Activity of brain cell networks

Learning is easier when it only requires nerve cells to rearrange existing patterns of activity than when the nerve cells have to generate new patterns, a study of monkeys has found. The scientists explored the brain’s capacity to learn through recordings of electrical activity of brain cell networks. The study was partly funded by the National Institutes of Health.

“We looked into the brain and may have seen why it’s so hard to think outside the box,” said Aaron Batista, Ph.D., an assistant professor at the University of Pittsburgh and a senior author of the study published in Nature, with Byron Yu, Ph.D., assistant professor at Carnegie Mellon University, Pittsburgh.

The human brain contains nearly 86 billion neurons, which communicate through intricate networks of connections. Understanding how they work together during learning can be challenging. Dr. Batista and his colleagues combined two innovative technologies, brain-computer interfaces and machine learning, to study patterns of activity among neurons in monkey brains as the animals learned to use their thoughts to move a computer cursor.

“This is a fundamental advance in understanding the neurobiological patterns that underlie the learning process,” said Theresa Cruz, Ph.D., a programme official at the National Center for Medical Rehabilitations Research at NIH’s Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). “The findings may eventually lead to new treatments for stroke as well as other neurological disorders.”

Brain-computer interfaces seek to turn thoughts into action. With small surgically implanted electrodes, researchers can simultaneously monitor the electrical activity of hundreds of neurons. A computer converts the signals into commands to move an external device, such as a robotic arm or a computer cursor. Brain-computer interfaces are being developed to help paralysed patients as well as to study the function of healthy brains.

“This evolving technology is a powerful tool for brain research,” said Daofen Chen, Ph.D., a programme director at the National Institute of Neurological Disorders and Stroke (NINDS), part of NIH. “It helps scientists study the dynamics of brain circuits that may explain the neural basis of learning.”