Neural Correlates of Flight Acceleration in Pigeons: Gamma-Band Activity and Local Functional Network Dynamics in the AId Region

Abstract

Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration-exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems.

Keywords: flight acceleration; gamma; local field potentials; local functional network; pigeon.

Figure 1

Outdoor free-flight experiment setup and synchronous acquisition of GPS, posture, and local field potential (LFP) data, along with the architecture of the decoder model. (a) The pigeon carrying the equipment flies freely outdoors. (b) Surgical implantation of the recording electrode array and postoperative histological verification of the implantation site. (c) Acceleration distribution and kernel density estimation of a representative pigeon flight experiment. (d) IMU data from a representative flight experiment, with acceleration segmented into different states for further analysis. (e) GPS trajectories from three outdoor flight experiments in pigeons. (f) Block diagram illustrating the integration and processing of GPS data, flight posture information, and LFPs in the decoding framework. (g) The architecture of the DNN-based decoding model (h) The architecture of the CNN-based decoding model.

Figure 2

Results of wingbeat artifact removal. (a) Correlation measurement of each IMF with wingbeat noise. (b) Time–frequency representations of the original and denoised neural signals. (c) Time domain plot of the original and denoised neural signals.

Figure 3

Time–Frequency Domain Characterization of AId Neural Signals During Flight Acceleration. (a) Statistical analysis of the PSD proportions of AId neural signals across different flight states. (b) Proportion of PSD in the gamma band across valid flight trials under different acceleration states. (c) Time–frequency characteristics of gamma-band neural activity in the AId under different flight states. “*” indicates $p$ < 0.05, “**” indicates $p$ < 0.01, “***” indicates $p$ < 0.001, “****” indicates $p$ < 0.0001, ns-not significant.

Figure 4

Changes in gamma-band PSD proportion in the AId region across different flight acceleration levels in pigeons. Each subplot shows the scatter distribution and fitted regression curve for an individual subject, revealing a decreasing trend in gamma-band activity with increasing acceleration.

Figure 5

Changes in gamma-band functional connectivity of the AId region across different flight states in pigeons. (a) Coherence matrix heatmaps of gamma-band LFP signals across different flight states from a single flight trial. (b) Statistical analysis of the clustering coefficients of gamma-band functional networks under different flight states. “***” indicates $p$ < 0.001, “****” indicates $p$ < 0.0001.

Figure 6

Dynamic changes in the topological properties of gamma-band functional networks in the AId region under different flight acceleration levels in pigeons. Each subplot presents the relationship between acceleration and the clustering coefficient, with second-order polynomial regression indicating non-linear modulation patterns.