Interdisciplinary Trends in the Reintegration of Organisms with Perceptron Units




biological intelligence, convergence, neural network


Various artificial algorithms have emerged from imitating the behavior of primitive cells. Despite their independent development, these algorithms still have the potential to integrate with the intelligence of the primitive biological cells and control their behavior. Research on biologically inspired intelligence emerged earlier, with the goal of combining algorithms with the inherent intelligence of living organisms. When microbial behavior meets the requirements of the algorithm, such microorganisms can be used as a carrier for computation. This review analyzes the shortcomings of this approach and points out potential directions for development, including using existing structures inside cells to simulate control systems such as circuits, in order to construct cells that fully satisfy the needs of the algorithm. However, when using classical control systems to achieve the goal, it is necessary to construct a fairly complex internal cell structure, and this review analyzes the shortcomings of this approach. Subsequently, by analyzing recent research, this review points out the latest novel research direction, in which some scholars attempt to construct artificial cell structures to directly achieve the function of the algorithm by building low-implementation-difficulty internal control systems in cells. However, there are still some problems to be solved, and this review summarizes the relevant research and briefly discusses the current theoretical challenges.


Received: 3 April 2023 | Revised: 17 May 2023 | Accepted: 25 May 2023


Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.


Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.




How to Cite

Deng, Y., Li, Z., & Chen, J. (2023). Interdisciplinary Trends in the Reintegration of Organisms with Perceptron Units. Journal of Data Science and Intelligent Systems.




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