Artificial intelligence

Abstract: This article explores one of the most mysterious and multifaceted aspects of human nature - memory. Memory defines the system's ability to learn, adapt, and make informed decisions based on accumulated experience. It plays an important role in solving conceptual and theoretical problems in the field of artificial intelligence and modeling. Learning and adaptation is one of the key aspects where memory models allow AI systems to not only reproduce but also improve their performance based on experience. It is also important to consider memory modeling as preserving the context of past events, which is important for the correct understanding and interpretation of current situations. The article examines the issue of memory location, starting with classical theories explaining the mechanisms of memory location in the brain, including neurophysiology and the theory of conditioned reflexes. However, special attention is paid to alternative approaches, such as the theory of intracellular memory, which offers new ways of understanding memory. Finally, the work draws attention to the connection between learning and memory, especially in the context of the formation of conditioned reflexes. In the process of considering the neuron as a key element of the nervous system and studying protein synthesis and polyribosomes, a surprising similarity between the process of protein synthesis in neurons and the functioning of the Turing machine was revealed. In the context of this analogy, a neuron can be perceived as a molecular computer, providing a new level of understanding of memory formation and information processing in the brain. The author hopes that this research will help to better understand the nature of human memory and enrich our knowledge about how the brain works.

Keywords: memory, neuron, biosynthesis Turing machine, neuron molecular computer

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