| Autor: |
Zhang X; Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China., Yi H; Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China., Bai W; Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China., Tian X; Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China. |
| Abstrakt: |
Working memory plays an important role in complex cognitive tasks. A popular theoretical view is that transient properties of neuronal dynamics underlie cognitive processing. The question raised here as to how the transient dynamics evolve in working memory. To address this issue, we investigated the multiple single-unit activity dynamics in rat medial prefrontal cortex (mPFC) during a Y-maze working memory task. The approach worked by reconstructing state space from delays of the original single-unit firing rate variables, which were further analyzed using kernel principal component analysis (KPCA). Then the neural trajectories were obtained to visualize the multiple single-unit activity. Furthermore, the maximal Lyapunov exponent (MLE) was calculated to quantitatively evaluate the neural trajectories during the working memory task. The results showed that the neuronal activity produced stable and reproducible neural trajectories in the correct trials while showed irregular trajectories in the incorrect trials, which may establish a link between the neurocognitive process and behavioral performance in working memory. The MLEs significantly increased during working memory in the correctly performed trials, indicating an increased divergence of the neural trajectories. In the incorrect trials, the MLEs were nearly zero and remained unchanged during the task. Taken together, the trial-specific neural trajectory provides an effective way to track the instantaneous state of the neuronal population during the working memory task and offers valuable insights into working memory function. The MLE describes the changes of neural dynamics in working memory and may reflect different neuronal population states in working memory. |
