Challenges for cognitive decoding using deep learning methods

Abstract

In cognitive decoding, researchers aim to characterize a brain region's representations by identifying the cognitive states (e.g., accepting/rejecting a gamble) that can be identified from the region's activity. Deep learning (DL) methods are highly promising for cognitive decoding, with their unmatched ability to learn versatile representations of complex data. Yet, their widespread application in cognitive decoding is hindered by their general lack of interpretability as well as difficulties in applying them to small datasets and in ensuring their reproducibility and robustness. We propose to approach these challenges by leveraging recent advances in explainable artificial intelligence and transfer learning, while also providing specific recommendations on how to improve the reproducibility and robustness of DL modeling results.

References in corpus (10)

• Neural Architecture Search with Reinforcement Learning
• On the difficulty of training Recurrent Neural Networks
• How transferable are features in deep neural networks?
• Towards A Rigorous Science of Interpretable Machine Learning
• Striving for Simplicity: The All Convolutional Net
• Snorkel: Rapid Training Data Creation with Weak Supervision
• SmoothGrad: removing noise by adding noise
• Captum: A unified and generic model interpretability library for PyTorch
• Attention is not Explanation
• Visualizing Deep Neural Network Decisions: Prediction Difference Analysis