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Convolutional neural network models of V1 responses to complex patterns

View ORCID ProfileYimeng Zhang, Tai Sing Lee, Ming Li, Fang Liu, View ORCID ProfileShiming Tang
doi: https://doi.org/10.1101/296301
Yimeng Zhang
1Center for the Neural Basis of Cognition and Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213
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Tai Sing Lee
1Center for the Neural Basis of Cognition and Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213
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Ming Li
2Peking University School of Life Sciences and Peking Tsinghua Center for Life Sciences, Beijing 100871, China
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Fang Liu
2Peking University School of Life Sciences and Peking Tsinghua Center for Life Sciences, Beijing 100871, China
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Shiming Tang
3IDG/McGovern Institute for Brain Research at Peking University, Beijing 100871, China
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Abstract

In this study, we evaluated the convolutional neural network (CNN) method for modeling V1 neurons of awake macaque monkeys in response to a large set of complex pattern stimuli. CNN models outperformed all the other baseline models, such as Gabor-based standard models for V1 cells and various variants of generalized linear models. We then systematically dissected different components of the CNN and found two key factors that made CNNs outperform other models: thresholding nonlinearity and convolution. In addition, we fitted our data using a pre-trained deep CNN via transfer learning. The deep CNN’s higher layers, which encode more complex patterns, outperformed lower ones, and this result was consistent with our earlier work on the complexity of V1 neural code. Our study systematically evaluates the relative merits of different CNN components in the context of V1 neuron modeling.

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Posted April 06, 2018.
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Convolutional neural network models of V1 responses to complex patterns
Yimeng Zhang, Tai Sing Lee, Ming Li, Fang Liu, Shiming Tang
bioRxiv 296301; doi: https://doi.org/10.1101/296301
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Convolutional neural network models of V1 responses to complex patterns
Yimeng Zhang, Tai Sing Lee, Ming Li, Fang Liu, Shiming Tang
bioRxiv 296301; doi: https://doi.org/10.1101/296301

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