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In this project, we modelled the effects of arousal on sensory processing by using a deep convolutional neural network augmented with a global gain mechanism.

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Deep hierarchical sensory processing accounts for effects of arousal state on perceptual decision-making

by Lynn K.A. Sörensen, Sander M. Bohté, Heleen A. Slagter*, & H. Steven Scholte*

The figure is copied from this preprint. The example picture in B is licensed under CC BY-SA 2.0 and was adapted from Flickr.

Overview

This is the code to reproduce the results of this paper. The repository is organized in three parts:

  • the asn package for DCNNs for obtaining the ASN transfer function and the global gain modulation.
  • the code to reproduce the results in the paper (ModelPerformance, ModelAnalysis)
  • the code to reproduce the paper figures (Figures)

Dependencies

Implementation for Keras (2.2.4) with a tensorflow backend (1.10). All result files can be downloaded here. Please make sure to add the files to folder Results to reproduce the Figures. The weights of the base models trained on ImageNet can be accessed here.

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In this project, we modelled the effects of arousal on sensory processing by using a deep convolutional neural network augmented with a global gain mechanism.

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