Scientists at UC San Diego have uncovered processes that explain how our brains record new information, which could help aid advancements in brain and behavioral disorders and even artificial intelligence.
As new information is learned, the brain’s circuitry undergoes a change in order to accommodate it. While neuroscientists have closely studied how certain synapses within the brain strengthen with new information and others weaken, there has been a lack of understanding as to what dictates this process.
UC San Diego neurobiologists William “Jake” Wright, Nathan Hedrick and Takaki Komiyama have discovered key details about this process and their results were published Thursday in the journal Science.
The researchers used a cutting-edge visualization methodology to zoom into the brain activity of mice and track the activities of synapses and neuron cells during learning activities.
Previously, it was assumed that neurons followed a single set of rules during episodes of learning. But with the ability to see individual synapses like never before, the data revealed that individual neurons follow multiple rules, with synapses in different regions of the brain following different rules.
“When people talk about synaptic plasticity, it’s typically regarded as uniform within the brain,” said Wright, a postdoctoral scholar in the School of Biological Sciences and first author of the study. “Our research provides a clearer understanding of how synapses are being modified during learning, with potentially important health implications since many diseases in the brain involve some form of synaptic dysfunction.”
Neuroscientists have carefully studied how synapses only have access to their own “local” information, yet collectively they help shape broad new learned behaviors, a conundrum labeled as the “credit assignment problem.”
“This discovery fundamentally changes the way we understand how the brain solves the credit assignment problem, with the concept that individual neurons perform distinct computations in parallel in different sub-cellular compartments,” said Komiyama, senior study author and professor in the departments of Neurobiology and Neurosciences.
The new information offers promising insights for the future of A.I. and the brain-like neural networks upon which it operates. For instance, the possibility of designing new, advanced A.I. systems using multiple rules across singular units.
For health and behavior, the findings could offer a new way to treat conditions including addiction, post-traumatic stress disorder and Alzheimer’s disease, as well as neurodevelopmental disorders such as autism.
“This work is laying a potential foundation of trying to understand how the brain normally works to allow us to better understand what’s going wrong in these different diseases,” said Wright.
The main financial support for this multi-year study was provided by several National Institutes of Health research grants and a training grant.
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