IMR Press / JIN / Volume 23 / Issue 4 / DOI: 10.31083/j.jin2304072
Open Access Original Research
Decoding Typical Flight States Based on Neural Signals from the Midbrain Motor Nuclei of Pigeons
Long Yang1,2,†Erteng Ma1,2,†Lifang Yang1,2Mengmeng Li1,2,*Zhigang Shang1,2,*Liaofeng Wang1,2Zuohao Ma1,2Jiajia Li1,2
Show Less
1 School of Electrical and Information Engineering, Zhengzhou University, 450001 Zhengzhou, Henan, China
2 Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, 450001 Zhengzhou, Henan, China
*Correspondence: (Mengmeng Li); (Zhigang Shang)
These authors contributed equally.
J. Integr. Neurosci. 2024, 23(4), 72;
Submitted: 27 September 2023 | Revised: 14 November 2023 | Accepted: 21 November 2023 | Published: 3 April 2024
Copyright: © 2024 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

Background: Exploring the neural encoding mechanism and decoding of motion state switching during flight can advance our knowledge of avian behavior control and contribute to the development of avian robots. However, limited acquisition equipment and neural signal quality have posed challenges, thus we understand little about the neural mechanisms of avian flight. Methods: We used chronically implanted micro-electrode arrays to record the local field potentials (LFPs) in the formation reticularis medialis mesencephali (FRM) of pigeons during various motion states in their natural outdoor flight. Subsequently, coherence-based functional connectivity networks under different bands were constructed and the topological features were extracted. Finally, we used a support vector machine model to decode different flight states. Results: Our findings indicate that the gamma band (80–150 Hz) in the FRM exhibits significant power for identifying different states in pigeons. Specifically, the avian brain transmitted flight related information more efficiently during the accelerated take-off or decelerated landing states, compared with the uniform flight and baseline states. Finally, we achieved a best average accuracy of 0.86 using the connectivity features in the 80–150 Hz band and 0.89 using the fused features for state decoding. Conclusions: Our results open up possibilities for further research into the neural mechanism of avian flight and contribute to the understanding of flight behavior control in birds.

flight state
functional connectivity
GZC20232447/National Postdoctoral Researcher Program
62301496/National Natural Science Foundation of China
232102210098/Key Scientific and Technological Projects of Henan Province
222102310223/Key Scientific and Technological Projects of Henan Province
Fig. 1.
Back to top