Work will aid neural-network modeling, studies of learning and disease
A simple, elegant method could enable scientists to predict how groups of neurons respond to one another and synchronize their activity, report a group of investigators at Carnegie Mellon University. Their work, in press with "Physical Review Letters," ultimately could help scientists understand how neurons network with one another in learning and disease.
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.
Studying PRCs is extremely important to the overall goal of determining what sensory input gets through, what gets suppressed and what gets enhanced, noted Urban.
In fact, determining the PRCs of neurons can help reveal many aspects of neural function, according to the CNBC authors, which include Urban, Roberto Fernández Galán, a physicist and postdoctoral researcher in Urban's group, and G. Bard Ermentrout, University Professor of Computational Biology in the University of Pittsburgh Department of Mathematics.
A PRC can act like a fingerprint, telling neuroscientists whether a neuron is capable of speeding up or delaying its output in response to a stimulus, or whether a neuron only can fire more quickly in response to a stimulus. The CNBC's novel method is the first for estimating PRCs for both these types of neurons. A neuron's PRC also tells researchers when an outside stimulus could stop that neuron from firing. Additionally, knowing PRCs of neurons allows investigators to predict whether they will become entrained to a rhythmic stimulus.
The investigators developed their method to estimate PRCs on simulated neurons and on living neurons within the mouse olfactory bulb, the region of the brain responsible for sensing smell and relaying that information to other parts of the brain.
"We expect our method to help elucidate mechanisms for neural synchrony in the olfactory bulb and for other biological neural networks," said Urban.
This work could reveal how sensory stimuli are transformed into information used in learning. It also could help scientists determine how drugs disrupt or enhance synchronous neuronal activity associated with health or disease.
PRCs have been used widely to study oscillatory behavior in chemical systems and the climate, but CNBC researchers are now able to use the PRCs measured from living neurons to explore how inputs shared by multiple neurons may be able to cause synchronous neural activity. This activity leads to more synchronized firing, which in turn amplifies the sensitivity of the neurons to the shared signal, eventually leading to strong synchronization
Source: Eurekalert & othersLast reviewed: By John M. Grohol, Psy.D. on 21 Feb 2009
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