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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.


Visualization of neural trajectories representing multiple single-unit activity over time. (A) Scree plot of the principal components (PCs) obtained from the smoothed firing rate. The PCs were calculated over 10 correct trials for each rat via KPCA. Time slice points calculated from neuronal spike activity of four rats, projected onto state space using the first three PCs. The minimum embedding dimension m = 3 and the optimal time delay τ = 200 ms were applied for time-delay embedding. (B) Shown were 10 individual-trial neural trajectories for each rat. Gray arrows indicated the direction of evolution of neural trajectories during working memory. (C) Trajectory averaged over 10 trials for each rat.
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Figure 3: Visualization of neural trajectories representing multiple single-unit activity over time. (A) Scree plot of the principal components (PCs) obtained from the smoothed firing rate. The PCs were calculated over 10 correct trials for each rat via KPCA. Time slice points calculated from neuronal spike activity of four rats, projected onto state space using the first three PCs. The minimum embedding dimension m = 3 and the optimal time delay τ = 200 ms were applied for time-delay embedding. (B) Shown were 10 individual-trial neural trajectories for each rat. Gray arrows indicated the direction of evolution of neural trajectories during working memory. (C) Trajectory averaged over 10 trials for each rat.

Mentions: The minimum embedding dimension m = 3 and the optimal time delay τ = 200 ms were chosen for time-delay embedding. The cumulative variance explained by the first three PCs is above 70% (79 ± 2, 78 ± 4, 74 ± 1, and 86 ± 2% for the four rats, mean ± SEM) (Figure 3A). As the first three PCs capture the most of the variance, it is appropriate to transform the original data to a low dimensional space defined by the first three PCs to reveal the transient dynamics as neural trajectory. The individual trajectories and averaged trajectory during the working memory task were shown in Figures 3B,C. Each point along the trajectory represented the instantaneous neuronal population activity during working memory. It can be seen quite clearly from the Figure 3B that the trajectories were quite similar on the correct trials across correct trials for four rats. Besides, the trajectories for correct right-turn trials and left-turn trials evolved in a similar way. This suggested that the trajectories contain specific information dependent on neuronal activity during working memory. In this view, a neuronal sequence of activity may be activated during working memory task. The neuronal activity may exhibit different dynamics over time and formed a trajectory in state space.


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)

Visualization of neural trajectories representing multiple single-unit activity over time. (A) Scree plot of the principal components (PCs) obtained from the smoothed firing rate. The PCs were calculated over 10 correct trials for each rat via KPCA. Time slice points calculated from neuronal spike activity of four rats, projected onto state space using the first three PCs. The minimum embedding dimension m = 3 and the optimal time delay τ = 200 ms were applied for time-delay embedding. (B) Shown were 10 individual-trial neural trajectories for each rat. Gray arrows indicated the direction of evolution of neural trajectories during working memory. (C) Trajectory averaged over 10 trials for each rat.
© Copyright Policy
Related In: Results  -  Collection

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Figure 3: Visualization of neural trajectories representing multiple single-unit activity over time. (A) Scree plot of the principal components (PCs) obtained from the smoothed firing rate. The PCs were calculated over 10 correct trials for each rat via KPCA. Time slice points calculated from neuronal spike activity of four rats, projected onto state space using the first three PCs. The minimum embedding dimension m = 3 and the optimal time delay τ = 200 ms were applied for time-delay embedding. (B) Shown were 10 individual-trial neural trajectories for each rat. Gray arrows indicated the direction of evolution of neural trajectories during working memory. (C) Trajectory averaged over 10 trials for each rat.
Mentions: The minimum embedding dimension m = 3 and the optimal time delay τ = 200 ms were chosen for time-delay embedding. The cumulative variance explained by the first three PCs is above 70% (79 ± 2, 78 ± 4, 74 ± 1, and 86 ± 2% for the four rats, mean ± SEM) (Figure 3A). As the first three PCs capture the most of the variance, it is appropriate to transform the original data to a low dimensional space defined by the first three PCs to reveal the transient dynamics as neural trajectory. The individual trajectories and averaged trajectory during the working memory task were shown in Figures 3B,C. Each point along the trajectory represented the instantaneous neuronal population activity during working memory. It can be seen quite clearly from the Figure 3B that the trajectories were quite similar on the correct trials across correct trials for four rats. Besides, the trajectories for correct right-turn trials and left-turn trials evolved in a similar way. This suggested that the trajectories contain specific information dependent on neuronal activity during working memory. In this view, a neuronal sequence of activity may be activated during working memory task. The neuronal activity may exhibit different dynamics over time and formed a trajectory in state space.

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.