Research On Bionic Modeling Of Decentralized Decision-making System

Compared with centralized systems,decentralized systems have better performance in extreme environments,and are more resilient to external attacks,damaged or missing components,etc.Every organism lives in a certain environment,and these environments provide every organism with everything it needs to survive.However,the environmental damage associated with human activities has greatly affected the living environment of natural organisms.For example,the glass-walled buildings,everywhere can be seen,have become a major threat to birds.Obstacles such as glass or mesh structures produced by human activities pose new challenges to animal survival.There is a key question here,whether animals can bypass similar obstacles to escape or find food to survive.Whether the entity can use the navigation strategies to complete the detour task in a maze with transparent obstacles,which involves simulating animals' abilities.From the perspective of nematode behavior simulation,this essay aims to explore the specific behavior patterns of the decentralized system.And seek to examine the system's efficiency when changing the decentralized decision-making mechanism and its navigation algorithm.It then conducts a quantitative visual analysis of the connectomes of Caenorhabditis elegans to understand the decentralized decision-making mode of the neural network.The overall content of the study takes the form of follows:a)A basic decentralized decision-making model in a simple environment was established to simulate the movement and chemotaxis of worms.On the basis,according to the actual living environment of organisms,we study its detour ability in the presence of obstacles,found that the applicability of the model becomes worse.Then,this paper proposes a circuitous path algorithm,such decentralized entity still able to bypass obstacles and reach the visible goal,even when the component capacity is insufficient.b)Caenorhabditis elegans is one of the most widely used groups of organisms,due to its relatively simple structure.The objective of this study was to investigate the neural connectors of nematodes and analyze the quantitative connectivity matrix to analyze the control mechanisms related to movement.This matrix covers all the connections of the entire nematode from sensory input to final organ output,which is necessary information for modeling behavior.