HomeNewsResearchers Create New Prototype of the Brain's Real-Life Neural Networks

Researchers Create New Prototype of the Brain’s Real-Life Neural Networks

Scientists have created a new paradigm of how data deep in the brain could travel from one network to another ​and how these neuronal network bundles self-improve with time.

The team – comprised of researchers from the Cyber-Physical Systems Group at the USC Viterbi School of Engineering and the University of Illinois at Urbana-Champaign – has titled the paper “Network Science Characteristics of Brain-Derived Neuronal Cultures Deciphered From Quantitative Phase Imaging Data,” which is believed to be the first research to examine this self-optimization event in vitro neuronal networks.

Uncertainty in the Brain

The researchers’ discoveries can make new paths for biologically inspired artificial intelligence, identify brain cancer and diagnosis, and help develop new Parkinson’s treatments.

The team analyzed the structure and development of neuronal networks in mice and rats’ brains to detect the connectivity patterns. Co-author and Electrical and Computing Engineering associate professor Paul Bogdan explained how the brain operates in decision-making; he detailed the brain activity that takes place when someone is noticed to be counting cards.

According to Bodgan, the brain might not memorize all the options but instead is ‘conducting a type of model of uncertainty.’ The brain, he says, is gathering sufficient information from all the connections of neurons.

The active clustering that is taking place in this scenario is allowing the brain to assess diverse degrees of uncertainty, get approximate probabilistic details, and understand what kind of conditions are less probable.

Modeling the Phenomenon Accurately

“We observed that the brain’s networks have an extraordinary capacity to minimize latency, maximize throughput and maximize robustness while doing all of those in a distributed manner (without a central manager or coordinator),” said Bogdan, who has the Jack Munushian Early Career Chair at the Ming Hsieh Department of Electrical Engineering. “This means that neuronal networks negotiate with each other and connect to each other in a way that rapidly enhances network performance yet, the rules of connecting are unknown.”

To the researcher’s surprise, none of the traditional mathematical models used by neuroscience managed to precisely replicate this dynamic nascent connectivity phenomenon.

Using multifractal examination and a new imaging technique known as quantitative phase imaging (QPI) created by Gabriel Popescu, a professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, and co-author of the research, the scientific team was able to model and examine this event with high accuracy.

Health Applications

The discoveries of this study could have a major impact on the early detection of brain tumors. By having a better topological map of the healthy brain and brain’s dynamics to compare, it will be simpler to detect basic anomalies early from imaging the dynamic connectivity among neurons in different cognitive tasks without having to use more intrusive methods.

Co-author Chenzhong Yin, a Ph.D. student in Bogdan’s Cyber-Physical Systems Group says: “Cancer spreads in small groups of cells and cannot be detected by FMRI or other scanning techniques until it’s too late. But with this method, we can train A.I. to detect and even predict diseases early by monitoring and discovering abnormal microscopic interactions between neurons.”

Additional Uses

The scientists are now trying to perfect their algorithm and imaging instrument to examine these intricate neuronal networks present inside a living brain. This could have more applications for diseases like Parkinson’s, which consists in losing the neuronal connections between the two hemispheres of the brain.

“By placing an imaging device on the brain of a living animal, we can also monitor and observe things like neuronal networks growing and shrinking, how memory and cognition form, if a drug is effective and ultimately how learning happens. We can then begin to design better artificial neural networks that, like the brain, would have the ability to self-optimize,” Yin explained.


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