Researchers are actively exploring the relevance of automated information analytics using brain models. This field combines neuroscience, artificial intelligence, and machine learning, enabling a deeper understanding of information processing mechanisms in the brain and the development of more effective technologies. Scientists are creating mathematical and computer models that mimic brain functions. These models provide new insights into information processing mechanisms, particularly in the context of cognitive processes. Machine learning has become a powerful tool for analyzing large volumes of data generated in brain research. Machine learning algorithms allow for the estimation of parameters for models that reflect how the brain processes information. Neuromorphic computing mimics the functioning of the biological brain using spiking neural networks (SNNs). These networks transmit data using short pulses, allowing them to simulate natural signal transmission processes. Such systems offer high data processing speeds, can learn in real time, and can effectively solve problems such as speech recognition or image recognition in video sequences. Intel is developing neuromorphic processors, such as Loihi, that mimic the adaptive behavior of the brain. The intersection of neuroscience and artificial intelligence promises revolutionary advances in understanding the human mind and developing more complex and adaptable AI systems. Automated data analytics using brain models is a promising field that could lead to breakthroughs in neuroscience, medicine, technology, and other areas of human endeavor.
| Published in | Innovation (Volume 6, Issue 4) |
| DOI | 10.11648/j.innov.20250604.14 |
| Page(s) | 171-177 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Scientific Information, Artificial Intelligence, Multimodal Neural Network, Models of the Brain
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APA Style
Bryndin, E. (2025). Automated Analytics by Models of Brain Based on Big Information and Artificial Intelligence. Innovation, 6(4), 171-177. https://doi.org/10.11648/j.innov.20250604.14
ACS Style
Bryndin, E. Automated Analytics by Models of Brain Based on Big Information and Artificial Intelligence. Innovation. 2025, 6(4), 171-177. doi: 10.11648/j.innov.20250604.14
AMA Style
Bryndin E. Automated Analytics by Models of Brain Based on Big Information and Artificial Intelligence. Innovation. 2025;6(4):171-177. doi: 10.11648/j.innov.20250604.14
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Download@article{10.11648/j.innov.20250604.14,
author = {Evgeniy Bryndin},
title = {Automated Analytics by Models of Brain Based on Big Information and Artificial Intelligence},
journal = {Innovation},
volume = {6},
number = {4},
pages = {171-177},
doi = {10.11648/j.innov.20250604.14},
url = {https://doi.org/10.11648/j.innov.20250604.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.innov.20250604.14},
abstract = {Researchers are actively exploring the relevance of automated information analytics using brain models. This field combines neuroscience, artificial intelligence, and machine learning, enabling a deeper understanding of information processing mechanisms in the brain and the development of more effective technologies. Scientists are creating mathematical and computer models that mimic brain functions. These models provide new insights into information processing mechanisms, particularly in the context of cognitive processes. Machine learning has become a powerful tool for analyzing large volumes of data generated in brain research. Machine learning algorithms allow for the estimation of parameters for models that reflect how the brain processes information. Neuromorphic computing mimics the functioning of the biological brain using spiking neural networks (SNNs). These networks transmit data using short pulses, allowing them to simulate natural signal transmission processes. Such systems offer high data processing speeds, can learn in real time, and can effectively solve problems such as speech recognition or image recognition in video sequences. Intel is developing neuromorphic processors, such as Loihi, that mimic the adaptive behavior of the brain. The intersection of neuroscience and artificial intelligence promises revolutionary advances in understanding the human mind and developing more complex and adaptable AI systems. Automated data analytics using brain models is a promising field that could lead to breakthroughs in neuroscience, medicine, technology, and other areas of human endeavor.},
year = {2025}
}
TY - JOUR T1 - Automated Analytics by Models of Brain Based on Big Information and Artificial Intelligence AU - Evgeniy Bryndin Y1 - 2025/12/09 PY - 2025 N1 - https://doi.org/10.11648/j.innov.20250604.14 DO - 10.11648/j.innov.20250604.14 T2 - Innovation JF - Innovation JO - Innovation SP - 171 EP - 177 PB - Science Publishing Group SN - 2994-7138 UR - https://doi.org/10.11648/j.innov.20250604.14 AB - Researchers are actively exploring the relevance of automated information analytics using brain models. This field combines neuroscience, artificial intelligence, and machine learning, enabling a deeper understanding of information processing mechanisms in the brain and the development of more effective technologies. Scientists are creating mathematical and computer models that mimic brain functions. These models provide new insights into information processing mechanisms, particularly in the context of cognitive processes. Machine learning has become a powerful tool for analyzing large volumes of data generated in brain research. Machine learning algorithms allow for the estimation of parameters for models that reflect how the brain processes information. Neuromorphic computing mimics the functioning of the biological brain using spiking neural networks (SNNs). These networks transmit data using short pulses, allowing them to simulate natural signal transmission processes. Such systems offer high data processing speeds, can learn in real time, and can effectively solve problems such as speech recognition or image recognition in video sequences. Intel is developing neuromorphic processors, such as Loihi, that mimic the adaptive behavior of the brain. The intersection of neuroscience and artificial intelligence promises revolutionary advances in understanding the human mind and developing more complex and adaptable AI systems. Automated data analytics using brain models is a promising field that could lead to breakthroughs in neuroscience, medicine, technology, and other areas of human endeavor. VL - 6 IS - 4 ER -
Research Center "NATURE INFORMATICS", Novosibirsk, Russia
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