Nav: Home

Balancing time & space in the brain: New model holds promise for predicting brain dynamics

October 31, 2016

PITTSBURGH--For as long as scientists have been listening in on the activity of the brain, they have been trying to understand the source of its noisy, apparently random, activity. In the past 20 years, "balanced network theory" has emerged to explain this apparent randomness through a balance of excitation and inhibition in recurrently coupled networks of neurons. A team of scientists has extended the balanced model to provide deep and testable predictions linking brain circuits to brain activity.

Lead investigators at the University of Pittsburgh say the new model accurately explains experimental findings about the highly variable responses of neurons in the brains of living animals. On Oct. 31, their paper, "The spatial structure of correlated neuronal variability," was published online by the journal Nature Neuroscience.

The new model provides a much richer understanding of how activity is coordinated between neurons in neural circuits. The model could be used in the future to discover neural "signatures" that predict brain activity associated with learning or disease, say the investigators.

"Normally, brain activity appears highly random and variable most of the time, which looks like a weird way to compute," said Brent Doiron, associate professor of mathematics at Pitt, senior author on the paper, and a member of the University of Pittsburgh Brain Institute (UPBI). "To understand the mechanics of neural computation, you need to know how the dynamics of a neuronal network depends on the network's architecture, and this latest research brings us significantly closer to achieving this goal."

Earlier versions of the balanced network theory captured how the timing and frequency of inputs--excitatory and inhibitory--shaped the emergence of variability in neural behavior, but these models used shortcuts that were biologically unrealistic, according to Doiron.

"The original balanced model ignored the spatial dependence of wiring in the brain, but it has long been known that neuron pairs that are near one another have a higher likelihood of connecting than pairs that are separated by larger distances. Earlier models produced unrealistic behavior--either completely random activity that was unlike the brain or completely synchronized neural behavior, such as you would see in a deep seizure. You could produce nothing in between."

In the context of this balance, neurons are in a constant state of tension. According to co-author Matthew Smith, assistant professor of ophthalmology at Pitt and a member of UPBI, "It's like balancing on one foot on your toes. If there are small overcorrections, the result is big fluctuations in neural firing, or communication."

The new model accounts for temporal and spatial characteristics of neural networks and the correlations in the activity between neurons--whether firing in one neuron is correlated with firing in another. The model is such a substantial improvement that the scientists could use it to predict the behavior of living neurons examined in the area of the brain that processes the visual world.

After developing the model, the scientists examined data from the living visual cortex and found that their model accurately predicted the behavior of neurons based on how far apart they were. The activity of nearby neuron pairs was strongly correlated. At an intermediate distance, pairs of neurons were anticorrelated (When one responded more, the other responded less.), and at greater distances still they were independent.

"This model will help us to better understand how the brain computes information because it's a big step forward in describing how network structure determines network variability," said Doiron. "Any serious theory of brain computation must take into account the noise in the code. A shift in neuronal variability accompanies important cognitive functions, such as attention and learning, as well as being a signature of devastating pathologies like Parkinson's disease and epilepsy."

While the scientists examined the visual cortex, they believe their model could be used to predict activity in other parts of the brain, such as areas that process auditory or olfactory cues, for example. And they believe that the model generalizes to the brains of all mammals. In fact, the team found that a neural signature predicted by their model appeared in the visual cortex of living mice studied by another team of investigators.

"A hallmark of the computational approach that Doiron and Smith are taking is that its goal is to infer general principles of brain function that can be broadly applied to many scenarios. Remarkably, we still don't have things like the laws of gravity for understanding the brain, but this is an important step for providing good theories in neuroscience that will allow us to make sense of the explosion of new experimental data that can now be collected," said Nathan Urban, associate director of UPBI.
-end-
In addition to Doiron and Smith, Jonathan Rubin, professor of mathematics at Pitt; Robert Rosenbaum, a former postdoctoral scholar at Pitt and now an assistant professor at the University of Notre Dame; and Adam Kohn from the Albert Einstein College of Medicine contributed to this work.

The research was funded by National Science Foundation grants awarded as part of the federal BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative. Additional support was provided by the National Eye Institute, Research to Prevent Blindness, the Eye and Ear Foundation of Pittsburgh, and the Simons Foundation.

With more than 150 faculty members, the University of Pittsburgh Brain Institute seeks to unlock the mysteries of normal and abnormal brain function and then translate discoveries into new approaches for overcoming brain disorders. The institute employs multiple levels of analysis, from molecular and cellular approaches to whole systems and behavioral analysis, and incorporates research across disciplines including neuroscience, bioengineering, computer science, and robotics.

University of Pittsburgh

Related Neurons Articles:

How do we get so many different types of neurons in our brain?
SMU (Southern Methodist University) researchers have discovered another layer of complexity in gene expression, which could help explain how we're able to have so many billions of neurons in our brain.
These neurons affect how much you do, or don't, want to eat
University of Arizona researchers have identified a network of neurons that coordinate with other brain regions to influence eating behaviors.
Mood neurons mature during adolescence
Researchers have discovered a mysterious group of neurons in the amygdala -- a key center for emotional processing in the brain -- that stay in an immature, prenatal developmental state throughout childhood.
Astrocytes protect neurons from toxic buildup
Neurons off-load toxic by-products to astrocytes, which process and recycle them.
Connecting neurons in the brain
Leuven researchers uncover new mechanisms of brain development that determine when, where and how strongly distinct brain cells interconnect.
More Neurons News and Neurons Current Events

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Rethinking Anger
Anger is universal and complex: it can be quiet, festering, justified, vengeful, and destructive. This hour, TED speakers explore the many sides of anger, why we need it, and who's allowed to feel it. Guests include psychologists Ryan Martin and Russell Kolts, writer Soraya Chemaly, former talk radio host Lisa Fritsch, and business professor Dan Moshavi.
Now Playing: Science for the People

#537 Science Journalism, Hold the Hype
Everyone's seen a piece of science getting over-exaggerated in the media. Most people would be quick to blame journalists and big media for getting in wrong. In many cases, you'd be right. But there's other sources of hype in science journalism. and one of them can be found in the humble, and little-known press release. We're talking with Chris Chambers about doing science about science journalism, and where the hype creeps in. Related links: The association between exaggeration in health related science news and academic press releases: retrospective observational study Claims of causality in health news: a randomised trial This...