John Clay at the National Institute of Neurological Disorders and Stroke studied neuronal excitation using mathematical models and experiments with that most famous of neuroscience study subjects, the squid giant axon -- a long arm of a nerve cell that controls part of the water jet propulsion system in squid.
Among the key findings: Neurons are quite adept at their job. "They can pick out a signal from hundreds of other, similar signals," said Forger, an associate professor of mathematics in the College of Literature, Science and the Arts and a research assistant professor of computational medicine and bioinformatics at the U-M Medical School.
Neurons discriminate among signals based on the signals' "shape," (how a signal changes over time), and Forger and coauthors found that, contrary to prior belief, a neuron's preference depends on context. Neurons are often compared to transistors on a computer, which search for and respond to one specific pattern, but it turns out that neurons are more complex than that. They can search for more than one signal at the same time, and their choice of signal depends on what else is competing for their attention.
"We found that a neuron can prefer one signal -- call it signal A -- when compared with a certain group of signals, and a different signal -- call it signal B -- when compared with another group of signals," Forger said. This is true even when signal A and signal B aren't at all alike.
The findings could contribute in two main ways to the design and use of brain implants in treating neurological disorders.
"First, our results determine the optimal signals to stimulate a neuron," Forger said. "These signals are much more effective and require less battery power than what is currently used." Such efficiency would translate into less frequent surgery to replace batteries in patients with brain implants.
"Second, we found that the optimal stimulus is context-dependent," he said, "so the best signal will differ, depending on the part of the brain where the implant is placed."
The research was funded by the Air Force Office of Scientific Research and the National Institutes of Health.
- Daniel B. Forger, David Paydarfar, John R. Clay. Optimal Stimulus Shapes for Neuronal Excitation. PLoS Computational Biology, 2011; 7 (7): e1002089 DOI: 10.1371/journal.pcbi.1002089
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