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Characterization of Neural Network Connectivity and Modularity of Pigeon Nidopallium Caudolaterale During Target Detection.

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Titel: Characterization of Neural Network Connectivity and Modularity of Pigeon Nidopallium Caudolaterale During Target Detection.
Autoren: Wang, Zhizhong, Wang, Hu, Zhu, Juncai, Zhao, Deyu, Wang, Rui, Ma, Zhuangzhuang, Zeng, Shaoju, Wang, Jiangtao
Quelle: Animals (2076-2615); Feb2025, Vol. 15 Issue 4, p609, 19p
Schlagwörter: PREFRONTAL cortex, DISTRIBUTED computing, ARTIFICIAL intelligence, FUNCTIONAL connectivity, PIGEONS
Abstract: Simple Summary: Efficient target detection is essential for animals to survive in complex and changing natural environments. Birds are particularly skilled at this ability, and a specific part of their brain, the nidopallium caudolaterale, plays a key role in detecting targets. Scientists often study this brain region to understand how birds detect targets. However, it is still unclear how network features of the nidopallium caudolaterale region change dynamically during this process. In this study, we used pigeons and placed them in a maze to explore changes in their neural network features before and after detecting a target. The results showed that when pigeons detected the target, the connections within their neural network features became stronger, and the network became less divided into separate modules. This suggests that pigeons shift their brain activity from widespread processing to a more efficient and focused mode when detecting targets. Our findings support the idea that the nidopallium caudolaterale brain region is crucial for target detection in birds and highlight the importance of its network features in processing target information. This research enhances our understanding of bird cognition and may benefit the development of artificial target detection systems, aiding fields such as robotics and artificial intelligence. Accurate target detection in natural environments is an important function of the visual systems of vertebrates and has a direct impact on animal survival and environmental adaptation. Existing studies have shown that the mammalian prefrontal cortex plays an important role in target detection. However, target detection mechanisms in brain regions similar to other species, such as the avian nidopallium caudolaterale, have not been well studied. Here, we selected pigeons, known for their excellent target detection ability, as an animal model and studied the dynamic changes in the nidopallium caudolaterale neural network features while they performed a target detection task in a maze. The results showed that the average node degree increased significantly during the target detection process while modularity decreased significantly. This indicated that functional connectivity in pigeon brains was enhanced during the task execution, the frequency of brain interactions increased, and the neural network shifted from distributed processing to more efficient integrated processing. The decoding results based on the average node degree and modularity and the combination of both showed that the accuracy of target decoding corresponding to the combination of both was higher. Taken together, our results confirmed the important role of the above properties for encoding target information. We provided evidence to support the view that the NCL is critical for target detection tasks and that studying key features of its neural network provides a powerful tool for revealing the functional state of the brain. [ABSTRACT FROM AUTHOR]
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Datenbank: Biomedical Index
Beschreibung
Abstract:Simple Summary: Efficient target detection is essential for animals to survive in complex and changing natural environments. Birds are particularly skilled at this ability, and a specific part of their brain, the nidopallium caudolaterale, plays a key role in detecting targets. Scientists often study this brain region to understand how birds detect targets. However, it is still unclear how network features of the nidopallium caudolaterale region change dynamically during this process. In this study, we used pigeons and placed them in a maze to explore changes in their neural network features before and after detecting a target. The results showed that when pigeons detected the target, the connections within their neural network features became stronger, and the network became less divided into separate modules. This suggests that pigeons shift their brain activity from widespread processing to a more efficient and focused mode when detecting targets. Our findings support the idea that the nidopallium caudolaterale brain region is crucial for target detection in birds and highlight the importance of its network features in processing target information. This research enhances our understanding of bird cognition and may benefit the development of artificial target detection systems, aiding fields such as robotics and artificial intelligence. Accurate target detection in natural environments is an important function of the visual systems of vertebrates and has a direct impact on animal survival and environmental adaptation. Existing studies have shown that the mammalian prefrontal cortex plays an important role in target detection. However, target detection mechanisms in brain regions similar to other species, such as the avian nidopallium caudolaterale, have not been well studied. Here, we selected pigeons, known for their excellent target detection ability, as an animal model and studied the dynamic changes in the nidopallium caudolaterale neural network features while they performed a target detection task in a maze. The results showed that the average node degree increased significantly during the target detection process while modularity decreased significantly. This indicated that functional connectivity in pigeon brains was enhanced during the task execution, the frequency of brain interactions increased, and the neural network shifted from distributed processing to more efficient integrated processing. The decoding results based on the average node degree and modularity and the combination of both showed that the accuracy of target decoding corresponding to the combination of both was higher. Taken together, our results confirmed the important role of the above properties for encoding target information. We provided evidence to support the view that the NCL is critical for target detection tasks and that studying key features of its neural network provides a powerful tool for revealing the functional state of the brain. [ABSTRACT FROM AUTHOR]
ISSN:20762615
DOI:10.3390/ani15040609