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In this study, we developed a network-based method to decode the behavioral outcomes in pigeons by using the functional connectivity strength values among LFPs recorded from the nidopallium caudolaterale (NCL). In our method, the functional connectivity strengths were first computed based on the synchronization likelihood. Second, the strength values were unwrapped into row vectors and their dimensions were then reduced by principal component analysis. Finally, the behavioral outcomes in single trials were decoded using leave-one-out combined with the <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi>k<\/mml:mi><\/mml:mrow><\/mml:math>-nearest neighbor method. The results showed that the LFP functional network based on the gamma-band was related to the goal-directed behavior of pigeons. Moreover, the accuracy of the network features (74 <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:mo>\u00b1<\/mml:mo><\/mml:mrow><\/mml:math> 8%) was significantly higher than that of the power features (61 <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:mo>\u00b1<\/mml:mo><\/mml:mrow><\/mml:math> 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network.<\/jats:p>","DOI":"10.1155\/2018\/3505371","type":"journal-article","created":{"date-parts":[[2018,2,15]],"date-time":"2018-02-15T18:31:16Z","timestamp":1518719476000},"page":"1-13","source":"Crossref","is-referenced-by-count":10,"title":["Decoding Pigeon Behavior Outcomes Using Functional Connections among Local Field Potentials"],"prefix":"10.1155","volume":"2018","author":[{"given":"Yan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Zhengzhou University, Zhengzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Huanghuai University, Zhumadian, Henan, China"},{"name":"Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou 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