Abstract
Extensive training on simple tasks such as fine orientation discrimination results in large improvements in performance, a form of learning known as perceptual learning. Previous models have argued that perceptual learning is due to either sharpening and amplification of tuning curves in early visual areas or to improved probabilistic inference in later visual areas (at the decision stage). However, early theories are inconsistent with the conclusions of psychophysical experiments manipulating external noise, whereas late theories cannot explain the changes in neural responses that have been reported in cortical areas V1 and V4. Here we show that we can capture both the neurophysiological and behavioral aspects of perceptual learning by altering only the feedforward connectivity in a recurrent network of spiking neurons so as to improve probabilistic inference in early visual areas. The resulting network shows modest changes in tuning curves, in line with neurophysiological reports, along with a marked reduction in the amplitude of pairwise noise correlations.
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Acknowledgements
J.M.B. was supported by the Gatsby Charitable Foundation. Z.-L.L. is supported by US National Eye Institute grant 9 R01 EY017491-05 and A.P. by Multidisciplinary University Research Initiative grant N00014-07-1-0937, US National Institute on Drug Abuse grant BCS0346785 and a research grant from the James S. McDonnell Foundation. This work was also partially supported by award P30 EY001319 from the US National Eye Institute.
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V.R.B. conceived the project, built the network model, ran all the simulations and analyses and wrote the paper. J.M.B. developed the analytic derivations, helped with building the network model and wrote the paper. Z.-L.L. worked on the link between the neural model and TVC curves and helped with parameter tuning. A.P. conceived the project, supervised the simulations and analyses and wrote the paper.
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Bejjanki, V., Beck, J., Lu, ZL. et al. Perceptual learning as improved probabilistic inference in early sensory areas. Nat Neurosci 14, 642–648 (2011). https://doi.org/10.1038/nn.2796
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DOI: https://doi.org/10.1038/nn.2796
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