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Perceptual training continuously refines neuronal population codes in primary visual cortex

Nature Neuroscience volume 17, pages 1380–1387 (2014)Cite this article

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Abstract

Perceptual learning substantially improves visual discrimination and detection ability, which has been associated with visual cortical plasticity. However, little is known about the dynamic changes in neuronal response properties over the course of training. Using chronically implanted multielectrode arrays, we were able to capture day-by-day spatiotemporal dynamics of neurons in the primary visual cortex (V1) of monkeys trained to detect camouflaged visual contours. We found progressive strengthening and accelerating in both facilitation of neurons encoding the contour elements and suppression of neurons responding to the background components. The enhancement of this figure-ground contrast in V1 was closely correlated with improved behavioral performance on a daily basis. Decoding accuracy of a simple linear classifier based on V1 population responses also paralleled the animal's behavioral changes. Our results indicate that perceptual learning shapes the V1 population code to allow a more efficient readout of task-relevant information.

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Figure 1: Experimental design.
Figure 2: Contour-related signals in V1 and their changes with training.
Figure 3: Quantification of learning-induced changes in V1 for the learned conditions.
Figure 4: Learning-induced changes in temporal dynamics of V1 responses.
Figure 5: Decoding V1 population responses.
Figure 6: Further decoding analyses.

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Acknowledgements

We thank X. Xu and F. Wang for technical assistance. This work was supported by the National Basic Research Program of China (973 Program 2014CB846101, 2011CBA00400), the National Natural Science Foundation of China (31125014, 31371109 and 30970983) and the Fundamental Research Funds for the Central Universities of China.

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Author notes

  1. Yin Yan, Malte J Rasch and Minggui Chen: These authors contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, and Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China

    Yin Yan, Malte J Rasch, Minggui Chen, Xiaoping Xiang, Min Huang, Si Wu & Wu Li

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  1. Yin Yan
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Contributions

M.C., Y.Y. and W.L. designed the experiments. Y.Y., M.C. and X.X. conducted the experiments. M.J.R., Y.Y., M.H. and S.W. analyzed the data. M.J.R. performed the population decoding analyses. M.J.R. and Y.Y. prepared the figures. M.J.R., Y.Y. and W.L. wrote the paper.

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Correspondence to Wu Li.

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Yan, Y., Rasch, M., Chen, M. et al. Perceptual training continuously refines neuronal population codes in primary visual cortex. Nat Neurosci 17, 1380–1387 (2014). https://doi.org/10.1038/nn.3805

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