How does your brain create new memories? Neuroscientists discover ‘rules’ for how neurons encode new information
Every day, people learn constantly and form new memories. When you pick up a new hobby, try a recipe recommended by a friend or read the latest news of the world, your mind stores many of these memories For years or contracts.
But how does your mind achieve this amazing achievement?
In our newly published research in the Journal of Science, we have identified some of “Rules” uses the brain to learn.
Learning in the brain
The human brain consists of Billions of nerve cells. These neurons make electrical impulses that carry information, such as how to use computers to a dual -data code.
These electrical impulses are connected with other neurons through the links between them Intertwined. Individual nerve cells have branched extensions known as ramifications that can receive thousands of electrical inputs from other cells. Dendrites transmit these inputs to the main body of neurons, where then then It merges all these signals To generate her own electrical pulse.
that it Group activity Among these electrical impulses through specific groups of neurons that make up the various information representations and experiences within the brain.
For decades, neuroscientists believed that the brain learns to change how neurons are linked to each other. Since new information and experiences change how neurons communicate with each other and change their group activity patterns, some interlocking connections have become stronger while they become weaker. This process The plasticity is intertwined It is what produces new information and experiences within your mind.
In order for your brain to result in the right representations during learning, the correct interlocking connections must be subject to the right time changes. “The rules” that your mind uses to choose clamps that must be changed during learning – what neurologists call Credit assignment problem – She remained largely unclear.
Determine the rules
We decided to monitor the activity of individual interlocking communications inside the brain while learning to know if we can determine the patterns of activity that define communications that will become stronger or weaker.
To do this, we have encoded genetically biological sensors in neurons from mice that will light up in response to intertwining and nervous activity. We have monitored this activity in the actual time as mice learned an task that included pressure on a lever to a specific position after an audio signal to receive water.
We were surprised when we found that neurons on neurons do not follow the same rule. For example, scientists often believed that neurons follow the so -called Hebbian rulesWhere the nerve cells that are constantly released together, wire together. Instead, we saw that the clamps are on different sites of ramifications of the same neurons It follows different rules To determine whether the connections are stronger or weaker. Some clamps are committed to the traditional Hebbian base, as they constantly release nerve cells. Other clamps did something different and completely independent of neurons.
The results we find indicate that the nerve cells, by using two different groups of the rules for learning across different groups of clamps, instead of one uniform base, can control the different types of inputs that they receive to represent new information appropriately in the brain.
In other words, by following different rules in the learning process, multi -tasking neurons can perform multiple functions in parallel.
Future applications
This discovery provides a clearer understanding of how the bonds between neurons change during learning. Given that most brain disorders, including Interestism and Psychiatry Conditions include a form of imbalances, and this has important effects on human health and society.
For example, Depression may develop Excessive weakness in interlocking communications within certain areas of the brain that makes it difficult to experience pleasure. By understanding how the interlocking plasticity usually works, scientists may be able to better understand what is happening in depression and then develop treatments to treat them more effectively.
These results may also have effects on artificial intelligence. The artificial nervous networks behind artificial intelligence are largely inspired How the brain works. However, the learning rules used by researchers to update communications within networks and training for models are usually uniform and also It is not reasonable biologically. Our research may provide an insight into how to develop the most realistic artificial intelligence models that are more efficient, better, or both.
There is still a long way to cut it before we can use this information to develop new treatments for human brain disorders. While we found that interlocking connections on different groups of ramifications use different learning rules, we do not know exactly the cause or how. In addition, although the ability of neurons to use multiple educational methods simultaneously increases their ability to encrypt information, other properties that these may give these are not yet clear.
We hope that future research will answer these questions and enhance our understanding of how to learn the brain.
This article has been republished from ConversationAn independent, non -profit news organization brings you facts and trusted analysis to help you understand our complex world. Written by: William Wrightand University of California, San Diego and Takaki Cumamaand University of California, San Diego
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William Wright receives funding from the National Health Institutes (NINDS) and the Schmidt Sciences Foundation.
Takaki Komiyama receives funding from NIH, NSF, Simons Foundation, Chan Zuckerberg Beauty, and Kavli Institute of Brain and Reason.
