This list is fully up to date (but surely not complete!) until 2018/06, afterwards likely more incomplete
DNN vs. Human Reviews / Commentaries (sorted by year):
Kriegeskorte, N. (2015) – Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual review of vision science, 1, 417-446. https://doi.org/10.1146/annurev-vision-082114-035447
Gauthier, I., & Tarr, M. J. (2016). Visual object recognition: Do we (finally) know more now than we did? Annual review of vision science, 2, 377-396. https://doi.org/10.1146/annurev-vision-111815-114621
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016). Toward an integration of deep learning and neuroscience. Frontiers in Computational Neuroscience, 10, 94. https://doi.org/10.3389/fncom.2016.00094 Excellent, very complete review of the ML/cortex frontier. It argues in favour of a computational neuroscience that researches the brain’s cost functions and their optimisation processes, rather than specific circuits. It debates what ML can learn from neuroscience and vice-versa.
Yamins, D. L., & DiCarlo, J. J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nature neuroscience, 19(3), 356 https://doi.org/10.1038/nn.4244 Gentle introduction to Convolutional Neural Networks and their use for predicting the encoding strategy of sensory areas. Mostly conversational, does not present hard analytical or data-driven results other than as a review.
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258. https://doi.org/10.1016/j.neuron.2017.06.011 General view, including historical perspective, of AI/Neuroscience relationships. Particularly focused on the cognitive/behavioural level (transfer learning, reinforcement learning, attention, memory). By DeepMind founder Demis Hassabis and colleagues.
Kay, K. N. (2017). Principles for models of neural information processing. NeuroImage. http://dx.doi.org/10.1016/j.neuroimage.2017.08.016
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40. https://doi.org/10.1017/S0140525X16001837 VanRullen, R. (2017). Perception science in the age of deep neural networks. Frontiers in psychology, 8, 142. https://doi.org/10.3389/fpsyg.2017.00142
Aru, J., & Vicente, R. (2018). What deep learning can tell us about higher cognitive functions like mindreading?. arXiv preprint arXiv:1803.10470. https://arxiv.org/abs/1803.10470
Barrett, David GT, Ari S. Morcos, and Jakob H. Macke. 'Analyzing biological and artificial neural networks: challenges with opportunities for synergy?.' arXiv preprint arXiv:1810.13373 (2018). https://arxiv.org/abs/1810.13373 Good overview on challenges and opportunities in comparing DNNs with BioNets. What analysis methods can be used on both? What methods can be used to compare them to each other?
Glaser, J. I., Benjamin, A. S., Farhoodi, R., & Kording, K. P. (2018). The Roles of Supervised Machine Learning in Systems Neuroscience. arXiv preprint arXiv:1805.08239. https://arxiv.org/abs/1805.08239 How can machine learning be used in neuroscience? 1. Solving engineering problems; 2. Identifying predictive variables; 3. Benchmarking simple models; 4. Serving as a model for the brain.
Majaj, N. J., & Pelli, D. G. (2018). Deep learning—Using machine learning to study biological vision. Journal of vision, 18(13), 2-2 https://doi.org/10.1167/18.13.2
Cichy, R. M., Kaiser, D. (2019). Deep neural networks as scientific models. Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2019.01.009
Kietzmann, T. C., McClure, P., & Kriegeskorte, N. (2019). Deep neural networks in computational neuroscience. In Oxford Research Encyclopedia of Neuroscience. Oxford University Press. doi: http://dx.doi.org/10.1093/acrefore/9780190264086.013.46 http://oxfordre.com/neuroscience/view/10.1093/acrefore/9780190264086.001.0001/acrefore-9780190264086-e-46
Storrs, K. & Kriegeskorte, N. (2019). Deep Learning for Cognitive Neuroscience. In The Cognitive Neurosciences, 6th Edition. https://arxiv.org/abs/1903.01458
Learning in DNN vs. Brain; Bio-plausible learning (reviews)
Marblestone et al. (2016). Toward an integration of deep learning and neuroscience. See above.
Whittington and Bogacz, Theories of Error Back-propagation in the Brain, Trends in Cog Sci 2018 https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(19)30012-9 A review with a particular focus on bio-plausible implementations of backpropagation in artificial neural networks.
Pieter R. Roelfsema & Anthony Holtmaat, Control of synaptic plasticity in deep cortical networks, Nat Rev Neurosci 19, 166–180 (2018) https://www.nature.com/articles/nrn.2018.6 Bio-oriented. Neuroscience review on how the brain may solve the “credit assignment problem” by means of neuromodulation and feedback that “gate” and “steer” plasticity in earlier layers, based on the outcome of the action and on what synapses contributed to the decision.
DNN vs. Brain Imaging Papers (sorted by year and alphabetically):
Please no medical imaging papers!
Cadieu et al (2014) – Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition – PloS Comput Biol https://doi.org/10.1371/journal.pcbi.1003963
Khaligh-Razavi & Kriegeskorte (2014) – Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation – PloS Comput Biol https://doi.org/10.1371/journal.pcbi.1003915
Yamins et al (2014) – Performance-optimized hierarchical models predict neural responses in higher visual cortex – PNAS http://www.pnas.org/content/111/23/8619.short
Güçlü & van Gerven (2015) – Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream – J Neurosci https://doi.org/10.1523/JNEUROSCI.5023-14.2015
Güçlü & van Gerven (2015) – Increasingly complex representations of natural movies across the dorsal stream are shared between subjects – Neuroimage https://doi.org/10.1016/j.neuroimage.2015.12.036
Cichy et al (2016) - Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence – Sci Reports https://www.nature.com/articles/srep27755
Güçlü et al (2016) – Brains on beats – NIPS https://papers.nips.cc/paper/6222-brains-on-beats.pdf
Hong et al (2016) – Explicit information for category-orthogonal object properties increases along the ventral stream – Nature Neuroscience https://www.nature.com/articles/nn.4247
Yamins & DiCarlo (2016) – Using goal-driven deep learning models to understand sensory cortex – Nature Neuroscience https://www.nature.com/articles/nn.4244
Cichy et al (2017) – Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks – Neuroimage https://doi.org/10.1016/j.neuroimage.2016.03.063
Eickenberg et al (2017) – Seeing it all: Convolutional network layers map the function of the human visual system – Neuroimage https://doi.org/10.1016/j.neuroimage.2016.10.001
Güçlütürk, Güçlü, Seeliger, Bosch, van Lier, van Gerven (2017) – Reconstructing perceived faces from brain activations with deep adversarial neural decoding – NIPS http://papers.nips.cc/paper/7012-reconstructing-perceived-faces-from-brain-activations-with-deep-adversarial-neural-decoding.pdf
Güçlü & van Gerven (2017) – Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks – Front Comput Neurosci https://dx.doi.org/10.3389%2Ffncom.2017.00007
Horikawa & Kamitani (2017) – Hierarchical neural representation of dreamed objects revealed by brain decoding with deep neural network features – Front Comput Neurosci https://doi.org/10.3389/fncom.2017.00004
Horikawa & Kamitani (2017) – Generic decoding of seen and imagined objects using hierarchical visual features – Nat Commun https://dx.doi.org/10.1038%2Fncomms15037
Kalfas, Kumar, Vogels (2017) – Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons – eNeuro https://doi.org/10.1523/ENEURO.0113-17.2017
Karimi-Rouzbahani et al (2017) - Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition - Journal of Neuroscience https://doi.org/10.1016/j.neuroscience.2017.02.050
Khaligh-Razavi et al (2017) – Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models – J Math Psych https://doi.org/10.1016/j.jmp.2016.10.007
Klindt, Ecker, Euler, Bethge (2017) – Neural system identification for large populations separating “what” and “where” – NIPS http://papers.nips.cc/paper/6942-neural-system-identification-for-large-populations-separating-what-and-where
Ratan Murty & Arun (2017) – A balanced comparison of object invariances in monkey IT neurons – eNeuro https://doi.org/10.1523/ENEURO.0333-16.2017
Scholte et al (2017) - Visual pathways from the perspective of cost functions and multi-task deep neural networks - Cortex https://doi.org/10.1016/j.cortex.2017.09.019
Seeliger et al (2017) – Convolutional neural network-based encoding and decoding of visual object recognition in space and time – Neuroimage https://doi.org/10.1016/j.neuroimage.2017.07.018
Seeliger et al (2017) - Generative adversarial networks for reconstructing natural images from brain activity. NeuroImage 181 http://www.sciencedirect.com/science/article/pii/S105381191830658X (preprint: https://doi.org/10.1101/226688 )
Tacchetti, Isik, Poggio (2017) – Invariant recognition drives neural representations of action sequences – PloS Comput Biology https://doi.org/10.1371/journal.pcbi.1005859
Tripp et al (2017) – Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks - IJCNN https://arxiv.org/abs/1612.06975
Wen et al (2017) – Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision – Cerebral Cortex https://doi.org/10.1093/cercor/bhx268
Zhuang, Wang, Yamins, Hu (2017) – Deep learning predicts correlation between a functional signature of higher visual areas and sparse firing of neurons – Front Comput Neurosci https://doi.org/10.3389/fncom.2017.00100
Abdelhack, Kamitani (2018) – Sharpening of hierarchical visual feature representations of blurred images – eNeuro https://doi.org/10.1523/ENEURO.0443-17.2018
Bankson, Hebart, Groen, Baker (2018) – The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks – Neuroimage https://doi.org/10.1016/j.neuroimage.2018.05.037
Banino et al (2018) - Vector-based navigation using grid-like representations in artificial agents - Nature https://doi.org/10.1038/s41586-018-0102-6
Bonner, Epstein (2018) – Computational mechanisms underlying cortical responses to the affordance properties of visual scenes – PloS Computational Biology https://doi.org/10.1371/journal.pcbi.1006111
Devereaux, Clarke, Tyler (2018) - Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway - Scientific Reports https://doi.org/10.1038/s41598-018-28865-1
Fong, Scheirer, Cox (2018) - Using human brain activity to guide machine learning - Scientific Reports https://doi.org/10.1038/s41598-018-23618-6
Greene & Hansen (2018) - Shared spatiotemporal category representations in biological and artificial deep neural networks - PloS Comput Biol https://doi.org/10.1371/journal.pcbi.1006327
Groen et al (2018) – Distinct contributions of functional and deep neural network features to scene representation in brain and behavior – eLife https://doi.org/10.7554/eLife.32962
Kell, Yamins, Shook, Norman-Haignere, McDermott (2018) – A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy – Neuron https://doi.org/10.1016/j.neuron.2018.03.044
O’Connell and Chun (2018) – Predicting eye movements from fMRI responses to natural scenes – Nature Communications https://doi.org/10.1038/s41467-018-07471-9
Rajalingham et al (2018): Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks. Journal of Neuroscience 38. (preprint: http://dx.doi.org/10.1101/240614 ) http://www.jneurosci.org/content/38/33/7255
Ratan Murty, Arun (2018) – Multiplicative mixing of object identity and image attributes in single inferior temporal neurons – PNAS https://doi.org/10.1073/pnas.1714287115
Shi et al (2018) – Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision – Human Brain Mapping http://dx.doi.org/10.1002/hbm.24006
Tang, Schrimpf, Lotter, Moerman, Paredes, Caro, Hardesty, Cox, and Kreiman (2018) – Recurrent computations for visual pattern completion – PNAS https://doi.org/10.1073/pnas.1719397115
Wen et al (2018) – Deep Residual Network Reveals a Nested Hierarchy of Distributed Cortical Representation for Visual Categorization – Scientific Reports https://www.nature.com/articles/s41598-018-22160-9
Wenliang, Seitz (2018) - Deep neural networks for modeling visual perceptual learning - J Neurosci https://doi.org/10.1523/JNEUROSCI.1620-17.2018
Kar, Kubilius, Schmidt, Issa, DiCarlo (2018) - Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior - Nature Neuroscience https://doi.org/10.1038/s41593-019-0392-5 Preprint: https://doi.org/10.1101/354753
Agrawal et al (2014) - Pixels to Voxels: Modeling Visual Representation in the Human Brain - arXiv https://arxiv.org/pdf/1407.5104.pdf
Bracci, Kalfas, Op de Beeck (2017) – The ventral visual pathway represents animal appearance over animacy, unlike human behavior and deep neural networks – bioRxiv https://doi.org/10.1101/228932
Bashivan, Kar, DiCarlo (2018) - Neural population control via deep image synthesis - bioRxiv https://doi.org/10.1101/461525
Cadena et al (2017) - Deep convolutional models improve predictions of macaque V1 responses to natural images - bioRxiv https://doi.org/10.1101/201764
Cichy et al (2017) - Neural dynamics of real-world object vision that guide behaviour - bioRxiv https://www.biorxiv.org/content/early/2017/06/08/147298
Devereux, Clarke, Tyler (2018) – Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway – bioRxiv https://doi.org/10.1101/302406
Dwivedi, Roig (2018) - Task-specific vision models explain task-specific areas of visual cortex - bioRxiv https://www.biorxiv.org/content/early/2018/08/28/402735
Gwilliams & King (2017) - Performance-optimized hierarchical models only partially predict neural responses during perceptual decision making - bioRxiv https://www.biorxiv.org/content/early/2017/11/20/221630.full
King, Groen, Steel, Kravitz, Baker (2018) - Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images – bioRxiv https://doi.org/10.1101/316554
Kindel, Christensen, Zylberberg (2017) – Using deep learning to reveal the neural code for images in primary visual cortex – arxiV https://arxiv.org/abs/1706.06208
Kuzovkin et al (2017) - Activations of Deep Convolutional Neural Network are Aligned with Gamma Band Activity of Human Visual Cortex - BioRxiv https://doi.org/10.1101/133694
Long et al (2017) - A mid-level organization of the ventral stream - BioRXiv https://doi.org/10.1101/213934
Lotter, Kreiman, Cox (2018) – A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception – arXiv https://arxiv.org/abs/1805.10734
Nayebi, Bear, Kubilius, Kar, Ganguli, Sussillo, DiCarlo, Yamins (2018) - Task-Driven Convolutional Recurrent Models of the Visual System - arXiv https://arxiv.org/abs/1807.00053
O’Connell & Chun (2017) - Predicting eye movements with deep neural network activity decoded from fMRI responses to natural scenes - bioRxiv https://doi.org/10.1101/166421
Pinotsis, Siegel, Miller (2019) - Sensory processing and categorization in cortical and deep neural networks - bioRxiv https://doi.org/10.1101/647222
Ponce, Xiao, Schade, Hartmann, Kreiman, Livingstone (2019) - Evolving super stimuli for real neurons using deep generative networks - bioRxiv doi: https://doi.org/10.1101/516484
Qiao, Zhang, Wang, Yan, Chen, Zeng, Tong (2018) – Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture – arXiv https://arxiv.org/abs/1801.00602
Rajaei, Mohsenzadeh, Ebrahimpour, Khaligh-Razavi (2018) – Beyond core object recognition: Recurrent processes account for object recognition under occlusion – bioRxiv https://doi.org/10.1101/302034
Ramakrishnan et al (2017) - Characterizing the temporal dynamics of object recognition by deep neural networks: role of depth - bioRxiv https://doi.org/10.1101/178541
Shen, G., Horikawa, T., Majima, K., & Kamitani, Y. (2017). Deep image reconstruction from human brain activity - bioRxiv. https://www.biorxiv.org/content/early/2017/12/28/240317
Tang, Schrimpf, Lotter, et al (2017) - Recurrent computations for visual pattern completion - arXiv https://arxiv.org/abs/1706.02240
VanRullen & Reddy (2018) – Reconstructing faces from fMRI patterns using deep generative neural networks – arXiv https://arxiv.org/abs/1810.03856
Walker, Sinz, Froudarakis, Fahey, Muhammad, Ecker, Cobos, Reimer, Pitkow, & Tolias (2019) - Inception in visual cortex: in vivo-silico loops reveal most exciting images - biorXiv https://doi.org/10.1101/506956
Wen et al (2017) – Transferring and Generalizing Deep-Learning-based Neural Encoding Models across Subjects – bioRxiv https://dxoi.org/10.1101/171017
Zhang, Qiao, Wang, Tong, Zeng, Yan (2018) – Constraint-free natural image reconstruction from fMRI signals based on convolutional neural network – arXiv https://arxiv.org/abs/1801.05151
Ghodrati et al (2014) – Feedforward object-vision models only tolerate small image variations compared to human – Front Comput Neurosci https://dx.doi.org/10.3389%2Ffncom.2014.00074
Lake et al (2015) - Deep neural networks predict category typicality ratings for images - Cogn Psych http://gureckislab.org/papers/LakeZarembaFergusGureckis.CogSci2015.pdf
Nguyen et al (2015) – Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images – IEEE Comput Vis Patt Recog https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf
Rajalingham et al (2015) – Comparison of object recognition behavior in human and monkey. Journal of Neuroscience http://www.jneurosci.org/content/35/35/12127
Eberhardt et al (2016) – How deep is the feature analysis underlying rapid visual categorization – NIPS http://papers.nips.cc/paper/6218-how-deep-is-the-feature-analysis-underlying-rapid-visual-categorization
Farzmahdi et al (2016) – A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans – Sci Rep https://dx.doi.org/10.1038%2Fsrep25025
Greene et al (2016) – Visual Scenes Are Categorized by Function – JEP:General http://psycnet.apa.org/fulltext/2015-58122-004.html
Kheradpisheh et al (2016) – Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition – Sci Rep https://www.nature.com/articles/srep32672
Kheradpisheh et al (2016) – Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder – Front Comput Neurosci https://dx.doi.org/10.3389%2Ffncom.2016.00092
Kubilius et al (2016) – Deep Neural Networks as a Computational Model for Human Shape Sensitivity – PloS Comput Biol https://doi.org/10.1371/journal.pcbi.1004896
Jozwik et al (2017) – Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments – Front Psychol https://doi.org/10.3389/fpsyg.2017.01726
Karimi-Rouzbahani, Bagheri & Ebrahimpour (2017) – Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models – Scientific Reports https://doi.org/10.1038/s41598-017-13756-8
Love et al (2017) – Deep Networks as Models of Human and Animal Categorization - Cogn Psych https://mindmodeling.org/cogsci2017/papers/0283/paper0283.pdf
Lukavský, J., & Děchtěrenko, F. (2017). Visual properties and memorising scenes: Effects of image-space sparseness and uniformity. Attention, Perception, & Psychophysics, 79(7), 2044–2054. https://doi.org/10.3758/s13414-017-1375-9
Song, Yang, Wang (2017) – Reward-based training of recurrent neural networks for cognitive and value-based tasks – eLife https://dx.doi.org/10.7554%2FeLife.21492
Suzuki et al (2017) - A Deep-Dream Virtual Reality Platform for Studying Altered Perceptual Phenomenology - Scientific Reports https://doi.org/10.1038/s41598-017-16316-2
Wallis et al (2017) – A parametric texture model based on deep convolutional features closely matches texture appearance for humans – JoV http://jov.arvojournals.org/article.aspx?articleid=2657215
Wichmann, F. A., Janssen, D. H., Geirhos, R., Aguilar, G., Schütt, H. H., Maertens, M., & Bethge, M. (2017). Methods and measurements to compare men against machines. Electronic Imaging, 2017(14), 36-45. https://doi.org/10.2352/ISSN.2470-1173.2017.14.HVEI-113
Tang, Schrimpf, Lotter, et al (2017) - Recurrent computations for visual pattern completion - arXiv https://arxiv.org/abs/1706.02240
Baker, Lu, Erlikhman, Kellman (2018) – Deep convolutional networks do not classify based on global object shape – PLoS Computational Biology https://doi.org/10.1371/journal.pcbi.1006613
Flesch, Balaguer, Dekker, Nili, & Summerfield (2018) – Comparing continual task learning in minds and machines – PNAS https://doi.org/10.1073/pnas.1803839115
Geirhos, Medina Temme, Rauber, Schütt, Bethge, Wichmann (2018) - Generalisation in humans and deep neural networks - NeurIPS https://arxiv.org/abs/1808.08750
Masse, Grant, & Freedman (2018) – Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization – PNAS https://doi.org/10.1073/pnas.1803839115
Rajalingham et al (2018): Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks. Journal of Neuroscience 38. (preprint: http://dx.doi.org/10.1101/240614 ) http://www.jneurosci.org/content/38/33/7255
Watanabe et al (2018) - Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction - Frontiers in Psychology https://doi.org/10.3389/fpsyg.2018.00345
Wenliang, Seitz (2018) - Deep neural networks for modeling visual perceptual learning - J Neurosci https://doi.org/10.1523/JNEUROSCI.1620-17.2018
Xu, Garrod, Scholte, Ince, Schyns (2018) - Using Psychophysical Methods to Understand Mechanisms of Face Identification in a Deep Neural Network - CVPRW https://0ceabf3e-a-62cb3a1a-s-sites.googlegroups.com/site/skytianxu/xu2018%20Using%20Psychophysical%20Methods%20to%20Understand%20Mechanisms%20of%20Face%20Identification%20in%20a%20Deep%20Neural%20Network.pdf
Geirhos; Rubisch, Michaelis, Bethge, Wichmann, & Brendel (2018) - ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness ICLR 2019 (Oral) https://openreview.net/forum?id=Bygh9j09KX
Peterson et al (2016) – Adapting deep network features to capture psychological representations – ArXiv https://arxiv.org/abs/1608.02164
Battleday, Peterson, Griffiths (2017) – Modeling Human Categorization of Natural Images Using Deep Feature Representations - ArXiv https://arxiv.org/abs/1711.04855
Dekel (2017) - Human perception in computer vision - arXiv https://arxiv.org/abs/1701.04674
Fan, Yamins, & Turk-Browne (2017) – Common object representations for visual production and recognition – bioRxiv https://www.biorxiv.org/content/early/2017/01/03/097840
Geirhos, Janssen, Schütt, Rauber, Bethge, & Wichmann (2017) – Comparing deep neural networks against humans: object recognition when the signal gets weaker – ArXiv https://arxiv.org/abs/1706.06969
Lin et al (2017) – Transfer of view-manifold learning to similarity perception of novel objects – ArXiv https://arxiv.org/abs/1704.00033
Lindsay & Miller (2017) - Understanding Biological Visual Attention Using Convolutional Neural Networks – biorXiv https://www.biorxiv.org/content/early/2017/12/20/233338
Fruend, Stalker (2018) – Measuring human sensitivity to perturbations within the manifold of natural images – bioRxiv https://doi.org/10.1101/320531
Leibo et al (2018) - Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents - arXiv https://arxiv.org/abs/1801.08116
Linsley, Scheibler, Eberhardt, Serre (2018) – Global-and-local attention networks for visual recognition – arXiv https://arxiv.org/abs/1805.08819
Hugo Richard, Ana Pinho, Bertrand Thirion, Guillaume Charpiat (2018) – Optimizing deep video representations to match brain activity – arXiv https://arxiv.org/abs/1809.02440
Rosenfeld, Solbach, Tsotsos (2018) – Totally Looks Like - How Humans Compare, Compared to Machines – arXiv https://arxiv.org/abs/1803.01485
Wallis, T. S. A., Funke, C. M., Ecker, A. S., Gatys, L. A., Wichmann, F. A., & Bethge, M. (2018). Image content is more important than Boumas Law for scene metamers. BioRxiv. https://doi.org/10.1101/378521
Fruend, I. Simple, biologically informed models, but not convolutional neural networks describe target detection in naturalistic images. bioRxiv. https://doi.org/10.1101/578633
Kim, B., Reif, E., Wattenberg, M., Bengio, S. (2019). Do neural networks show gestalt phenomena? An exploration of the law of closure. https://arxiv.org/abs/1903.01069
Work using older DNN models (e.g. HMAX)
(everything else, obviously incomplete)
Fukushima, K. (1988). Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural networks, 1(2), 119-130.
van Gerven, Marcel AJ, Floris P. de Lange, and Tom Heskes (2010). 'Neural decoding with hierarchical generative models.' Neural computation 22.12 (2010): 3127-3142.
Zeman et al (2013) The Müller-Lyer Illusion in a Computational Model of Biological Object Recognition. PLoS ONE8(2): e56126. https://doi.org/10.1371/journal.pone.0056126
Zeman et al (2014) - Complex cells decrease errors for the Müller-Lyer illusion in a model of the visual ventral stream - Front. Comput. Neurosci. 8:112. https://doi.org/10.3389/fncom.2014.00112
Ramakrishnan K, Scholte HS, Groen IIA, Smeulders AWM, Ghebreab S (2015) -Visual dictionaries as intermediate features in the human brain - Front Comput Neurosci https://www.frontiersin.org/articles/10.3389/fncom.2014.00168/full (HMAX vs bag-of-words tested against fMRI)
Tang, Schrimpf, Lotter, et al (2017) - Recurrent computations for visual pattern completion - arXiv https://arxiv.org/abs/1706.02240
Ponce, Lomber, Livingstone (2018) - Posterior Inferotemporal Cortex Cells Use Multiple Input Pathways for Shape Encoding - Journal of Neuroscience http://www.jneurosci.org/content/37/19/5019