The research was conducted at the Center for the Neural Basis of Cognition (CNBC), a joint initiative between Carnegie Mellon and the University of Pittsburgh.
"Synchronization is important for information coding and storage in the brain," said Nathan Urban, an assistant professor of biological sciences at the Mellon College of Science and a member of the CNBC.
The implications of this work for understanding human development and disease are far-reaching according to Urban, because some types of synchronized nerve activity lead to learning, while others can trigger disabling disorders like epilepsy.
Specifically, the study investigators developed a method to calculate the phase-resetting curve (PRC) of living neurons. Like a translation key, a PRC dictates how a given neuron will change its routine firing pattern in response to input from other neurons. "You can think of neurons firing like people clapping after a performance. People don't start out clapping in unison, but then someone sets a beat and everyone follows it. Populations of neurons with similar PRCs can work in the same manner, whereby steady outside input effectively drives them to synchronize their firing," Urban said.
The new method combines computational and experimental approaches to simplify the complex dynamics of a single neuron. Because it is so efficient, it's the first tool that can be practically applied given limited amounts of data. This method also allows scientists to infer coherent network activity of multiple neurons from an estimated PRC for a single neuron.