Interpretability of artificial neural network models in artificial intelligence versus neuroscience

[1]  M. Bethge,et al.  Partial success in closing the gap between human and machine vision , 2021, NeurIPS.

[2]  Eghbal A. Hosseini,et al.  The neural architecture of language: Integrative modeling converges on predictive processing , 2020, Proceedings of the National Academy of Sciences.

[3]  Kohitij Kar,et al.  Fast recurrent processing via ventral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition , 2020, bioRxiv.

[4]  Mason McGill,et al.  A map of object space in primate inferotemporal cortex , 2020, Nature.

[5]  Carlos R. Ponce,et al.  Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences , 2019, Cell.

[6]  James J DiCarlo,et al.  Neural population control via deep image synthesis , 2018, Science.

[7]  D. Erhan,et al.  A Benchmark for Interpretability Methods in Deep Neural Networks , 2018, NeurIPS.

[8]  Helena Webb,et al.  A governance framework for algorithmic accountability and transparency , 2019 .

[9]  Jonas Kubilius,et al.  Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? , 2018, bioRxiv.

[10]  Andreas Holzinger,et al.  From Machine Learning to Explainable AI , 2018, 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA).

[11]  James J. DiCarlo,et al.  Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior , 2018, Nature Neuroscience.

[12]  Ha Hong,et al.  Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance , 2015, The Journal of Neuroscience.

[13]  Ha Hong,et al.  Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.

[14]  W. Newsome,et al.  Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.

[15]  Richard F Murray,et al.  Classification images: A review. , 2011, Journal of vision.

[16]  Frédéric Gosselin,et al.  Bubbles: a technique to reveal the use of information in recognition tasks , 2001, Vision Research.

[17]  Ulf Assarsson,et al.  A Benchmark for , 2001 .

[18]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[19]  Deborah Silver,et al.  Feature Visualization , 1994, Scientific Visualization.

[20]  Richard A. Andersen,et al.  A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons , 1988, Nature.

[21]  Robert C. Wolpert,et al.  A Review of the , 1985 .