American Journal of Neural Networks and Applications
Volume 6, Issue 1, June 2020, Pages: 10-15
Received: Feb. 20, 2020;
Accepted: Mar. 10, 2020;
Published: Mar. 24, 2020
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Seisuke Yanagawa, OptID, Machida, Tokyo, Japan
Based on known functions of neuroscience the neural network that performs serial parallel conversion and its inverse transformation is presented. By hierarchy connecting the neural networks, the upper neural network that can process general time sequence data is constructed. The activity of the upper neural networks changes in response to the context structure inherent in the time series data and have both function of accepting and generating of general time series data. Eating behavior in animals in the early stages of evolution is also processing time series data, and it is possible to predict behavior although be limited short term by learning the contextual structure inherent in time series data. This function is the behavior of so-called short-term memory. Transition of the activation portion in this type of operation is illustrated. Although status of nervous system of the animal change according to the recognition by sensory organ and to the manipulation of the object by muscle in the vicinity of the animal itself, the evolved animals have in addition another nervous system so-called long-term memory or episodic memory being involved experience and prediction. The nervous system of long-term memory behaves freely but keeping consistency of the change in the environment. By the workings of long-term memory, lot of information are exchanged between fellows, and lot of time series data are conserved by characters in human society. In this paper, the model of the transfer of data between different nervous systems is shown using the concept of category theory.
Each Role of Short-term and Long-term Memory in Neural Networks, American Journal of Neural Networks and Applications.
Vol. 6, No. 1,
2020, pp. 10-15.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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