PROVIDENCE, R.I. – A research team based at Brown University has created a theoretical model that may shed light on a brain science mystery: What happens to cells when humans learn and remember?
Luk Chong Yeung, a neuroscience research associate, and her colleagues have come up with a concept that hinges on calcium control. Certain receptors, which act like gates, allow calcium to rush into brain cells that receive memory-making information. Once inside these cells, calcium sets off chemical reactions that change the connections between neurons, or synapses. That malleability, known as synaptic plasticity, is believed to be the fundamental basis of memory, learning and brain development.
The Brown team showed that the control of these receptors not only makes synapses stronger or weaker, but also stabilizes them - without interfering with the richness of the cellular response to signals sent from neighboring cells. Their model appears in the current online early edition of the Proceedings of the National Academy of Sciences.
"The beauty of the brain is that it is plastic and robust at the same time," Luk Chong said. "If the model is verified experimentally, we've solved an important piece of the puzzle of how these seemingly antagonistic properties can and, in fact must, coexist in the cell."
When Luk Chong helped create the model, she was a Brown graduate student pursing her doctoral degree in physics and working at the Institute for Brain and Neural Systems, a research laboratory run by Nobel Prize-winning physicist Leon Cooper.
Two years ago, institute scientists developed a model where N-methyl-D-aspartate receptors control the flow of calcium into signal-receiving neurons. They showed that the model unified several observations of synaptic plasticity and, after being tested in labs, it is seen as the standard model by many researchers in the field.
But the model had a flaw. Although it explained how synapses get stronger or weaker, it didn't account for how synapses stabilize. Without homeostasis, synapses could grow indefinitely - an impossible scenario. So Luk Chong and her colleagues began working on a new version.
They based their model on experimental data as well as mathematical equations. Then Luk Chong applied the model to a simulated brain cell receiving signals from competing synapses. She found that the theory held up: Regulating the flow of calcium into cells allows not only for rapid synaptic changes that capture the transient features of the signal, but also slows homeostatic control that returns the cell to a steady state.
"The key feature of the model is that, unlike many neural learning theories, it is built on real quantities that can be measured in the lab," Luk Chong said. "But the basic principles are universal enough to be applied to any stable plasticity model."
The research team included Cooper, a professor of physics and neuroscience at Brown; Harel Shouval, an assistant professor of neurobiology and anatomy at the University of Texas Medical School at Houston; and Brian Blais, a professor of physics at Bryant College.
The Burroughs Wellcome Fund and the Galkin Foundation funded the work.
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