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Rapid learning in visual cortical networks.

Wang Y, Dragoi V - Elife (2015)

Bottom Line: We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity.More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning.These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology and Anatomy, The University of Texas Medical School at Houston, Houston, United States.

ABSTRACT
Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of minutes. Multiple single units and LFP responses were recorded as monkeys improved their performance in an image discrimination task. We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity. More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning. These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning.

No MeSH data available.


Related in: MedlinePlus

Rapid learning increases spike-LFP theta synchronization.(A, B) Spike-field coherence (SFC) from two example pairs of recording sites during blocks of learning. Each row shows the mean SFC in the low-frequency bands for the two example pairs in a particular block. (C) SPC—population average. The two panels show the population average (median change) of normalized SFC change in blocks 2–4 relative to block 1 throughout the trial. For each block, SFC was calculated within a 300-ms window sliding every 10 ms, and then the results were normalized for each session. The left panel shows SFC changes for the low frequencies, and the right panel represents frequencies within the gamma band. The x-axis represents time relative to the onset of the target stimulus. The two white vertical bars mark the onset of the target and test stimuli. The horizontal bars represent the time interval when the target and test stimuli are presented.DOI:http://dx.doi.org/10.7554/eLife.08417.005
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fig2: Rapid learning increases spike-LFP theta synchronization.(A, B) Spike-field coherence (SFC) from two example pairs of recording sites during blocks of learning. Each row shows the mean SFC in the low-frequency bands for the two example pairs in a particular block. (C) SPC—population average. The two panels show the population average (median change) of normalized SFC change in blocks 2–4 relative to block 1 throughout the trial. For each block, SFC was calculated within a 300-ms window sliding every 10 ms, and then the results were normalized for each session. The left panel shows SFC changes for the low frequencies, and the right panel represents frequencies within the gamma band. The x-axis represents time relative to the onset of the target stimulus. The two white vertical bars mark the onset of the target and test stimuli. The horizontal bars represent the time interval when the target and test stimuli are presented.DOI:http://dx.doi.org/10.7554/eLife.08417.005

Mentions: Next, we directly tested our hypothesis that the improvement in behavioral performance during learning is accompanied by synchronous firing of neurons with their neighbors. We thus examined the timing relationship between the spikes of single neurons and the ongoing LFP oscillation by quantifying the spike-field coherence (SFC). First, we computed the spike-triggered average (STA, triggered from same number of subsampled spikes in order to avoid bias) by averaging the LFP signal within a window centered ±150 ms on each elicited spike in each block. Second, we computed SFC by dividing the power spectrum of the STA to the average of all power spectra of the LFP segments used to obtain the STA. SFC varies as a function of frequency and yields values between 0 and 1. The larger the SFC, the more accurately the spikes follow a particular phase of this frequency. We calculated the SFC separately for each block (for each single-unit-LFP pair, n = 625). Figure 2A,B shows two examples of cross-channel SFC early in the session (block 1) and during learning (blocks 2–4). Clearly, rapid learning is associated with an increase in low-frequency SFC (particularly in the theta band, 4–8 Hz) during the intervals when the two stimuli are presented (0–300 ms and 1300–1600 ms. In contrast, SFC at higher frequency bands (alpha, beta, and gamma bands) was either unchanged or slightly decreased during the time course of learning. These results were confirmed for the population of spike-LFP pairs (Figure 2C)—learning was associated with an increase in theta SFC during the intervals when the two stimuli are presented (p < 0.05, Wilcoxon signed-rank test, by comparing theta SFC in blocks 2–4 vs block 1 for time intervals 150–270 ms and 1430–1550 ms; SFC was calculated within a 300-ms window sliding every 10 ms), whereas coherence in the high-frequency bands did not change across blocks of learning (p > 0.05).10.7554/eLife.08417.005Figure 2.Rapid learning increases spike-LFP theta synchronization.


Rapid learning in visual cortical networks.

Wang Y, Dragoi V - Elife (2015)

Rapid learning increases spike-LFP theta synchronization.(A, B) Spike-field coherence (SFC) from two example pairs of recording sites during blocks of learning. Each row shows the mean SFC in the low-frequency bands for the two example pairs in a particular block. (C) SPC—population average. The two panels show the population average (median change) of normalized SFC change in blocks 2–4 relative to block 1 throughout the trial. For each block, SFC was calculated within a 300-ms window sliding every 10 ms, and then the results were normalized for each session. The left panel shows SFC changes for the low frequencies, and the right panel represents frequencies within the gamma band. The x-axis represents time relative to the onset of the target stimulus. The two white vertical bars mark the onset of the target and test stimuli. The horizontal bars represent the time interval when the target and test stimuli are presented.DOI:http://dx.doi.org/10.7554/eLife.08417.005
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4588715&req=5

fig2: Rapid learning increases spike-LFP theta synchronization.(A, B) Spike-field coherence (SFC) from two example pairs of recording sites during blocks of learning. Each row shows the mean SFC in the low-frequency bands for the two example pairs in a particular block. (C) SPC—population average. The two panels show the population average (median change) of normalized SFC change in blocks 2–4 relative to block 1 throughout the trial. For each block, SFC was calculated within a 300-ms window sliding every 10 ms, and then the results were normalized for each session. The left panel shows SFC changes for the low frequencies, and the right panel represents frequencies within the gamma band. The x-axis represents time relative to the onset of the target stimulus. The two white vertical bars mark the onset of the target and test stimuli. The horizontal bars represent the time interval when the target and test stimuli are presented.DOI:http://dx.doi.org/10.7554/eLife.08417.005
Mentions: Next, we directly tested our hypothesis that the improvement in behavioral performance during learning is accompanied by synchronous firing of neurons with their neighbors. We thus examined the timing relationship between the spikes of single neurons and the ongoing LFP oscillation by quantifying the spike-field coherence (SFC). First, we computed the spike-triggered average (STA, triggered from same number of subsampled spikes in order to avoid bias) by averaging the LFP signal within a window centered ±150 ms on each elicited spike in each block. Second, we computed SFC by dividing the power spectrum of the STA to the average of all power spectra of the LFP segments used to obtain the STA. SFC varies as a function of frequency and yields values between 0 and 1. The larger the SFC, the more accurately the spikes follow a particular phase of this frequency. We calculated the SFC separately for each block (for each single-unit-LFP pair, n = 625). Figure 2A,B shows two examples of cross-channel SFC early in the session (block 1) and during learning (blocks 2–4). Clearly, rapid learning is associated with an increase in low-frequency SFC (particularly in the theta band, 4–8 Hz) during the intervals when the two stimuli are presented (0–300 ms and 1300–1600 ms. In contrast, SFC at higher frequency bands (alpha, beta, and gamma bands) was either unchanged or slightly decreased during the time course of learning. These results were confirmed for the population of spike-LFP pairs (Figure 2C)—learning was associated with an increase in theta SFC during the intervals when the two stimuli are presented (p < 0.05, Wilcoxon signed-rank test, by comparing theta SFC in blocks 2–4 vs block 1 for time intervals 150–270 ms and 1430–1550 ms; SFC was calculated within a 300-ms window sliding every 10 ms), whereas coherence in the high-frequency bands did not change across blocks of learning (p > 0.05).10.7554/eLife.08417.005Figure 2.Rapid learning increases spike-LFP theta synchronization.

Bottom Line: We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity.More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning.These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology and Anatomy, The University of Texas Medical School at Houston, Houston, United States.

ABSTRACT
Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of minutes. Multiple single units and LFP responses were recorded as monkeys improved their performance in an image discrimination task. We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity. More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning. These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning.

No MeSH data available.


Related in: MedlinePlus