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 HealthMore information:
PLoS Computational Biology---http://www.ploscompbiol.org/home.action
Nancy Ross-Flanigan | Newswise Science News
The birth of a new protein
20.10.2017 | University of Arizona
Building New Moss Factories
20.10.2017 | Albert-Ludwigs-Universität Freiburg im Breisgau
University of Maryland researchers contribute to historic detection of gravitational waves and light created by event
On August 17, 2017, at 12:41:04 UTC, scientists made the first direct observation of a merger between two neutron stars--the dense, collapsed cores that remain...
Seven new papers describe the first-ever detection of light from a gravitational wave source. The event, caused by two neutron stars colliding and merging together, was dubbed GW170817 because it sent ripples through space-time that reached Earth on 2017 August 17. Around the world, hundreds of excited astronomers mobilized quickly and were able to observe the event using numerous telescopes, providing a wealth of new data.
Previous detections of gravitational waves have all involved the merger of two black holes, a feat that won the 2017 Nobel Prize in Physics earlier this month....
Material defects in end products can quickly result in failures in many areas of industry, and have a massive impact on the safe use of their products. This is why, in the field of quality assurance, intelligent, nondestructive sensor systems play a key role. They allow testing components and parts in a rapid and cost-efficient manner without destroying the actual product or changing its surface. Experts from the Fraunhofer IZFP in Saarbrücken will be presenting two exhibits at the Blechexpo in Stuttgart from 7–10 November 2017 that allow fast, reliable, and automated characterization of materials and detection of defects (Hall 5, Booth 5306).
When quality testing uses time-consuming destructive test methods, it can result in enormous costs due to damaging or destroying the products. And given that...
Using a new cooling technique MPQ scientists succeed at observing collisions in a dense beam of cold and slow dipolar molecules.
How do chemical reactions proceed at extremely low temperatures? The answer requires the investigation of molecular samples that are cold, dense, and slow at...
Scientists from the Max Planck Institute of Quantum Optics, using high precision laser spectroscopy of atomic hydrogen, confirm the surprisingly small value of the proton radius determined from muonic hydrogen.
It was one of the breakthroughs of the year 2010: Laser spectroscopy of muonic hydrogen resulted in a value for the proton charge radius that was significantly...
17.10.2017 | Event News
10.10.2017 | Event News
10.10.2017 | Event News
20.10.2017 | Information Technology
20.10.2017 | Materials Sciences
20.10.2017 | Interdisciplinary Research