Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence

[1]  J. Gallant,et al.  Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons , 2004, The Journal of Neuroscience.

[2]  Eero P. Simoncelli,et al.  Perceptual straightening of natural videos , 2019, Nature Neuroscience.

[3]  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.

[4]  Eero P. Simoncelli,et al.  A functional and perceptual signature of the second visual area in primates , 2013, Nature Neuroscience.

[5]  David Cox,et al.  Recurrent computations for visual pattern completion , 2017, Proceedings of the National Academy of Sciences.

[6]  Elias B. Issa,et al.  Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs , 2019, NeurIPS.

[7]  J. Gold,et al.  On the nature and use of models in network neuroscience , 2018, Nature Reviews Neuroscience.

[8]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[9]  Riegeskorte CONTROVERSIAL STIMULI: PITTING NEURAL NETWORKS AGAINST EACH OTHER AS MODELS OF HUMAN RECOGNITION , 2019 .

[10]  Surya Ganguli,et al.  A deep learning framework for neuroscience , 2019, Nature Neuroscience.

[11]  Vanessa D'Amario,et al.  Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks , 2019, ArXiv.

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

[13]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Ohad Shamir,et al.  Size-Independent Sample Complexity of Neural Networks , 2017, COLT.

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

[16]  J. Platt Strong Inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others. , 1964, Science.

[17]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[18]  James J. DiCarlo,et al.  How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.

[19]  Lorenzo Rosasco,et al.  Theory III: Dynamics and Generalization in Deep Networks , 2019, ArXiv.

[20]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Surya Ganguli,et al.  From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction , 2019, NeurIPS.

[22]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.

[23]  Surya Ganguli,et al.  Task-Driven Convolutional Recurrent Models of the Visual System , 2018, NeurIPS.

[24]  Antonio Torralba,et al.  Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.

[25]  Nikolaus Kriegeskorte,et al.  Recurrence is required to capture the representational dynamics of the human visual system , 2019, Proceedings of the National Academy of Sciences.

[26]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[27]  Guosong Hong,et al.  Novel electrode technologies for neural recordings , 2019, Nature Reviews Neuroscience.

[28]  Neville Hogan,et al.  Experimenting with Theoretical Motor Neuroscience , 2010, Journal of motor behavior.

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

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

[31]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[32]  Leon A. Gatys,et al.  Deep convolutional models improve predictions of macaque V1 responses to natural images , 2019, PLoS Comput. Biol..

[33]  Yalda Mohsenzadeh,et al.  The Algonauts Project: A Platform for Communication between the Sciences of Biological and Artificial Intelligence , 2019, 2019 Conference on Cognitive Computational Neuroscience.

[34]  James J DiCarlo,et al.  Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks , 2018, The Journal of Neuroscience.

[35]  Keiji Tanaka,et al.  Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.

[36]  Leon A. Gatys,et al.  Deep convolutional models improve predictions of macaque V1 responses to natural images , 2017, bioRxiv.

[37]  Radoslaw Martin Cichy,et al.  Deep Neural Networks as Scientific Models , 2019, Trends in Cognitive Sciences.

[38]  Jonas Kubilius,et al.  Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior , 2019, Nature Neuroscience.

[39]  Tapani Raiko,et al.  International Conference on Learning Representations (ICLR) , 2016 .

[40]  Mikhail Belkin,et al.  Reconciling modern machine-learning practice and the classical bias–variance trade-off , 2018, Proceedings of the National Academy of Sciences.