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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

Changes in spike-LFP coherence (SFC, spike-field coherence) during the delay period.Median change in SFC (blocks 2–4 vs block 1) during the delay period for each frequency band. The 1000-ms delay period was divided into three 333-ms windows referred as early, middle, and late. *** denotes p < 0.001; ** denotes p < 0.01.DOI:http://dx.doi.org/10.7554/eLife.08417.008
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fig5: Changes in spike-LFP coherence (SFC, spike-field coherence) during the delay period.Median change in SFC (blocks 2–4 vs block 1) during the delay period for each frequency band. The 1000-ms delay period was divided into three 333-ms windows referred as early, middle, and late. *** denotes p < 0.001; ** denotes p < 0.01.DOI:http://dx.doi.org/10.7554/eLife.08417.008

Mentions: Previous work in area V4 has shown that working memory influences theta power and the phase synchronization between spikes and LFPs (Lee et al., 2005) and theta coupling between areas V4 and prefrontal cortex. To test the possibility that learning might be accompanied by a change in spike-LFP coherence, particularly in the theta band, we calculated SFC in the delay period between the target and test by dividing the delay into three time windows (early, middle, late), each of identical length to the stimulus period. However, we found that the median SFC in block 1 was not significantly different from that in blocks 2–4 in any of the windows of the delay period (Figure 5, p > 0.05, Wilcoxon signed-rank test for each delay interval). This indicates that the improvement in behavioral performance during learning is unlikely to be explained by a change in the working memory load when novel images are presented during training.10.7554/eLife.08417.008Figure 5.Changes in spike-LFP coherence (SFC, spike-field coherence) during the delay period.


Rapid learning in visual cortical networks.

Wang Y, Dragoi V - Elife (2015)

Changes in spike-LFP coherence (SFC, spike-field coherence) during the delay period.Median change in SFC (blocks 2–4 vs block 1) during the delay period for each frequency band. The 1000-ms delay period was divided into three 333-ms windows referred as early, middle, and late. *** denotes p < 0.001; ** denotes p < 0.01.DOI:http://dx.doi.org/10.7554/eLife.08417.008
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4588715&req=5

fig5: Changes in spike-LFP coherence (SFC, spike-field coherence) during the delay period.Median change in SFC (blocks 2–4 vs block 1) during the delay period for each frequency band. The 1000-ms delay period was divided into three 333-ms windows referred as early, middle, and late. *** denotes p < 0.001; ** denotes p < 0.01.DOI:http://dx.doi.org/10.7554/eLife.08417.008
Mentions: Previous work in area V4 has shown that working memory influences theta power and the phase synchronization between spikes and LFPs (Lee et al., 2005) and theta coupling between areas V4 and prefrontal cortex. To test the possibility that learning might be accompanied by a change in spike-LFP coherence, particularly in the theta band, we calculated SFC in the delay period between the target and test by dividing the delay into three time windows (early, middle, late), each of identical length to the stimulus period. However, we found that the median SFC in block 1 was not significantly different from that in blocks 2–4 in any of the windows of the delay period (Figure 5, p > 0.05, Wilcoxon signed-rank test for each delay interval). This indicates that the improvement in behavioral performance during learning is unlikely to be explained by a change in the working memory load when novel images are presented during training.10.7554/eLife.08417.008Figure 5.Changes in spike-LFP coherence (SFC, spike-field coherence) during the delay period.

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