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Dynamic trajectory of multiple single-unit activity during working memory task in rats.

Zhang X, Yi H, Bai W, Tian X - Front Comput Neurosci (2015)

Bottom Line: The question raised here as to how the transient dynamics evolve in working memory.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China.

ABSTRACT
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.

No MeSH data available.


The comparison of MLEs of dynamic trajectories for 20 correct right-turn trials and for 20 left-turn trials. There was no significant difference for the two correct cases (P > 0.05).
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Figure 6: The comparison of MLEs of dynamic trajectories for 20 correct right-turn trials and for 20 left-turn trials. There was no significant difference for the two correct cases (P > 0.05).

Mentions: To test whether the trajectory pattern and MLE were significant relative to working memory dynamics. We constructed the trajectories and calculated the corresponding MLE for incorrect trials. Neural trajectories over eight incorrect trials (from rat 1) were shown in Figure 5A. Accordingly, the changes of the MLEs in the incorrect trials were shown in Figure 5B. The neural trajectories during the same duration (duration: the time bin corresponding to the peak of the MLEs) in the correct and incorrect trials were further compared (Figure 5C). The peak of the MLEs in the correct trials was significantly higher than those in the incorrect trials (2.4860 ± 0.2882 vs. 0.1486 ± 0.1095, ***P < 0.001). Furthermore, the question arises here, whether the dynamics of trajectories have any difference for correct right-turn trials and left-turn trials? To answer the above question, we calculated the MLE as a qualitative description for trajectory dynamics, the MLEs were compared for the above two conditions, the results were shown in Figure 6, there was no significant difference (2.5092 ± 0.3739 vs. 2.4786 ± 0.3683, P > 0.05).


Dynamic trajectory of multiple single-unit activity during working memory task in rats.

Zhang X, Yi H, Bai W, Tian X - Front Comput Neurosci (2015)

The comparison of MLEs of dynamic trajectories for 20 correct right-turn trials and for 20 left-turn trials. There was no significant difference for the two correct cases (P > 0.05).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585230&req=5

Figure 6: The comparison of MLEs of dynamic trajectories for 20 correct right-turn trials and for 20 left-turn trials. There was no significant difference for the two correct cases (P > 0.05).
Mentions: To test whether the trajectory pattern and MLE were significant relative to working memory dynamics. We constructed the trajectories and calculated the corresponding MLE for incorrect trials. Neural trajectories over eight incorrect trials (from rat 1) were shown in Figure 5A. Accordingly, the changes of the MLEs in the incorrect trials were shown in Figure 5B. The neural trajectories during the same duration (duration: the time bin corresponding to the peak of the MLEs) in the correct and incorrect trials were further compared (Figure 5C). The peak of the MLEs in the correct trials was significantly higher than those in the incorrect trials (2.4860 ± 0.2882 vs. 0.1486 ± 0.1095, ***P < 0.001). Furthermore, the question arises here, whether the dynamics of trajectories have any difference for correct right-turn trials and left-turn trials? To answer the above question, we calculated the MLE as a qualitative description for trajectory dynamics, the MLEs were compared for the above two conditions, the results were shown in Figure 6, there was no significant difference (2.5092 ± 0.3739 vs. 2.4786 ± 0.3683, P > 0.05).

Bottom Line: The question raised here as to how the transient dynamics evolve in working memory.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, School of Biomedical Engineering and Technology, Tianjin Medical University Tianjin, China.

ABSTRACT
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.

No MeSH data available.