A team of investigators has identified new “rules” that neurons follow that can revolutionize psychiatric medicine – and even artificial intelligence.
Every day people are constantly learning and creating new memories. When you start practicing a new hobby, try a recipe recommended by a friend or reads the latest news from the world, your brain keeps many of these memories for years or decades.
But how does your brain get this amazing feat?
In our recently published investigation in the magazine, a team of scientists identified some of the “rules” that the brain uses to learn. Explain them in.
Brain learning
The human brain consists of thousands of millions of nerve cells. These neurons conduct electrical impulses that carry information, as computers use binary code to transport data.
These electrical impulses are communicated to other neurons through connections between them called synapses. Individual neurons have branched extensions known as dendritis that can receive thousands of electrical inputs from other cells. Transmit these inputs to the main body of neuron, where it integrates all these signs to generate your own electrical impulses.
Is the collective activity of these electrical impulses in specific groups of neurons that form the representations of different information and experiences in the brain.
For decades, neuroscientists have thought that the brain learns by altering the way neurons are linked to each other. As new information and experiences change the way neurons communicate with each other and change their standards of collective activity, Some synaptic bonds become stronger while others become weaker. This process of synaptic plasticity is what produces representations of new information in the brain.
However, for your brain to produce the right representations during learning, the right synaptic connections must undergo the right changes at the right time. The “rules” that your brain uses to select the synapses that must be changed during learning – what neuroscientists call the Problem of credit assignment – They have remained unfolded.
Define the rules
The team then decided to monitor the activity of individual synaptic connections in the brain during learning to see if they could identify activity patterns that determined which connections would be stronger or weaker.
For such, genetically encoded biosensors in mouse neurons which were lit in response to synaptic and neural activity. They monitored this activity in real time as rats learned a task involving pressing a lever for a given position after a beep to receive water.
They were surprised to find that the synapses of a neuron do not all follow the same rule. For example, scientists often thought that neurons followed the so -called Hebbian rules, where the neurons who consistently shoot together, bind each other.
Instead, they found that synapses in different places of the dendrites of the same neuron They followed different rules To determine if the calls were stronger or weaker. Some synapses adhered to Hebbian’s traditional rule, in which neurons who consistently trigger together reinforce their calls. Other synapses did something different and completely independent.
Our findings suggest that neurons, by simultaneously using two different sets of learning rules in different groups of synapses, rather than a single uniform rule, can more accurately tune the different types of inputs which receive to properly represent new information in the brain.
In other words, following different rules in the learning process, Neurons can be multitask and perform multiple functions in parallel.
Future Applications
This discovery allows you to better understand how the links between neurons change during learning. Given that most brain disturbances, including degenerative and psychiatric diseases, involve some form of malfunction of synapses, this fact has potentially implications important for human health and society.
For example, depression may develop due to excessive weakening of synaptic bonds in certain areas of the brain, which makes it more difficult to feel pleasure. Understanding how synaptic plasticity works normally, scientists may be able to better understand what is wrong in depression and develop therapies to treat it more effectively.
These findings can also have Implications for Artificial Intelligence. Artificial neuronal networks underlying AI were widely inspired by the functioning of the brain.
However, the learning rules that researchers use to update connections within networks and train models are generally uniform and are not biologically plausible either. The new investigation can provide information on how develop more realistic IA models From the biological point of view, which are more efficient, perform better or both.
There is a long way to go before you can use this information to develop new therapies for human brain disturbances. Although it has been found that synaptic bonds in different dendritic groups use different learning rules, We don’t know exactly why or how. Moreover, although neurons’ ability to simultaneously use various learning methods increase their ability to codify information, it is not yet known that other properties this can give them.
Future investigation is expected to answer these questions and deepen the understanding of how the brain learns.
