A New Finding About the Human Brain Discovered

By studying the structure of the human brain, a group of scientists found a way to explain the working principle of biological optimization. The research work was published in the scientific journal Communications Biology.

A group of scientists at the RIKEN Center for Brain Science in Japan studied the working principle of the brain. As a result of the research, the principle of free energy efficiency of neural networks Explained how it is optimized for

Scientists, due to thought disorders of the data obtained from these studies analyzing impaired brain functions and even in artificial intelligence technology create neural networks reported that it can be used for

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Biological optimization can be briefly described as a natural process that makes our behaviors and bodies as efficient as possible. In the brain, neural networks maintain the ability to adapt and reconfigure to changing environments and enable efficient control of behavior and transmission of information. This process is the natural biological optimization of the brain. Scientists at the RIKEN Brain Science Center in Japan self-optimization of neural networks conducted a research to discover the underlying mathematical principles.

The study, which was published in the scientific journal Communications Biology, shows that the free energy principle does not apply to any basis of neural network showed that. According to the principle of free energy, sensory data that is formed/will be formed by past outputs or decisions is constantly updated. The strength of neural connections and sensory changes within a network are shaped by this principle.

Research leader Takuya Isomura, “Our findings prove that a neural network can be used as an agent that complies with the free energy principle, providing a universal characterization for the brain.” said. These rules apply to people with thought disorders such as schizophrenia. examining decision-making processes and it can be used to predict the changed aspects of neural networks.


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One of the interesting aspects of the result is that these rules can be used by artificial intelligence developers. This theory, which the researchers put forward, is a new generation of “self-learning” artificial intelligence technologies. designing neuromorphic hardware will reduce the complexity said they were thinking.

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