Mathematical Model Shows How the Brain Remains Stable During Learning

Complex biochemical signals that coordinate fast and slow changes in neuronal networks keep the brain in balance during learning, according to a study recently published in Neuron. The work represents a six-year quest by a collaborative team from the three institutions to solve a question of how the brain learns and consolidates new experiences on dramatically different timescales. Neuronal networks form a learning machine that allows the brain to extract and store new information from its surroundings via the senses, and a new model devised by the research team shows how the brain’s network can learn new information while maintaining stability. By modeling Hebbian and homeostatic plasticity together, both of which control ocular dominance plasticity, researchers saw a possible resolution to the paradox of brain stability during learning. The theory and experimental findings showed that fast Hebbian and slow homeostatic plasticity work together during learning, but only after each has independently assured stability on its own timescale. To learn more about this study, click here.