Brain Adjusts Learning Rate Depending on Environment

Each time we get feedback, the brain updates its knowledge and behavior in response to changes in the environment. However, if there’s uncertainty or volatility in the environment, the entire process must be adjusted.

In a new study, Dartmouth researchers discovered that there’s not a single rate of learning for everything we do, as the brain can self-adjust its learning rates using a synaptic mechanism called metaplasticity.

The findings refute the theory that the brain always behaves optimally. How the brain adjusts learning has long been thought to be driven by the brain’s reward system and its goal of optimizing rewards obtained from the environment or by a more cognitive system responsible for learning the structure of the environment.

Study findings are published in Neuron.

Researchers explain that synapses are the connections between neurons in the brain and are responsible for transferring information from one neuron to the next.

When it comes to choice in evaluating potential rewards, your learned value of a particular option, reflecting how much you like something, is stored in certain synapses. If you get positive feedback after choosing a particular option, the brain increases the value of that option by making the associated synapses stronger.

In contrast, if the feedback is negative, those synapses become weaker. Synapses, however, can also undergo modifications without changing how they transmit information through a process called metaplasticity.

Previous studies have suggested that the brain relies on a dedicated system for monitoring the uncertainty in the environment to adjust its rate of learning. The authors of this study found however, that metaplasticity alone is sufficient to fine-tune learning according to the uncertainty about reward in a given environment.

“One of the most complex problems in learning is how to adjust to uncertainty and the rapid changes that take place in the environment. It is very exciting to find that synapses, the simplest computational elements in the brain, can provide a robust solution for such challenges,” said Dr. Alireza Soltani, an assistant professor of psychological and brain sciences.

“Of course, such simple elements may not provide an optimal solution but we found that a model based on metaplasticity can explain real behaviors better than models that are based on optimality,” he added.

This study demonstrates that learning can be self-adjusted and does not require explicit optimization or complete knowledge of the environment. The authors propose potential practical implications of their findings.

For behavioral anomalies such as addiction, where the synapses might not adapt flexibly, more carefully designed feedback may be required to make the system plastic again, illustrating how metaplasticity may have broader relevance.

Source: Dartmouth College/EurekAlert