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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 during learning.(A) Relative change of mean SFC in each block and frequency band. The error bars represent s.e.m. across spike-LFP pairs. Asterisks for each point show the statistical significance of the difference between SFC in each of blocks 2, 3, and 4 relative to block 1 in each frequency band. (B) Distribution of mean theta SFC values in blocks 2–4 vs block 1. Color indicates the number of spike-LFP pairs measured in blocks 2–4 vs block 1 in 0.02 bins. (C) Block-by-block change in theta SFC relative to block 1 for each monkey. Error bars represent s.e.m. (* represents p < 0.05; *** represents p < 0.001). (D) Relative change in theta SFC when the pairs originating from the same electrode are included (blue, all pairs) or excluded (red, different electrodes). The error bars represent s.e.m. (E) Near pairs (electrode distance <2 mm): Kruskal–Wallis test showed no significant changes induced by learning in alpha, beta, and gamma bands (p = 0.09, 0.19, and 0.12, respectively), but a significant increase in theta band (p = 2.29 10−5). (F) Far pairs (electrode distance >2 mm): Kruskal–Wallis test showed no significant changes in alpha, beta, and gamma bands (p = 0.58, 0.10, and 0.13 respectively), but a significant increase in theta band (p = 1.73 10−6).DOI:http://dx.doi.org/10.7554/eLife.08417.006
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fig3: Changes in spike-LFP coherence during learning.(A) Relative change of mean SFC in each block and frequency band. The error bars represent s.e.m. across spike-LFP pairs. Asterisks for each point show the statistical significance of the difference between SFC in each of blocks 2, 3, and 4 relative to block 1 in each frequency band. (B) Distribution of mean theta SFC values in blocks 2–4 vs block 1. Color indicates the number of spike-LFP pairs measured in blocks 2–4 vs block 1 in 0.02 bins. (C) Block-by-block change in theta SFC relative to block 1 for each monkey. Error bars represent s.e.m. (* represents p < 0.05; *** represents p < 0.001). (D) Relative change in theta SFC when the pairs originating from the same electrode are included (blue, all pairs) or excluded (red, different electrodes). The error bars represent s.e.m. (E) Near pairs (electrode distance <2 mm): Kruskal–Wallis test showed no significant changes induced by learning in alpha, beta, and gamma bands (p = 0.09, 0.19, and 0.12, respectively), but a significant increase in theta band (p = 2.29 10−5). (F) Far pairs (electrode distance >2 mm): Kruskal–Wallis test showed no significant changes in alpha, beta, and gamma bands (p = 0.58, 0.10, and 0.13 respectively), but a significant increase in theta band (p = 1.73 10−6).DOI:http://dx.doi.org/10.7554/eLife.08417.006

Mentions: Previous studies have reported synchrony modulation mainly during the sustained neuronal response (Gieselmann and Thiele, 2008). Therefore, we focused our SFC analysis between 150 and 350 ms after stimulus onset since the transient spike responses, which are highly synchronized to stimulus onset, might possibly induce artifacts related to event-related synchrony. In addition, since neurons have different strengths of their stimulus onset transients, and the SFC measure is sensitive to spike counts, focusing the analysis on the period immediately following stimulus presentation might contaminate our measure of spike-LFP synchronization. We thus computed the mean SFC values (across all pairs and sessions) and found that whereas SFC in the higher frequency bands showed no significant change across blocks (Kruskal–Wallis, p > 0.2), theta-band SFC was significantly increased during the time course of learning (Figure 3A; n = 625 pairs, comparing blocks 2–4 with block 1, Kruskal–Wallis, p < 10−7). Figure 3B shows the distribution of SFC values for all the pairs in our population and reveals that learning is accompanied by a pronounced increase in theta SFC in blocks 2–4 relative to block 1 (p < 0.05, t-test). Furthermore, post hoc analysis shows a significant SFC increase in each of blocks 2, 3, and 4 relative to block 1 (p < 0.05; the median theta SFC was increased by 34%). The increase in theta SFC during learning was statistically significant in both animals (Figure 3C, p < 0.001, Kruskal–Wallis test, post hoc analysis).10.7554/eLife.08417.006Figure 3.Changes in spike-LFP coherence during learning.


Rapid learning in visual cortical networks.

Wang Y, Dragoi V - Elife (2015)

Changes in spike-LFP coherence during learning.(A) Relative change of mean SFC in each block and frequency band. The error bars represent s.e.m. across spike-LFP pairs. Asterisks for each point show the statistical significance of the difference between SFC in each of blocks 2, 3, and 4 relative to block 1 in each frequency band. (B) Distribution of mean theta SFC values in blocks 2–4 vs block 1. Color indicates the number of spike-LFP pairs measured in blocks 2–4 vs block 1 in 0.02 bins. (C) Block-by-block change in theta SFC relative to block 1 for each monkey. Error bars represent s.e.m. (* represents p < 0.05; *** represents p < 0.001). (D) Relative change in theta SFC when the pairs originating from the same electrode are included (blue, all pairs) or excluded (red, different electrodes). The error bars represent s.e.m. (E) Near pairs (electrode distance <2 mm): Kruskal–Wallis test showed no significant changes induced by learning in alpha, beta, and gamma bands (p = 0.09, 0.19, and 0.12, respectively), but a significant increase in theta band (p = 2.29 10−5). (F) Far pairs (electrode distance >2 mm): Kruskal–Wallis test showed no significant changes in alpha, beta, and gamma bands (p = 0.58, 0.10, and 0.13 respectively), but a significant increase in theta band (p = 1.73 10−6).DOI:http://dx.doi.org/10.7554/eLife.08417.006
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fig3: Changes in spike-LFP coherence during learning.(A) Relative change of mean SFC in each block and frequency band. The error bars represent s.e.m. across spike-LFP pairs. Asterisks for each point show the statistical significance of the difference between SFC in each of blocks 2, 3, and 4 relative to block 1 in each frequency band. (B) Distribution of mean theta SFC values in blocks 2–4 vs block 1. Color indicates the number of spike-LFP pairs measured in blocks 2–4 vs block 1 in 0.02 bins. (C) Block-by-block change in theta SFC relative to block 1 for each monkey. Error bars represent s.e.m. (* represents p < 0.05; *** represents p < 0.001). (D) Relative change in theta SFC when the pairs originating from the same electrode are included (blue, all pairs) or excluded (red, different electrodes). The error bars represent s.e.m. (E) Near pairs (electrode distance <2 mm): Kruskal–Wallis test showed no significant changes induced by learning in alpha, beta, and gamma bands (p = 0.09, 0.19, and 0.12, respectively), but a significant increase in theta band (p = 2.29 10−5). (F) Far pairs (electrode distance >2 mm): Kruskal–Wallis test showed no significant changes in alpha, beta, and gamma bands (p = 0.58, 0.10, and 0.13 respectively), but a significant increase in theta band (p = 1.73 10−6).DOI:http://dx.doi.org/10.7554/eLife.08417.006
Mentions: Previous studies have reported synchrony modulation mainly during the sustained neuronal response (Gieselmann and Thiele, 2008). Therefore, we focused our SFC analysis between 150 and 350 ms after stimulus onset since the transient spike responses, which are highly synchronized to stimulus onset, might possibly induce artifacts related to event-related synchrony. In addition, since neurons have different strengths of their stimulus onset transients, and the SFC measure is sensitive to spike counts, focusing the analysis on the period immediately following stimulus presentation might contaminate our measure of spike-LFP synchronization. We thus computed the mean SFC values (across all pairs and sessions) and found that whereas SFC in the higher frequency bands showed no significant change across blocks (Kruskal–Wallis, p > 0.2), theta-band SFC was significantly increased during the time course of learning (Figure 3A; n = 625 pairs, comparing blocks 2–4 with block 1, Kruskal–Wallis, p < 10−7). Figure 3B shows the distribution of SFC values for all the pairs in our population and reveals that learning is accompanied by a pronounced increase in theta SFC in blocks 2–4 relative to block 1 (p < 0.05, t-test). Furthermore, post hoc analysis shows a significant SFC increase in each of blocks 2, 3, and 4 relative to block 1 (p < 0.05; the median theta SFC was increased by 34%). The increase in theta SFC during learning was statistically significant in both animals (Figure 3C, p < 0.001, Kruskal–Wallis test, post hoc analysis).10.7554/eLife.08417.006Figure 3.Changes in spike-LFP coherence during learning.

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