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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.


Single-unit activity from four example correct trials during the working memory task. (A) Spike detection and sorting from the recordings. The raw data was filtered (250–7500 Hz) and multi-unit spikes were detected using a preset threshold. Then, single-unit spike trains were isolated from the multi-unit spikes. (B) Raster plots of neuronal activity during the working memory task for 4 rats. For each rat, one correct trial was shown. Each row plotted the response of an individual neuron. Time 0 represented the reference point, marked by the red triangle. (C) Mean firing rates of neuronal population during the working memory task. Data from (B) were used, with 10 correct trials for each rat. Shaded areas indicated the SEM. Time 0 represents the reference point, marked by the red triangle.
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Figure 2: Single-unit activity from four example correct trials during the working memory task. (A) Spike detection and sorting from the recordings. The raw data was filtered (250–7500 Hz) and multi-unit spikes were detected using a preset threshold. Then, single-unit spike trains were isolated from the multi-unit spikes. (B) Raster plots of neuronal activity during the working memory task for 4 rats. For each rat, one correct trial was shown. Each row plotted the response of an individual neuron. Time 0 represented the reference point, marked by the red triangle. (C) Mean firing rates of neuronal population during the working memory task. Data from (B) were used, with 10 correct trials for each rat. Shaded areas indicated the SEM. Time 0 represents the reference point, marked by the red triangle.

Mentions: Spike trains of single neurons were obtained (Figure 2A) from 40 correct trials (four rats, 10 correct trials for each rat). The time course of the working memory task was 3.5 ± 0.21, 3 ± 0.19, 3.3 ± 0.15, 3.7 ± 0.23 s (mean ± SEM) for rat 1, rat 2, rat 3, and rat 4, respectively. Besides, for each rat, the resting-state activity was taken during the inter-trial intervals. A typical example shows the changes in neuronal population activity during the working memory task (Figure 2). The neuronal population firing increased rapidly prior to the reference point and decayed down to baseline level after the reference point (Figure 2B). Then the spike trains were transformed into smooth, continuous-time firing rates with a 200-ms Gaussian kernel. The neuronal population activity profile was shown in Figure 2C. The neuronal population firing rates increased, peaked around 500 ms prior to the reference point and then declined to baseline.


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)

Single-unit activity from four example correct trials during the working memory task. (A) Spike detection and sorting from the recordings. The raw data was filtered (250–7500 Hz) and multi-unit spikes were detected using a preset threshold. Then, single-unit spike trains were isolated from the multi-unit spikes. (B) Raster plots of neuronal activity during the working memory task for 4 rats. For each rat, one correct trial was shown. Each row plotted the response of an individual neuron. Time 0 represented the reference point, marked by the red triangle. (C) Mean firing rates of neuronal population during the working memory task. Data from (B) were used, with 10 correct trials for each rat. Shaded areas indicated the SEM. Time 0 represents the reference point, marked by the red triangle.
© Copyright Policy
Related In: Results  -  Collection

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Figure 2: Single-unit activity from four example correct trials during the working memory task. (A) Spike detection and sorting from the recordings. The raw data was filtered (250–7500 Hz) and multi-unit spikes were detected using a preset threshold. Then, single-unit spike trains were isolated from the multi-unit spikes. (B) Raster plots of neuronal activity during the working memory task for 4 rats. For each rat, one correct trial was shown. Each row plotted the response of an individual neuron. Time 0 represented the reference point, marked by the red triangle. (C) Mean firing rates of neuronal population during the working memory task. Data from (B) were used, with 10 correct trials for each rat. Shaded areas indicated the SEM. Time 0 represents the reference point, marked by the red triangle.
Mentions: Spike trains of single neurons were obtained (Figure 2A) from 40 correct trials (four rats, 10 correct trials for each rat). The time course of the working memory task was 3.5 ± 0.21, 3 ± 0.19, 3.3 ± 0.15, 3.7 ± 0.23 s (mean ± SEM) for rat 1, rat 2, rat 3, and rat 4, respectively. Besides, for each rat, the resting-state activity was taken during the inter-trial intervals. A typical example shows the changes in neuronal population activity during the working memory task (Figure 2). The neuronal population firing increased rapidly prior to the reference point and decayed down to baseline level after the reference point (Figure 2B). Then the spike trains were transformed into smooth, continuous-time firing rates with a 200-ms Gaussian kernel. The neuronal population activity profile was shown in Figure 2C. The neuronal population firing rates increased, peaked around 500 ms prior to the reference point and then declined to baseline.

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.