Washington: A new mathematical model has provided scientists with a deeper insight into how brain remains stable during the learning process.
Complex biochemical signals that coordinate fast and slow changes in neuronal networks keep the brain in balance during learning.
Neuronal networks form a learning machine that allows the brain to extract and store new information from its surroundings via the senses. Researchers have long puzzled over how the brain achieves sensitivity and stability to unexpected new experiences during learning, two seemingly contradictory requirements.
To address the problem, the team turned to a classic experimental system. After birth, the visual area of the brain's cortex undergoes rapid modification to match the properties of neurons when seeing the world through the left and right eyes, a phenomenon termed "ocular dominance plasticity," or ODP. The discovery of this dramatic plasticity was recognized by the 1981 Nobel Prize in Physiology or Medicine awarded to David H. Hubel and Torsten N. Wiesel.
ODP learning contains a paradox that puzzled researchers, it relies on fast-acting changes in activity called "Hebbian plasticity" in which neural connections strengthen or weaken almost instantly depending on their frequency of use. However, acting alone, this process could lead to unstable activity levels.
By modeling Hebbian and homeostatic plasticity together, mathematicians Taro Toyoizumi and Ken Miller of Columbia 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.
The study is published in the journal Neuron.