The brain sets a unique learning rate for everything we do, by self-adjusting to the environment

April 19, 2017
brain
Credit: public domain

Each time we get feedback, the brain is hard at work updating its knowledge and behavior in response to changes in the environment; yet, if there's uncertainty or volatility in the environment, the entire process must be adjusted. A Dartmouth-led study published in Neuron reveals 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 always behaves optimally. How the brain adjusts learning, has long been thought to be driven by the brain's 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.

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," says Alireza Soltani, assistant professor of psychological and brain sciences at Dartmouth. "Of course, such simple elements may not provide an optimal solution but we found that a based on metaplasticity can explain real behaviors better than models that are based on optimality," he added.

To understand the neural mechanisms for adjusting learning, more specifically, how learning and choice are impacted by reward uncertainty and volatility in an environment, the researchers created a model based on metaplasticity. They tested this model against a behavioral dataset from a recent Yale University study of non-human primates in which the probabilities of obtaining a reward were switched to create environments with different levels of volatility. When things change frequently, a large learning rate is required but this reduces precision, whereas, a stable environment requires a small learning rate, which improves precision. The study illustrates how metaplasticity can mitigate the tradeoff between adaptability and precision in learning.

The metaplasticity model also demonstrates how the learning rate might be different for each choice or option. If a particular choice continues to give reward for a while, the learning rate on that option becomes larger for rewarding outcomes and smaller for non-rewarding outcomes. That is, if the environment does not change, the synapses needed for changing the preferences become less sensitive to feedback in the opposite direction. In addition, the model also predicts that different options or actions could maintain their own learning rates.

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. The brain's inability to modify its behavior may be attributed to the slowing down of plasticity due to metaplasticity, which can occur in a highly stable . For behavioral anomalies such as addiction, where the might not adapt flexibly, more carefully designed feedback may be required to make the system plastic again, illustrating how metaplasticity may have broader relevance.

Explore further: Mice offer a window into sleep's role in memory

More information: Neuron (2017). www.cell.com/neuron/fulltext/S0896-6273(17)30288-X)

Related Stories

Mice offer a window into sleep's role in memory

March 24, 2017
Sleep provides essential support for learning and memory, but scientists do not fully understand how that process works on a molecular level. What happens to synapses, the connections between neurons, during sleep that helps ...

Study measures bias in how we learn and make decisions

April 26, 2016
Thinking about drawing to an inside straight or playing another longshot? Just remember that while human decision-making is biased by potential rewards, what we know about individual cues that help us to make those decisions ...

Scientists create mouse that resists cocaine's lure

February 13, 2017
Scientists at the University of British Columbia have genetically engineered a mouse that does not become addicted to cocaine, adding to the evidence that habitual drug use is more a matter of genetics and biochemistry than ...

Addicted individuals less responsive to reward-anticipation

February 2, 2017
It may be difficult for addicted individuals to learn when they can expect a reward. This learning problem could perhaps explain why they are more prone to addiction and find it difficult to kick the habit. Researchers at ...

Scientists find brain plasticity assorted into functional networks

February 4, 2016
The brain still has a lot to learn about itself. Scientists at the Virginia Tech Carilion Research Institute have made a key finding of the striking differences in how the brain's cells can change through experience.

Team examines the molecular basis of brain plasticity and the manner in which neurons 'learn'

December 21, 2016
UC Santa Barbara neuroscientist Kenneth S. Kosik has been studying the brain for decades. His UC Santa Barbara neurobiology lab focuses on the evolution of synapses that connect neurons and the genetics of Alzheimer's disease. ...

Recommended for you

Touching helps build the sexual brain

September 21, 2017
Hormones or sexual experience? Which of these is crucial for the onset of puberty? It seems that when rats are touched on their genitals, their brain changes and puberty accelerates. In a new study publishing September 21 ...

Gene immunotherapy protects against multiple sclerosis in mice

September 21, 2017
A potent and long-lasting gene immunotherapy approach prevents and reverses symptoms of multiple sclerosis in mice, according to a study published September 21st in the journal Molecular Therapy. Multiple sclerosis is an ...

Neuron types in brain are defined by gene activity shaping their communication patterns

September 21, 2017
In a major step forward in research, scientists at Cold Spring Harbor Laboratory (CSHL) today publish in Cell a discovery about the molecular-genetic basis of neuronal cell types. Neurons are the basic building blocks that ...

Your neurons register familiar faces, whether you notice them or not

September 21, 2017
When people see an image of a person they recognize—the famous tennis player Roger Federer or actress Halle Berry, for instance—particular cells light up in the brain. Now, researchers reporting in Current Biology on ...

Highly precise wiring in the cerebral cortex

September 21, 2017
Our brains house extremely complex neuronal circuits whose detailed structures are still largely unknown. This is especially true for the cerebral cortex of mammals, where, among other things, vision, thoughts or spatial ...

Faulty cell signaling derails cerebral cortex development, could it lead to autism?

September 20, 2017
As the embryonic brain develops, an incredibly complex cascade of cellular events occur, starting with progenitors - the originating cells that generate neurons and spur proper cortex development. If this cascade malfunctions ...

1 comment

Adjust slider to filter visible comments by rank

Display comments: newest first

EmceeSquared
1 / 5 (3) Apr 19, 2017
"When it comes to choice in evaluating potential rewards, your learned value [...] is stored in certain synapses."

It is still unknown whether the value of any specific choice is stored in certain synapses, or distributed more diffusely, or stored in certain somethings that aren't synapses. The described behavior has been demonstrated in some cases, but not by any means universally.


"If you get positive feedback [...], the brain increases the value of that option [...] synapses stronger. [...] feedback is negative, those synapses become weaker."

In fact it is entirely possible that some kinds of preferences might be stored as inhibitions on the alternatives.

"The findings refute the theory that the brain always behaves optimally."

With so little understood about learning and what factors are optimized, these findings cannot refute that.

These articles should have bylines so we can learn who is making these wrong sweeping generalizations.

Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.