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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.


Evolution of the maximal Lyapunov exponent (MLE) of neural trajectories during the working memory task. (A) Changes of the MLEs during the working memory task in the correct trials (averaged over 10 trials for each rat). (B) MLEs in working memory and resting state. The MLEs in working memory state were significantly higher than those in resting state (averaged over 40 correct trials from four rats, ***P < 0.001).
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Figure 4: Evolution of the maximal Lyapunov exponent (MLE) of neural trajectories during the working memory task. (A) Changes of the MLEs during the working memory task in the correct trials (averaged over 10 trials for each rat). (B) MLEs in working memory and resting state. The MLEs in working memory state were significantly higher than those in resting state (averaged over 40 correct trials from four rats, ***P < 0.001).

Mentions: To quantify these qualitative observations from a dynamics perspective, we calculated the MLE for four rats. The whole time course of the working memory task and resting state were divided into non-overlapping 500 ms time bins, respectively. The MLEs during the working memory task were calculated in each time bin, combining the individual trajectories with the averaged trajectory. The results were shown in Figure 4. The MLEs changed consistently across the four rats, increased obviously and peaked prior to the reference point, and followed by a steady decline (Figure 4A). Then, we compared the neuronal activity at working memory state (time duration: pre-0.5 s and post-0.5 s the peak of the MLEs) with that at the resting-state (40 correct trials from four rats, mean ± SEM, Figure 4B). The MLEs during working memory were significantly higher than those in the resting-state (2.3970 ± 0.1394 vs. 0.0051 ± 0.0003, ***P < 0.001).


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)

Evolution of the maximal Lyapunov exponent (MLE) of neural trajectories during the working memory task. (A) Changes of the MLEs during the working memory task in the correct trials (averaged over 10 trials for each rat). (B) MLEs in working memory and resting state. The MLEs in working memory state were significantly higher than those in resting state (averaged over 40 correct trials from four rats, ***P < 0.001).
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Related In: Results  -  Collection

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Figure 4: Evolution of the maximal Lyapunov exponent (MLE) of neural trajectories during the working memory task. (A) Changes of the MLEs during the working memory task in the correct trials (averaged over 10 trials for each rat). (B) MLEs in working memory and resting state. The MLEs in working memory state were significantly higher than those in resting state (averaged over 40 correct trials from four rats, ***P < 0.001).
Mentions: To quantify these qualitative observations from a dynamics perspective, we calculated the MLE for four rats. The whole time course of the working memory task and resting state were divided into non-overlapping 500 ms time bins, respectively. The MLEs during the working memory task were calculated in each time bin, combining the individual trajectories with the averaged trajectory. The results were shown in Figure 4. The MLEs changed consistently across the four rats, increased obviously and peaked prior to the reference point, and followed by a steady decline (Figure 4A). Then, we compared the neuronal activity at working memory state (time duration: pre-0.5 s and post-0.5 s the peak of the MLEs) with that at the resting-state (40 correct trials from four rats, mean ± SEM, Figure 4B). The MLEs during working memory were significantly higher than those in the resting-state (2.3970 ± 0.1394 vs. 0.0051 ± 0.0003, ***P < 0.001).

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.