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Published on: May 26, 2023
Increase of spike-LFP coordination in rat prefrontal cortex during working memory
Shuangyan Li1, Mei Ouyang1, Tiaotiao Liu1
1Laboratory of Neurobiology in Medicine, School of Biomedical Engineering, Tianjin Medical University, Tianjin 300070, China.
This study reveals that joint entropy indexes between neural spikes and local field potentials (LFPs) effectively encode working memory (WM). Increased spike-LFP coupling in theta, low gamma, and high gamma bands signifies successful WM task performance.
Area of Science:
- Neuroscience
- Cognitive Neuroscience
- Computational Neuroscience
Background:
- Working memory (WM) involves short-term information maintenance crucial for higher cognitive functions.
- Neural activity is encoded through various signals, including local field potentials (LFPs) and individual neuronal spikes.
- Understanding the coordinated encoding of WM by LFPs and spikes is an emerging research area.
Purpose of the Study:
- To investigate how spike-LFP coupling encodes working memory (WM).
- To analyze the coordination between LFPs and spikes during WM tasks using joint entropy analysis.
Main Methods:
- Multi-channel spikes and LFPs were recorded from rat prefrontal cortex during Y-maze WM tasks.
- Short-time Fourier transform (STFT) analyzed power changes in WM-related LFP frequency bands (theta, low gamma, high gamma).
- Joint entropy indexes (JEIs) were calculated between spike trains and principal components of LFPs.
Main Results:
- LFP power increased in theta (4-12 Hz), low gamma (LG, 30-60 Hz), and high gamma (HG, 60-100 Hz) bands during WM tasks.
- JEIs between spikes and LFP components (theta, LG, HG) significantly increased during correct WM trials.
- Spike-LFP coupling levels were low when rats were in the starting area, indicating task-specific modulation.
Conclusions:
- JEIs between spikes and LFP components effectively encode WM.
- Spike-LFP coupling provides insights into the neural mechanisms of working memory.
- This study highlights the importance of analyzing joint neural signal encoding for understanding cognitive functions.
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