Abstract
Recent studies indicate that the local field potential (LFP) carries information about an animal's behavior, but issues regarding whether there are any relationships between the LFP functional networks and behavior tasks as well as whether it is possible to employ LFP network features to decode the behavioral outcome in a single trial remain unresolved. 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 k-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 ± 8%) was significantly higher than that of the power features (61 ± 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network.
Highlights
The local field potential (LFP) is a low-pass filtered signal and it is considered to primarily reflect the sum of the slow local synaptic currents originating from an area around the electrode tip [1]
A dominant highfrequency oscillation was observed around the turning end sensor (TES), which indicates that the high-frequency oscillations in the LFP may be related to the behavior of pigeons
We explored the functional connectivity properties of LFPs recorded from the pigeon nidopallium caudolaterale (NCL) during a goal-directed decision-making task
Summary
The local field potential (LFP) is a low-pass filtered signal and it is considered to primarily reflect the sum of the slow local synaptic currents originating from an area around the electrode tip [1]. The LFP has been shown to reflect sensory and motor-related signals that can be modulated by cognitive processes, and it provides additional information regarding single neuron activity [2]. The LFP appears to be correlated more closely with the BOLD signal measured by fMRI than spike activity [3,4,5]. The LFP has been shown to correspond to spike activity under certain behavioral or perceptual conditions [6]. The LFP is easy to record, even over long periods of time, so it may be an efficient candidate signal for the control of neural prostheses [7]
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