There is a view that the cranial nerve circuit is composed of a combination of the same modules as the basic functions. According to that view, the author has presented the module (Basic Unit) that performs parallel-serial mutual conversion and has shown that the neural network that recognizes and generates arbitrary time-series data can be constructed by combining the module. In Chapter 2, the neural network that has the functions of federated learning and imitation that enable collective behavior of animals is shown, and added an idea of concrete circuit configuration to published papers. In Chapter 3, following a consideration of the fundamental role of language, a neural network with the same basic structure connected to the upper level of the neural network shown in Chapter 2 but with functions closely related to language is presented. The new neural network consists of a pair of neural networks that handle languages and images respectively. Each activated part is expressed using the Category theory concept. Category's entity is a set of Basic Units connected each other and changes of their state. The activated Categories are tied with the corresponding activation part in the pairing neural network, and interconverting is performed. The state of the Basic Unit may be inspired by sensory organs, but behave independently of the actuating behavior of conventional neural networks connected to the low position. Humans can generate an image of events that may occur in past or in future even if that are not directly related to the situation in front of the eye, and share their images by dialogue. The dialogue consists of time series data with a response format such as question or negation. The newly added neural network helps generate shared recognition.
| Published in | American Journal of Neural Networks and Applications (Volume 7, Issue 2) |
| DOI | 10.11648/j.ajnna.20210702.13 |
| Page(s) | 38-44 |
| 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), 2021. Published by Science Publishing Group |
Short-Term Memory, Long-Term Memory, Serial Parallel Conversion, Parallel Serial Conversion, Mirror Neuron, Prediction, Category Theory, Federated Learning
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APA Style
Seisuke Yanagawa. (2021). Communication Between Neural Networks, and Beginning of Language. American Journal of Neural Networks and Applications, 7(2), 38-44. https://doi.org/10.11648/j.ajnna.20210702.13
ACS Style
Seisuke Yanagawa. Communication Between Neural Networks, and Beginning of Language. Am. J. Neural Netw. Appl. 2021, 7(2), 38-44. doi: 10.11648/j.ajnna.20210702.13
AMA Style
Seisuke Yanagawa. Communication Between Neural Networks, and Beginning of Language. Am J Neural Netw Appl. 2021;7(2):38-44. doi: 10.11648/j.ajnna.20210702.13
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Download@article{10.11648/j.ajnna.20210702.13,
author = {Seisuke Yanagawa},
title = {Communication Between Neural Networks, and Beginning of Language},
journal = {American Journal of Neural Networks and Applications},
volume = {7},
number = {2},
pages = {38-44},
doi = {10.11648/j.ajnna.20210702.13},
url = {https://doi.org/10.11648/j.ajnna.20210702.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20210702.13},
abstract = {There is a view that the cranial nerve circuit is composed of a combination of the same modules as the basic functions. According to that view, the author has presented the module (Basic Unit) that performs parallel-serial mutual conversion and has shown that the neural network that recognizes and generates arbitrary time-series data can be constructed by combining the module. In Chapter 2, the neural network that has the functions of federated learning and imitation that enable collective behavior of animals is shown, and added an idea of concrete circuit configuration to published papers. In Chapter 3, following a consideration of the fundamental role of language, a neural network with the same basic structure connected to the upper level of the neural network shown in Chapter 2 but with functions closely related to language is presented. The new neural network consists of a pair of neural networks that handle languages and images respectively. Each activated part is expressed using the Category theory concept. Category's entity is a set of Basic Units connected each other and changes of their state. The activated Categories are tied with the corresponding activation part in the pairing neural network, and interconverting is performed. The state of the Basic Unit may be inspired by sensory organs, but behave independently of the actuating behavior of conventional neural networks connected to the low position. Humans can generate an image of events that may occur in past or in future even if that are not directly related to the situation in front of the eye, and share their images by dialogue. The dialogue consists of time series data with a response format such as question or negation. The newly added neural network helps generate shared recognition.},
year = {2021}
}
TY - JOUR T1 - Communication Between Neural Networks, and Beginning of Language AU - Seisuke Yanagawa Y1 - 2021/12/31 PY - 2021 N1 - https://doi.org/10.11648/j.ajnna.20210702.13 DO - 10.11648/j.ajnna.20210702.13 T2 - American Journal of Neural Networks and Applications JF - American Journal of Neural Networks and Applications JO - American Journal of Neural Networks and Applications SP - 38 EP - 44 PB - Science Publishing Group SN - 2469-7419 UR - https://doi.org/10.11648/j.ajnna.20210702.13 AB - There is a view that the cranial nerve circuit is composed of a combination of the same modules as the basic functions. According to that view, the author has presented the module (Basic Unit) that performs parallel-serial mutual conversion and has shown that the neural network that recognizes and generates arbitrary time-series data can be constructed by combining the module. In Chapter 2, the neural network that has the functions of federated learning and imitation that enable collective behavior of animals is shown, and added an idea of concrete circuit configuration to published papers. In Chapter 3, following a consideration of the fundamental role of language, a neural network with the same basic structure connected to the upper level of the neural network shown in Chapter 2 but with functions closely related to language is presented. The new neural network consists of a pair of neural networks that handle languages and images respectively. Each activated part is expressed using the Category theory concept. Category's entity is a set of Basic Units connected each other and changes of their state. The activated Categories are tied with the corresponding activation part in the pairing neural network, and interconverting is performed. The state of the Basic Unit may be inspired by sensory organs, but behave independently of the actuating behavior of conventional neural networks connected to the low position. Humans can generate an image of events that may occur in past or in future even if that are not directly related to the situation in front of the eye, and share their images by dialogue. The dialogue consists of time series data with a response format such as question or negation. The newly added neural network helps generate shared recognition. VL - 7 IS - 2 ER -
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