neuroaid

⚡ 📚 Papers and other material for getting started with Neuro-AI! 🧠 💥

A. FROM NEURO-AI COURSE:

1. ANNs and the Brain

1.1. The Zoo of Networks

1.2. Modeling Introduction

1.3. ANNs as Brain Models

1.4. Feedforward Networks

1.5. Convolutional Neural Networks

1.6. Recurrent Neural Networks

1.7. Long Short Term Memory Networks

1.8. RNNs and Decision-Making

1.9. Spiking Neural Networks

1.10. SNNs and Bayesian Brain

2. Learning

2.1. Learning

2.2. How Brains Learn

2.3. How ANNs Learn

2.4. Backpropagation and Brains

2.5. Unsupervised Representation Learning

2.6. Autoencoders

2.7. GANs and Consciousness

2.8. Reinforcement Learning 1

2.9. Reinforcement Learning 2

2.10. Deep RL and Decision-Making

2.11. Deep RL and Cognitive Maps

2.12. Deep Meta-Reinforcement Learning

2.13. Innate Knowledge

Extra (from Q&As)

STDP

Neuronal subtypes

Dendrites

Glia, computation, cognition

Sleep

Misc.

B. FROM AWESOME NEURO-AI PAPERS:

Papers

Schneider, S., Lee, J. H., & Mathis, M. W. Learnable latent embeddings for joint behavioral and neural analysis arXiv (2022)

Raju, R. V., Guntupalli, J. S., Zhou, G., Lázaro-Gredilla, M., & George, D. Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus arXiv (2022)

Millet, J., Caucheteux, C., Orhan, P., Boubenec, Y., Gramfort, A., Dunbar, E., ... & King, J. R. Toward a realistic model of speech processing in the brain with self-supervised learning arXiv (2022)

Sucevic, J., & Schapiro, A. C. A neural network model of hippocampal contributions to category learning bioRxiv (2022)

Schmidgall, Samuel, and Joe Hays. Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks . bioRxiv (2022)

Adolfi, F., Bowers, J. S., & Poeppel, D. Successes and critical failures of neural networks in capturing human-like speech recognition arXiv (2022)

Bakhtiari, S., Mineault, P., Lillicrap, T., Pack, C., & Richards, B. The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning NeurIPS (2021)

Conwell, C., Mayo, D., Barbu, A., Buice, M., Alvarez, G., & Katz, B. Neural regression, representational similarity, model zoology & neural taskonomy at scale in rodent visual cortex NeurIPS (2021)

Krotov, Dmitry. Hierarchical associative memory arXiv (2021)

Krotov, Dmitry, and John Hopfield. Large associative memory problem in neurobiology and machine learning ICLR (2021)

Whittington, J. C., Warren, J., & Behrens, T. E. Relating transformers to models and neural representations of the hippocampal formation arXiv (2021)

Nonaka, S., Majima, K., Aoki, S. C., & Kamitani, Y. Brain hierarchy score: Which deep neural networks are hierarchically brain-like? IScience (2021)

Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., ... & Fedorenko, E. The neural architecture of language: Integrative modeling converges on predictive processing PNAS (2021)

Liang, Yuchen, Chaitanya K. Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed J. Zaki, and Dmitry Krotov. Can a Fruit Fly Learn Word Embeddings? ICLR (2021)

Liu, Helena Y., Stephen Smith, Stefan Mihalas, Eric Shea-Brown, and Uygar Sümbül Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network. Proceedings of the National Academy of Sciences of the United States of America (2021)

George, D., Rikhye, R. V., Gothoskar, N., Guntupalli, J. S., Dedieu, A., & Lázaro-Gredilla, M. Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps Nature communications (2021)

Whittington, J. C., Muller, T. H., Mark, S., Chen, G., Barry, C., Burgess, N., & Behrens, T. E. The Tolman-Eichenbaum machine: Unifying space and relational memory through generalization in the hippocampal formation Cell (2020)

Banino, A., Badia, A. P., Köster, R., Chadwick, M. J., Zambaldi, V., Hassabis, D. & Blundell, C. Memo: A deep network for flexible combination of episodic memories arXiv (2020)

Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, Daniel L. K. Yamins Unsupervised Neural Network Models of the Ventral Visual Stream bioRxiv (2020)

Tyler Bonnen, Daniel L.K. Yaminsa, Anthony D. Wagner When the ventral visual stream is not enough: A deep learning account of medial temporal lobe involvement in perception bioRxiv (2020)

Kim, K., Sano, M., De Freitas, J., Haber, N., & Yamins, D. Active World Model Learning with Progress Curiosity arXiv (2020)

Guangyu Robert Yang, Xiao-Jing Wang Artificial Neural Networks for Neuroscientists: A Primer Neuron (2020)

Glaser G.I., Benjamin, S.A., Chowdhury, H.R., Perich G.M., Miller, L.E., Kording, K.P. Machine Learning for Neural Decoding eNeuro (2020)

Jones, I. S., & Kording, K. P. Can Single Neurons Solve MNIST? The Computational Power of Biological Dendritic Trees arXiv (2020)

Rolnick, D., & Kording, K. Reverse-engineering deep ReLU networks ICML (2020)

Geirhos, R., Narayanappa, K., Mitzkus, B., Bethge, M., Wichmann, F. A., & Brendel, W. On the surprising similarities between supervised and self-supervised models arXiv (2020)

Storrs, K. R., Kietzmann, T. C., Walther, A., Mehrer, J., & Kriegeskorte, N. Diverse deep neural networks all predict human IT well, after training and fitting bioRxiv (2020)

Yonatan Sanz Perl, Hernán Boccacio, Ignacio Pérez-Ipiña, Federico Zamberlán, Helmut Laufs, Morten Kringelbach, Gustavo Deco, Enzo Tagliazucchi Generative embeddings of brain collective dynamics using variational autoencoders arXiv (2020)

George, D., Lazaro-Gredilla, M., Lehrach, W., Dedieu, A., & Zhou, G. A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model bioRxiv (2020)

van Bergen, R. S., & Kriegeskorte, N. Going in circles is the way forward: the role of recurrence in visual inference arXiv (2020)

Joseph G. Makin, David A. Moses, Edward F. Chang Machine translation of cortical activity to text with an encoder–decoder framework Nature Neuroscience (2020)

Richards, B. A., & Lillicrap, T. P. Dendritic solutions to the credit assignment problem Current opinion in neurobiology (2019)

Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M., & Tolias, A. S. Engineering a less artificial intelligence Neuron (2019)

Kubilius, J., Schrimpf, M., Kar, K., Rajalingham, R., Hong, H., Majaj, N. & DiCarlo, J. J. Brain-like object recognition with high-performing shallow recurrent ANNs Advances in Neural Information Processing Systems (2019)

Barrett, D. G., Morcos, A. S., & Macke, J. H. Analyzing biological and artificial neural networks: challenges with opportunities for synergy? Current opinion in neurobiology (2019)

Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M., & Harris, K. D. High-dimensional geometry of population responses in visual cortex Nature (2019)

Beniaguev David, Segev Idan, London Michael Single Cortical Neurons as Deep Artificial Neural Networks bioRxiv (2019)

Krotov, D. & Hopfield, J.J. Unsupervised learning by competing hidden units PNAS (2019)

Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, Wolfgang Maass A solution to the learning dilemma for recurrent 2 networks of spiking neurons bioRxiv (2019)

Albert Gidon, Timothy Adam Zolnik, Pawel Fidzinski, Felix Bolduan, Athanasia Papoutsi, Panayiota Poirazi, Martin Holtkamp, Imre Vida, Matthew Evan Larkum Dendritic action potentials and computation in human layer 2/3 cortical neurons Science (2019)

Adam Gaier, David Ha Weight Agnostic Neural Networks arXiv (2019)

Ben Sorscher, Gabriel C. Mel, Surya Ganguli, Samuel A. Ocko A unified theory for the origin of grid cells through the lens of pattern formation NeurIPS (2019)

Sara Hooker, Aaron Courville, Yann Dauphin, Andrea Frome Selective Brain Damage: Measuring the Disparate Impact of Model Pruning arXiv (2019)

Walker, E. Y., Sinz, F. H., Cobos, E., Muhammad, T., Froudarakis, E., Fahey, P. G. & Tolias, A. S. Inception loops discover what excites neurons most using deep predictive models Nature neuroscience (2019)

Alessio Ansuini, Alessandro Laio, Jakob H. Macke, Davide Zoccolan Intrinsic dimension of data representations in deep neural networks arXiv (2019)

Josh Merel, Diego Aldarondo, Jesse Marshall, Yuval Tassa, Greg Wayne, Bence Ölveczky Deep neuroethology of a virtual rodent arXiv (2019)

Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias Learning From Brains How to Regularize Machines arXiv (2019)

Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction NeurIPS (2019)

Stefano Recanatesi, Matthew Farrell ,Guillaume Lajoie, Sophie Deneve, Mattia Rigotti, and Eric Shea-Brown Predictive learning extracts latent space representations from sensory observations BiorXiv (2019)

Nasr, Khaled, Pooja Viswanathan, and Andreas Nieder. Number detectors spontaneously emerge in a deep neural network designed for visual object recognition. Science Advances (2019)

Bashivan, Pouya, Kohitij Kar, and James J. DiCarlo. Neural population control via deep image synthesis. Science (2019)

Ponce, Carlos R., Will Xiao, Peter F. Schade, Till S. Hartmann, Gabriel Kreiman, and Margaret S. Livingstone. Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences Cell (2019)

Kar, Kohitij, Jonas Kubilius, Kailyn M. Schmidt, Elias B. Issa, and James J. DiCarlo. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nature Neuroscience (2019)

Russin, Jake, Jason Jo, and Randall C. O'Reilly. Compositional generalization in a deep seq2seq model by separating syntax and semantics. arXiv (2019)

Rajalingham, Rishi, Elias B. Issa, Pouya Bashivan, Kohitij Kar, Kailyn Schmidt, and James J. DiCarlo. 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 (2018)

Eslami, SM Ali, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S. Morcos, Marta Garnelo, Avraham Ruderman et al. Neural scene representation and rendering. Science (2018)

Banino, Andrea, Caswell Barry, Benigno Uria, Charles Blundell, Timothy Lillicrap, Piotr Mirowski, Alexander Pritzel et al. Vector-based navigation using grid-like representations in artificial agents. Nature (2018)

Schrimpf, Martin, Kubilius, Jonas, Hong, Ha, Majaj, Najib J., Rajalingham, Rishi, Issa, Elias B., Kar, Kohitij, Bashivan, Pouya, Prescott-Roy, Jonathan, Geiger, Franziska, Schmidt, Kailyn, Yamins, Daniel L. K., and DiCarlo, James J. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? bioRxiv (2018)

Kell, A. J., Yamins, D. L., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy Neuron (2018)

Guerguiev, Jordan, Timothy P. Lillicrap, and Blake A. Richards. Towards deep learning with segregated dendrites. ELife (2017).

Kanitscheider, I., & Fiete, I. Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems arXiv (2017)

George, D., Lehrach, W., Kansky, K., Lázaro-Gredilla, M., Laan, C., Marthi, B., ... & Phoenix, D. S. A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs Science (2017)

DeWolf, T., Stewart, T. C., Slotine, J. J., & Eliasmith, C. A spiking neural model of adaptive arm control Proceedings of the Royal Society B: Biological Sciences, (2016)

Bengio, Yoshua, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, and Zhouhan Lin. Towards biologically plausible deep learning. arXiv (2015).

Güçlü, Umut, and Marcel AJ van Gerven. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. Journal of Neuroscience (2015)

Cadieu, Charles F., Ha Hong, Daniel LK Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, and James J. DiCarlo. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS computational biology (2014)

Reviews

Zador, A., Richards, B., Ölveczky, B., Escola, S., Bengio, Y., Boahen, K., ... & Tsao, D. Toward next-generation artificial intelligence: catalyzing the NeuroAI revolution arXiv (2022)

Doerig, A., Sommers, R., Seeliger, K., Richards, B., Ismael, J., Lindsay, G., ... & Kietzmann, T. C. The neuroconnectionist research programme arXiv (2022)

Lindsay, G. W. Convolutional neural networks as a model of the visual system: Past, present, and future arXiv (2021)

Hasselmo, M. E., Alexander, A. S., Hoyland, A., Robinson, J. C., Bezaire, M. J., Chapman, G. W., ... & Dannenberg, H. The Unexplored Territory of Neural Models: Potential Guides for Exploring the Function of Metabotropic Neuromodulation Neuroscience (2021)

Bermudez-Contreras, E., Clark, B.J., Wilber, A. The Neuroscience of Spatial Navigation and the Relationship to Artificial Intelligence Front. Comput. Neurosci. (2020)

Botvinick, M., Wang, J.X., Dabney, W., Miller, K.J., Kurth-Nelson, Z. Deep Reinforcement Learning and Its Neuroscientific Implications Neuron (2020)

Lillicrap, T.P., Santoro, A., Marris, L., Akerman, C.J. & Hinton, G. Backpropagation and the brain Nature Reviews Neuroscience, (2020)

Saxe, A., Nelli, S. & Summerfield, C. If deep learning is the answer, then what is the question? arXiv, (2020)

Hasson, U., Nastase, S. A., & Goldstein, A. Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks. Neuron (2020)

Schrimpf, M., Kubilius, J., Lee, M. J., Ratan Murty, N. A., Ajemian, R., & DiCarlo, J. J. Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron (2020)

Merel, J., Botvinick, M., & Wayne, G. Hierarchical motor control in mammals and machines Nature communications (2019)

Storrs, K. R., & Kriegeskorte, N. Deep learning for cognitive neuroscience. arXiv (2019)

Zador, M.Z. A critique of pure learning and what artificial neural networks can learn from animal brains, Nature Communications, (2019)

Richards, Blake A., Timothy P. Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath et al. A deep learning framework for neuroscience. Nature neuroscience (2019)

Kietzmann, T. C., McClure, P., & Kriegeskorte, N. (2018). Deep neural networks in computational neuroscience BioRxiv, (2018)

Hassabis, Demis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick. Neuroscience-inspired artificial intelligence. Neuron (2017)

Lake, Brenden M., Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman. Building machines that learn and think like people. Behavioral and brain sciences (2017).

Marblestone, Adam H., Greg Wayne, and Konrad P. Kording. Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience (2016)

Blogs

Mineault, Patrick What’s the endgame of neuroAI? (2022)

Mineault, Patrick Unsupervised models of the brain (2021)

Dettmers, Tim The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near (2015)

C. FROM INNOCENTI'S NEURO-AI PAPERS

Outline
Surveys
Deep learning
Reinforcement learning
- Reviews & perspectives
- Experiments
The Thousand Brains Theory

Outline

Is the brain a good model for machine intelligence? (2012)
What Intelligent Machines Need to Learn From the Neocortex by Hawkins (2017)
To Advance Artificial Intelligence, Reverse-Engineer the Brain by DiCarlo (2018);
The intertwined quest for understanding biological intelligence and creating artificial intelligence by Ganguli (2018)
How AI and neuroscience drive each other forwards by Savage (2019)
[Using neuroscience to develop artificial intelligence](https://science.sciencemag.org/content/363/6428/692 by Ullman (2019)

Surveys

Neuroscience-Inspired Artificial Intelligence by Hassabis et al. (2017)
Building machines that learn and think like people by Lake et al. (2017)
Cognitive computational neuroscience by Kriegeskorte & Douglas (2018)
Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research by Macpherson et al. (2021)
The roles of supervised machine learning in systems neuroscience by Glaser et al. (2019)
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated by Kumaran, Hassabis & McClelland (2016)
Computational Foundations of Natural Intelligence by van Gerven (2017)
Insights from the brain: The road towards Machine Intelligence by Thiboust (2020)
The Mutual Inspirations of Machine Learning and Neuroscience by Helmstaedter (2015)

Deep learning

Reinforcement learning

Reviews & perspectives

Deep Reinforcement Learning and Its Neuroscientific Implications by Botvinick et al. (2020)
A distributional code for value in dopamine-based reinforcement learning
Reinforcement Learning, Fast and Slow by Botvinick et al. (2019)
Reinforcement learning in artificial and biological systems by Neftci & Averbeck (2019)
The successor representation in human reinforcement learning

Experiments

Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments by Cross et al. (2021)
Validating the Representational Space of Deep Reinforcement Learning Models of Behavior with Neural Data by Bruch et al. (2021)

The Thousand Brains Theory

On Intelligence (2004)
A Thousand Brains: A New Theory of Intelligence by Hawkins (2021)
A thousand brains: toward biologically constrained AI by Hole & Ahmad (2021)
Grid Cell Path Integration For Movement-Based Visual Object Recognition by Leadholm, Lewis & Ahmad (2021)
A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex by Hawkins et al. (2019)
Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells by Lewis et al. (2019)
A Theory of How Columns in the Neocortex Enable Learning the Structure of the World by Hawkins, Ahmad & Cui (2017)
Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex by Hawkins & Ahmad (2016)

Background sources

Neuroscience

Principles of Neural Science by Kandel et al. (2021)
Neuroscience by Purves et al. (2018)
Principles of Neural Design by Sterling & Laughlin (2017)
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems by Abbott & Dayan (2005)
The Computational Brain by Churchland & Sejnowski (1992)
Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain by Lindsay (2021)
The Idea of the Brain: A History by Cobb (2020)
Brain Computation as Hierarchical Abstraction by Ballard (2015)

AI

Artificial Intelligence: A Modern Approach by Russell & Norvig (2020) - the equivalent bible of AI
Deep Learning for AI by Hinton, Bengio & LeCun (2021) - the most recent survey of deep learning
Deep Learning by Goodfellow et al. (2016)
The Deep Learning Revolution by Sejnowski (2018)
Neural Networks and Deep Learning by Nielsen (2015)
Dive Into Deep Learning: Tools for Engagement by Quinn et al. (2019)
Neural network models and deep learning by Kriegeskorte & Golan (2019) - a good primer on deep neural networks for biologists
Reinforcement Learning: An Introduction by Sutton & Barto (2018)

D. FROM HEBART LAB LIST:

Deep neural networks rival the representation of primate IT cortex for core visual object recognition by Cadieu, Charles F.; Hong, Ha; Yamins, Daniel L. K.; Pinto, Nicolas; Ardila, Diego; Solomon, Ethan A.; Majaj, Najib J.; DiCarlo, James J. (2014)
Feedforward object-vision models only tolerate small image variations compared to human by Ghodrati, Masoud; Farzmahdi, Amirhossein; Rajaei, Karim; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi (2014)
Deep supervised, but not unsupervised, models may explain IT cortical representation by Khaligh-Razavi, Seyed-Mahdi; Kriegeskorte, Nikolaus (2014)
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images by Nguyen, Anh; Yosinski, Jason; Clune, Jeff (2014)
Performance-optimized hierarchical models predict neural responses in higher visual cortex by Yamins, Daniel L. K.; Hong, Ha; Cadieu, Charles F.; Solomon, Ethan A.; Seibert, Darren; DiCarlo, James J. (2014)
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream by Güçlü, Umut; van Gerven, Marcel A. J. (2015)
Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing by Kriegeskorte, Nikolaus (2015)
Deep neural networks predict category typicality ratings for images by Lake, Brenden M.; Zaremba, Wojciech; Fergus, Rob; Gureckis, Todd M. (2015)
Deep learning by LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015)
Comparison of Object Recognition Behavior in Human and Monkey by Rajalingham, Rishi; Schmidt, Kailyn; DiCarlo, James J. (2015)
A Convolutional Subunit Model for Neuronal Responses in Macaque V1 by Vintch, Brett; Movshon, J. Anthony; Simoncelli, Eero P. (2015)
Model Constrained by Visual Hierarchy Improves Prediction of Neural Responses to Natural Scenes by Antolík, Ján; Hofer, Sonja B.; Bednar, James A.; Mrsic-Flogel, Thomas D. (2016)
Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence by Cichy, Radoslaw M.; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude (2016)
How Deep is the Feature Analysis underlying Rapid Visual Categorization? by Eberhardt, Sven; Cader, Jonah; Serre, Thomas (2016)
A specialized face-processing model inspired by the organization of monkey face patches explains several face-specific phenomena observed in humans by Farzmahdi, Amirhossein; Rajaei, Karim; Ghodrati, Masoud; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi (2016)
Visual Object Recognition: Do We (Finally) Know More Now Than We Did? by Gauthier, Isabel; Tarr, Michael J. (2016)
Visual scenes are categorized by function by Greene, Michelle R.; Baldassano, Christopher; Esteva, Andre; Beck, Diane M.; Fei-Fei, Li (2016)
Brains on Beats by Güçlü, Umut; Thielen, Jordy; Hanke, Michael; van Gerven, Marcel A. J. (2016)
Explicit information for category-orthogonal object properties increases along the ventral stream by Hong, Ha; Yamins, Daniel L. K.; Majaj, Najib J.; DiCarlo, James J. (2016)
Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder by Kheradpisheh, Saeed R.; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée (2016)
Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition by Kheradpisheh, Saeed R.; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée (2016)
Deep Neural Networks as a Computational Model for Human Shape Sensitivity by Kubilius, Jonas; Bracci, Stefania; Op de Beeck, Hans (2016)
Toward an Integration of Deep Learning and Neuroscience by Marblestone, Adam H.; Wayne, Greg; Kording, Konrad P. (2016)
Adapting Deep Network Features to Capture Psychological Representations by Peterson, Joshua C.; Abbott, Joshua T.; Griffiths, Thomas L. (2016)
Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks by Tripp, Bryan (2016)
Using goal-driven deep learning models to understand sensory cortex by Yamins, Daniel L. K.; DiCarlo, James J. (2016)
Modeling Human Categorization of Natural Images Using Deep Feature Representations by Battleday, Ruairidh M.; Peterson, Joshua C.; Griffiths, Thomas L. (2017)
Neural dynamics of real-world object vision that guide behaviour by Cichy, Radoslaw M.; Kriegeskorte, Nikolaus; Jozwik, Kamila M.; van den Bosch, Jasper J. F.; Charest, Ian (2017)
Human perception in computer vision by Dekel, Ron (2017)
Seeing it all: Convolutional network layers map the function of the human visual system by Eickenberg, Michael; Gramfort, Alexandre; Varoquaux, Gaël; Thirion, Bertrand (2017)
Comparing deep neural networks against humans: object recognition when the signal gets weaker by Geirhos, Robert; Janssen, David H. J.; Schütt, Heiko H.; Rauber, Jonas; Bethge, Matthias; Wichmann, Felix A. (2017)
Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks by Güçlü, Umut; van Gerven, Marcel A. J. (2017)
Increasingly complex representations of natural movies across the dorsal stream are shared between subjects by Güçlü, Umut; van Gerven, Marcel A. J. (2017)
Reconstructing perceived faces from brain activations with deep adversarial neural decoding by Güçlütürk, Yagmur; Güçlü, Umut; Seeliger, Katja; Bosch, Sander; van Lier, Rob; van Gerven, Marcel A. J. (2017)
Performance-optimized hierarchical models only partially predict neural responses during perceptual decision making by Gwilliams, Laura; King, Jean-Rémi (2017)
Neuroscience-Inspired Artificial Intelligence by Hassabis, Demis; Kumaran, Dharshan; Summerfield, Christopher; Botvinick, Matthew M. (2017)
Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features by Horikawa, Tomoyasu; Kamitani, Yukiyasu (2017)
Generic decoding of seen and imagined objects using hierarchical visual features by Horikawa, Tomoyasu; Kamitani, Yukiyasu (2017)
Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments by Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Storrs, Katherine R.; Mur, Marieke (2017)
Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons by Kalfas, Ioannis; Kumar, Satwant; Vogels, Rufin (2017)
Hard-wired feed-forward visual mechanisms of the brain compensate for affine variations in object recognition by Karimi-Rouzbahani, Hamid; Bagheri, Nasour; Ebrahimpour, Reza (2017)
Invariant object recognition is a personalized selection of invariant features in humans, not simply explained by hierarchical feed-forward vision models by Karimi-Rouzbahani, Hamid; Bagheri, Nasour; Ebrahimpour, Reza (2017)
How do targets, nontargets, and scene context influence real-world object detection? by Katti, Harish; Peelen, Marius V.; Arun, Sripati P. (2017)
Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models by Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick N.; Kriegeskorte, Nikolaus (2017)
Using deep learning to reveal the neural code for images in primary visual cortex by Kindel, William F.; Christensen, Elijah D.; Zylberberg, Joel (2017)
Neural system identification for large populations separating “what” and “where” by Klindt, David; Ecker, Alexander S.; Euler, Thomas; Bethge, Matthias (2017)
Building machines that learn and think like people by Lake, Brenden M.; Ullman, Tomer D.; Tenenbaum, Joshua B.; Gershman, Samuel J. (2017)
Transfer of View-manifold Learning to Similarity Perception of Novel Objects by Lin, Xingyu; Wang, Hao; Li, Zhihao; Zhang, Yimeng; Yuille, Alan; Lee, Tai Sing (2017)
Visual properties and memorising scenes: Effects of image-space sparseness and uniformity by Lukavský, Jiří; Děchtěrenko, Filip (2017)
Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks by Martin Cichy, Radoslaw; Khosla, Aditya; Pantazis, Dimitrios; Oliva, Aude (2017)
Posterior Inferotemporal Cortex Cells Use Multiple Input Pathways for Shape Encoding by Ponce, Carlos R.; Lomber, Stephen G.; Livingstone, Margaret S. (2017)
Characterizing the temporal dynamics of object recognition by deep neural networks : role of depth by Ramakrishnan, Kandan; Groen, Iris I. A.; Smeulders, Arnold W. M.; Steven Scholte, H.; Ghebreab, Sennay (2017)
A Balanced Comparison of Object Invariances in Monkey IT Neurons by Ratan Murty, N. Apurva; Arun, Sripati P. (2017)
Reward-based training of recurrent neural networks for cognitive and value-based tasks by Song, H. Francis; Yang, Guangyu R.; Wang, Xiao-Jing (2017)
A Deep-Dream Virtual Reality Platform for Studying Altered Perceptual Phenomenology by Suzuki, Keisuke; Roseboom, Warrick; Schwartzman, David J.; Seth, Anil K. (2017)
Invariant recognition drives neural representations of action sequences by Tacchetti, Andrea; Isik, Leyla; Poggio, Tomaso A. (2017)
Perception Science in the Age of Deep Neural Networks by VanRullen, Rufin (2017)
A parametric texture model based on deep convolutional features closely matches texture appearance for humans by Wallis, Thomas S. A.; Funke, Christina M.; Ecker, Alexander S.; Gatys, Leon A.; Wichmann, Felix A.; Bethge, Matthias (2017)
Methods and measurements to compare men against machines by Wichmann, Felix A.; Janssen, David H. J.; Geirhos, Robert; Aguilar, Guillermo; Schütt, Heiko H.; Maertens, Marianne; Bethge, Matthias (2017)
Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons by Zhuang, Chengxu; Wang, Yulong; Yamins, Daniel L. K.; Hu, Xiaolin (2017)
Sharpening of Hierarchical Visual Feature Representations of Blurred Images by Abdelhack, Mohamed; Kamitani, Yukiyasu (2018)
What deep learning can tell us about higher cognitive functions like mindreading? by Aru, Jaan; Vicente, Raul (2018)
Deep convolutional networks do not classify based on global object shape by Baker, Nicholas; Lu, Hongjing; Erlikhman, Gennady; Kellman, Philip J. (2018)
Vector-based navigation using grid-like representations in artificial agents by Banino, Andrea; Barry, Caswell; Uria, Benigno; Blundell, Charles; Lillicrap, Timothy P.; Mirowski, Piotr; Pritzel, Alexander; Chadwick, Martin J.; Degris, Thomas; Modayil, Joseph; Wayne, Greg; Soyer, Hubert; Viola, Fabio; Zhang, Brian; Goroshin, Ross; Rabinowitz, Neil; Pascanu, Razvan; Beattie, Charlie; Petersen, Stig; Sadik, Amir; Gaffney, Stephen; King, Helen; Kavukcuoglu, Koray; Hassabis, Demis; Hadsell, Raia; Kumaran, Dharshan (2018)
The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks by Bankson, B. B.; Hebart, Martin N.; Groen, Iris I. A.; Baker, Chris I. (2018)
Analyzing biological and artificial neural networks: challenges with opportunities for synergy? by Barrett, David G. T.; Morcos, Ari S.; Macke, Jakob H. (2018)
Neural Population Control via Deep ANN Image Synthesis by Bashivan, Pouya; Kar, Kohitij; DiCarlo, James J. (2018)
Computational mechanisms underlying cortical responses to the affordance properties of visual scenes by Bonner, Michael F.; Epstein, Russell A. (2018)
Integrated deep visual and semantic attractor neural networks predict fMRI pattern-information along the ventral object processing pathway by Devereux, Barry J.; Clarke, Alex; Tyler, Lorraine K. (2018)
Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models by Dezfouli, Amir; Morris, Richard; Ramos, Fabio; Dayan, Peter; Balleine, Bernard W. (2018)
Task-specific vision models explain task-specific areas of visual cortex by Dwivedi, Kshitij; Roig, Gemma (2018)
A rotation-equivariant convolutional neural network model of primary visual cortex by Ecker, Alexander S.; Sinz, Fabian H.; Froudarakis, Emmanouil; Fahey, Paul G.; Cadena, Santiago A.; Walker, Edgar Y.; Cobos, Erick; Reimer, Jacob; Tolias, Andreas S.; Bethge, Matthias (2018)
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans by Elsayed, Gamaleldin F.; Shankar, Shreya; Cheung, Brian; Papernot, Nicolas; Kurakin, Alex; Goodfellow, Ian; Sohl-Dickstein, Jascha (2018)
Common Object Representations for Visual Production and Recognition by Fan, Judith E.; Yamins, Daniel L. K.; Turk-Browne, Nicholas B. (2018)
Comparing continual task learning in minds and machines by Flesch, Timo; Balaguer, Jan; Dekker, Ronald; Nili, Hamed; Summerfield, Christopher (2018)
Using human brain activity to guide machine learning by Fong, Ruth C.; Scheirer, Walter J.; Cox, David D. (2018)
Human sensitivity to perturbations constrained by a model of the natural image manifold by Fruend, Ingo; Stalker, Elee (2018)
Generalisation in humans and deep neural networks by Geirhos, Robert; Medina Temme, Carlos R.; Rauber, Jonas; Schütt, Heiko H.; Bethge, Matthias; Wichmann, Felix A. (2018)
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness by Geirhos, Robert; Rubisch, Patricia; Michaelis, Claudio; Bethge, Matthias; Wichmann, Felix A.; Brendel, Wieland (2018)
The Roles of Supervised Machine Learning in Systems Neuroscience by Glaser, Joshua I.; Benjamin, Ari S.; Farhoodi, Roozbeh; Kording, Konrad P. (2018)
Shared spatiotemporal category representations in biological and artificial deep neural networks by Greene, Michelle R.; Hansen, Bruce C. (2018)
Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior by Groen, Iris I. A.; Greene, Michelle R.; Baldassano, Christopher; Fei-Fei, Li; Beck, Diane M.; Baker, Chris I. (2018)
Perceptual Dominance in Brief Presentations of Mixed Images: Human Perception vs. Deep Neural Networks by Gruber, Liron Z.; Haruvi, Aia; Basri, Ronen; Irani, Michal (2018)
Principles for models of neural information processing by Kay, Kendrick N. (2018)
A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy by Kell, Alexander J. E.; Yamins, Daniel L. K.; Shook, Erica N.; Norman-Haignere, Sam V.; McDermott, Josh H. (2018)
Cognitive computational neuroscience by Kriegeskorte, Nikolaus; Douglas, Pamela K. (2018)
Can deep neural networks rival human ability to generalize in core object recognition by Kubilius, Jonas; Kar, Kohitij; Schmidt, Kailyn; DiCarlo, James J. (2018)
Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex by Kuzovkin, Ilya; Vicente, Raul; Petton, Mathilde; Lachaux, Jean-Philippe; Baciu, Monica; Kahane, Philippe; Rheims, Sylvain; Vidal, Juan R.; Aru, Jaan (2018)
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning Agents by Leibo, Joel Z.; Masson d’Autume, Cyprien de; Zoran, Daniel; Amos, David; Beattie, Charles; Anderson, Keith; Castañeda, Antonio García; Sanchez, Manuel; Green, Simon; Gruslys, Audrunas; Legg, Shane; Hassabis, Demis; Botvinick, Matthew M. (2018)
How biological attention mechanisms improve task performance in a large-scale visual system model by Lindsay, Grace W.; Miller, Kenneth D. (2018)
Learning what and where to attend by Linsley, Drew; Shiebler, Dan; Eberhardt, Sven; Serre, Thomas (2018)
Mid-level visual features underlie the high-level categorical organization of the ventral stream by Long, Bria; Yu, Chen-Ping; Konkle, Talia (2018)
A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception by Lotter, William; Kreiman, Gabriel; Cox, David D. (2018)
Deep learning-Using machine learning to study biological vision by Majaj, Najib J.; Pelli, Denis G. (2018)
Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization by Masse, Nicolas Y.; Grant, Gregory D.; Freedman, David J. (2018)
Task-Driven Convolutional Recurrent Models of the Visual System by Nayebi, Aran; Bear, Daniel; Kubilius, Jonas; Kar, Kohitij; Ganguli, Surya; Sussillo, David; DiCarlo, James J.; Yamins, Daniel L. K. (2018)
Predicting eye movement patterns from fMRI responses to natural scenes by O’Connell, Thomas P.; Chun, Marvin M. (2018)
Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations by Peterson, Joshua C.; Abbott, Joshua T.; Griffiths, Thomas L. (2018)
Capturing human category representations by sampling in deep feature spaces by Peterson, Joshua C.; Suchow, Jordan W.; Aghi, Krisha; Ku, Alexander Y.; Griffiths, Thomas L. (2018)
Human peripheral blur is optimal for object recognition by Pramod, Raghavendraro T.; Katti, Harish; Arun, Sripati P. (2018)
Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture by Qiao, Kai; Zhang, Chi; Wang, Linyuan; Yan, Bin; Chen, Jian; Zeng, Lei; Tong, Li (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 by Rajalingham, Rishi; Issa, Elias B.; Bashivan, Pouya; Kar, Kohitij; Schmidt, Kailyn; DiCarlo, James J. (2018)
Multiplicative mixing of object identity and image attributes in single inferior temporal neurons by Ratan Murty, N. Apurva; Arun, Sripati P. (2018)
Optimizing deep video representation to match brain activity by Richard, Hugo; Pinho, Ana; Thirion, Bertrand; Charpiat, Guillaume (2018)
Control of synaptic plasticity in deep cortical networks by Roelfsema, Pieter R.; Holtmaat, Anthony (2018)
Totally Looks Like - How Humans Compare, Compared to Machines by Rosenfeld, Amir; Solbach, Markus D.; Tsotsos, John K. (2018)
Visual pathways from the perspective of cost functions and multi-task deep neural networks by Scholte, H. Steven; Losch, Max M.; Ramakrishnan, Kandan; Haan, Edward H. F. de; Bohte, Sander M. (2018)
Convolutional neural network-based encoding and decoding of visual object recognition in space and time by Seeliger, Katja; Fritsche, M.; Güçlü, Umut; Schoenmakers, S.; Schoffelen, J-M; Bosch, S. E.; van Gerven, Marcel A. J. (2018)
Generative adversarial networks for reconstructing natural images from brain activity by Seeliger, Katja; Güçlü, Umut; Ambrogioni, L.; Güçlütürk, Yagmur; van Gerven, Marcel A. J. (2018)
Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision by Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming (2018)
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video by Sinz, Fabian H.; Ecker, Alexander S.; Fahey, Paul G.; Walker, Edgar Y.; Cobos, Erick; Froudarakis, Emmanouil; Yatsenko, Dimitri; Pitkow, Xaq; Reimer, Jacob; Tolias, Andreas S. (2018)
The feature-weighted receptive field: an interpretable encoding model for complex feature spaces by St-Yves, Ghislain; Naselaris, Thomas (2018)
Invariant Recognition Shapes Neural Representations of Visual Input by Tacchetti, Andrea; Isik, Leyla; Poggio, Tomaso A. (2018)
Recurrent computations for visual pattern completion by Tang, Hanlin; Schrimpf, Martin; Lotter, William; Moerman, Charlotte; Paredes, Ana; Ortega Caro, Josue; Hardesty, Walter; Cox, David D.; Kreiman, Gabriel (2018)
Illusory Motion Reproduced by Deep Neural Networks Trained for Prediction by Watanabe, Eiji; Kitaoka, Akiyoshi; Sakamoto, Kiwako; Yasugi, Masaki; Tanaka, Kenta (2018)
Transferring and generalizing deep-learning-based neural encoding models across subjects by Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming (2018)
Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization by Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming (2018)
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision by Wen, Haiguang; Shi, Junxing; Zhang, Yizhen; Lu, Kun-Han; Cao, Jiayue; Liu, Zhongming (2018)
Deep Neural Networks for Modeling Visual Perceptual Learning by Wenliang, Li K.; Seitz, Aaron R. (2018)
Constraint-Free Natural Image Reconstruction From fMRI Signals Based on Convolutional Neural Network by Zhang, Chi; Qiao, Kai; Wang, Linyuan; Tong, Li; Zeng, Ying; Yan, Bin (2018)
Conflicting Bottom-up and Top-down Signals during Misrecognition of Visual Objects by Abdelhack, Mohamed; Kamitani, Yukiyasu (2019)
Neural population control via deep image synthesis by Bashivan, Pouya; Kar, Kohitij; DiCarlo, James J. (2019)
Reinforcement Learning, Fast and Slow by Botvinick, Matthew M.; Ritter, Sam; Wang, Jane X.; Kurth-Nelson, Zeb; Blundell, Charles; Hassabis, Demis (2019)
The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks by Bracci, Stefania; Ritchie, J. Brendan; Kalfas, Ioannis; Op de Beeck, Hans (2019)
Deep convolutional models improve predictions of macaque V1 responses to natural images by Cadena, Santiago A.; Denfield, George H.; Walker, Edgar Y.; Gatys, Leon A.; Tolias, Andreas S.; Bethge, Matthias; Ecker, Alexander S. (2019)
How well do deep neural networks trained on object recognition characterize the mouse visual system? by Cadena, Santiago A.; Sinz, Fabian H.; Muhammad, Taliah; Froudarakis, Emmanouil; Cobos, Erick; Walker, Edgar Y.; Reimer, Jake; Bethge, Matthias; Tolias, Andreas S.; Ecker, Alexander S. (2019)
BOLD5000, a public fMRI dataset while viewing 5000 visual images by Chang, Nadine; Pyles, John A.; Marcus, Austin; Gupta, Abhinav; Tarr, Michael J.; Aminoff, Elissa M. (2019)
The Roles of Statistics in Human Neuroscience by Chén, Oliver Y. (2019)
Deep Neural Networks as Scientific Models by Cichy, Radoslaw M.; Kaiser, Daniel (2019)
Disentangled behavioral representations by Dezfouli, A., Ashtiani, H., Ghattas, O., Nock, R., Dayan, P., & Ong, C. S. (2019)
Models that learn how humans learn: The case of decision-making and its disorders by Dezfouli, Amir; Griffiths, Kristi; Ramos, Fabio; Dayan, Peter; Balleine, Bernard W. (2019)
Human and DNN Classification Performance on Images With Quality Distortions: A Comparative Study by Dodge, Samuel; Karam, Lina (2019)
Do Primates and Deep Artificial Neural Networks Perform Object Categorization in a Similar Manner? by Gangopadhyay, Prabaha; Das, Jhilik (2019)
Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks by Grossman, Shany; Gaziv, Guy; Yeagle, Erin M.; Harel, Michal; Mégevand, Pierre; Groppe, David M.; Khuvis, Simon; Herrero, Jose L.; Irani, Michal; Mehta, Ashesh D.; Malach, Rafael (2019)
Levels of Representation in a Deep Learning Model of Categorization by Guest, Olivia; Love, Bradley C. (2019)
Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex by Han, Kuan; Wen, Haiguang; Shi, Junxing; Lu, Kun-Han; Zhang, Yizhen; Fu, Di; Liu, Zhongming (2019)
Comparing the Visual Representations and Performance of Humans and Deep Neural Networks by Jacobs, Robert A.; Bates, Christopher J. (2019)
Relating Simple Sentence Representations in Deep Neural Networks and the Brain by Jat, Sharmistha; Tang, Hao; Talukdar, Partha; Mitchell, Tom (2019)
To find better neural network models of human vision, find better neural network models of primate vision by Jozwik, Kamila M.; Schrimpf, Martin; Kanwisher, Nancy; DiCarlo, James J. (2019)
Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior by Kar, Kohitij; Kubilius, Jonas; Schmidt, Kailyn; Issa, Elias B.; DiCarlo, James J. (2019)
Machine vision benefits from human contextual expectations by Katti, Harish; Peelen, Marius V.; Arun, Sripati P. (2019)
Deep neural network models of sensory systems: windows onto the role of task constraints by Kell, Alexander J. E.; McDermott, Josh H. (2019)
Deep neural networks in computational neuroscience by Kietzmann, Tim C.; McClure, Patrick; Kriegeskorte, Nikolaus (2019)
Recurrence is required to capture the representational dynamics of the human visual system by Kietzmann, Tim C.; Spoerer, Courtney J.; Sörensen, Lynn K. A.; Cichy, Radoslaw M.; Hauk, Olaf; Kriegeskorte, Nikolaus (2019)
Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure by Kim, Been; Reif, Emily; Wattenberg, Martin; Bengio, Samy; Mozer, Michael C. (2019)
Neural network models and deep learning by Kriegeskorte, Nikolaus; Golan, Tal (2019)
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs by Kubilius, Jonas; Schrimpf, Martin; Kar, Kohitij; Hong, Ha; Majaj, Najib J.; Rajalingham, Rishi; Issa, Elias B.; Bashivan, Pouya; Prescott-Roy, Jonathan; Schmidt, Kailyn; Nayebi, Aran; Bear, Daniel; Yamins, Daniel L. K.; DiCarlo, James J. (2019)
Methods for computing the maximum performance of computational models of fMRI responses by Lage-Castellanos, Agustin; Valente, Giancarlo; Formisano, Elia; Martino, Federico de (2019)
Conscious perception of natural images is constrained by category-related visual features by Lindh, Daniel; Sligte, Ilja G.; Assecondi, Sara; Shapiro, Kimron L.; Charest, Ian (2019)
Human uncertainty makes classification more robust by Peterson, Joshua C.; Battleday, Ruairidh M.; Griffiths, Thomas L.; Russakovsky, Olga (2019)
Sensory processing and categorization in cortical and deep neural networks by Pinotsis, Dimitris A.; Siegel, Markus; Miller, Earl K. (2019)
Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences by Ponce, Carlos R.; Xiao, Will; Schade, Peter F.; Hartmann, Till S.; Kreiman, Gabriel; Livingstone, Margaret S. (2019)
Beyond core object recognition: Recurrent processes account for object recognition under occlusion by Rajaei, Karim; Mohsenzadeh, Yalda; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi (2019)
A deep learning framework for neuroscience by Richards, Blake A.; Lillicrap, Timothy P.; Beaudoin, Philippe; Bengio, Yoshua; Bogacz, Rafal; Christensen, Amelia; Clopath, Claudia; Costa, Rui Ponte; Berker, Archy de; Ganguli, Surya; Gillon, Colleen J.; Hafner, Danijar; Kepecs, Adam; Kriegeskorte, Nikolaus; Latham, Peter; Lindsay, Grace W.; Miller, Kenneth D.; Naud, Richard; Pack, Christopher C.; Poirazi, Panayiota; Roelfsema, Pieter R.; Sacramento, João; Saxe, Andrew; Scellier, Benjamin; Schapiro, Anna C.; Senn, Walter; Wayne, Greg; Yamins, Daniel L. K.; Zenke, Friedemann; Zylberberg, Joel; Therien, Denis; Kording, Konrad P. (2019)
Deep Learning: The Good, the Bad, and the Ugly by Serre, Thomas (2019)
Deep image reconstruction from human brain activity by Shen, Guohua; Horikawa, Tomoyasu; Majima, Kei; Kamitani, Yukiyasu (2019)
Comparison Against Task Driven Artificial Neural Networks Reveals Functional Organization of Mouse Visual Cortex by Shi, Jianghong; Shea-Brown, Eric; Buice, Michael A. (2019)
Engineering a Less Artificial Intelligence by Sinz, Fabian H.; Pitkow, Xaq; Reimer, Jacob; Bethge, Matthias; Tolias, Andreas S. (2019)
Deep Learning for Cognitive Neuroscience by Storrs, Katherine R.; Kriegeskorte, Nikolaus (2019)
Assessing Neural Network Scene Classification from Degraded Images by Tadros, Timothy; Cullen, Nicholas C.; Greene, Michelle R.; Cooper, Emily A. (2019)
Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network by Ukita, Jumpei; Yoshida, Takashi; Ohki, Kenichi (2019)
Reconstructing faces from fMRI patterns using deep generative neural networks by VanRullen, Rufin; Reddy, Leila (2019)
Image content is more important than Bouma’s Law for scene metamers by Wallis, Thomas S. A.; Funke, Christina M.; Ecker, Alexander S.; Gatys, Leon A.; Wichmann, Felix A.; Bethge, Matthias (2019)
Theories of Error Back-Propagation in the Brain by Whittington, James C. R.; Bogacz, Rafal (2019)
An integrative computational architecture for object-driven cortex by Yildirim, Ilker; Wu, Jiajun; Kanwisher, Nancy; Tenenbaum, Joshua B. (2019)
A critique of pure learning and what artificial neural networks can learn from animal brains by Zador, Anthony M. (2019)
Continual learning of context-dependent processing in neural networks by Zeng, Guanxiong; Chen, Yang; Cui, Bo; Yu, Shan (2019)
Humans can decipher adversarial images by Zhou, Zhenglong; Firestone, Chaz (2019)
Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models by Zhu, Hongru; Tang, Peng; Park, Jeongho; Park, Soojin; Yuille, Alan (2019)
A unified theory of early visual representations from retina to cortex through anatomically constrained deep cnNs by Lindsey, Jack; Ocko, Samuel A.; Ganguli, Surya; Deny, Stephane (2019)
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction by Tanaka, Hidenori; Nayebi, Aran; Maheswaranathan, Niru; McIntosh, Lane; Baccus, Stephen; Ganguli, Surya (2019)
Capturing human categorization of natural images by combining deep networks and cognitive models by Battleday, Ruairidh M.; Peterson, Joshua C.; Griffiths, Thomas L. (2020)
Minimal videos: Trade-off between spatial and temporal information in human and machine vision by Ben-Yosef, Guy; Kreiman, Gabriel; Ullman, Shimon (2020)
DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains by Chen, Xiayu; Zhou, Ming; Gong, Zhengxin; Xu, Wei; Liu, Xingyu; Huang, Taicheng; Zhen, Zonglei; Liu, Jia (2020)
Models of primate ventral stream that categorize and visualize images by Christensen, Elijah D.; Zylberberg, Joel (2020)
Crowding reveals fundamental differences in local vs. global processing in humans and machines by Doerig, Adrien; Bornet, A.; Choung, O. H.; Herzog, Michael H. (2020)
Capsule Networks as Recurrent Models ofGrouping and Segmentation by Doerig, Adrien; Schmittwilken, Lynn; Sayim, Bilge; Manassi, Mauro; Herzog, Michael H. (2020)
What do adversarial images tell us about human vision? by Dujmović, Marin; Malhotra, Gaurav; Bowers, Jeffrey S. (2020)
Unraveling Representations in Scene-selective Brain Regions Using Scene-Parsing Deep Neural Networks by Dwivedi, Kshitij; Cichy, Radoslaw M.; Roig, Gemma (2020)
Relating Visual Production and Recognition of Objects in Human Visual Cortex by Fan, Judith E.; Wammes, Jeffrey D.; Gunn, Jordan B.; Yamins, Daniel L. K.; Norman, Kenneth A.; Turk-Browne, Nicholas B. (2020)
Training neural networks to mimic the brain improves object recognition performance by Federer, Callie; Xu, Haoyan; Fyshe, Alona; Zylberberg, Joel (2020)
Constrained sampling from deep generative image models reveals mechanisms of human target detection by Fruend, Ingo (2020)
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency by Geirhos, Robert; Meding, Kristof; Wichmann, Felix A. (2020)
Controversial stimuli: Pitting neural networks against each other as models of human cognition by Golan, Tal; Raju, Prashant C.; Kriegeskorte, Nikolaus (2020)
Extracting low-dimensional psychological representations from convolutional neural networks by Jha, Aditi; Peterson, Joshua C.; Griffiths, Thomas L. (2020)
Image memorability is predicted at different stages of a convolutional neural network by Koch, Griffin E.; Akpan, Essang; Coutanche, Marc N. (2020)
Time-resolved correspondences between deep neural network layers and EEG measurements in object processing by Kong, Nathan C. L.; Kaneshiro, Blair; Yamins, Daniel L. K.; Norcia, Anthony M. (2020)
Topographic deep artificial neural networks reproduce the hallmarks of the primate inferior temporal cortex face processing network by Lee, Hyodong; Margalit, Eshed; Jozwik, Kamila M.; Cohen, Michael A.; Kanwisher, Nancy; Yamins, Daniel L. K.; DiCarlo, James J. (2020)
Backpropagation and the brain by Lillicrap, Timothy P.; Santoro, Adam; Marris, Luke; Akerman, Colin J.; Hinton, Geoffrey (2020)
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future by Lindsay, Grace W. (2020)
Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations by Marques, T.; Schrimpf, Martin; Geiger, Franziska; Cox, David D.; DiCarlo, James J. (2020)
Individual differences among deep neural network models by Mehrer, Johannes; Spoerer, Courtney J.; Kriegeskorte, Nikolaus; Kietzmann, Tim C. (2020)
Brain Hierarchy Score: Which Deep Neural Networks are Hierarchically Brain-Like? by Nonaka, Soma; Majima, Kei; Aoki, Shuntaro C.; Kamitani, Yukiyasu (2020)
Improving Machine Vision using Human Perceptual Representations: The Case of Planar Reflection Symmetry for Object Classification by Pramod, Raghavendraro T.; Sp, Arun (2020)
The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys by Rajalingham, Rishi; Kar, Kohitij; Sanghavi, Sachi; Dehaene, Stanislas; DiCarlo, James J. (2020)
Training Deep Networks to Construct a Psychological Feature Space for a Natural-Object Category Domain by Sanders, Craig A.; Nosofsky, Robert M. (2020)
Brain-score: Which artificial neural network for object recognition is most brain-like? by Schrimpf, Martin; Kubilius, Jonas; Hong, Ha; Majaj, Najib J.; Rajalingham, Rishi; Issa, Elias B.; Kar, Kohitij; Bashivan, Pouya; Prescott-Roy, Jonathan; Geiger, Franziska; Schmidt, Kailyn; Yamins, Daniel L. K.; DiCarlo, James J. (2020)
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior by Singh, Pulkit; Peterson, Joshua C.; Battleday, Ruairidh M.; Griffiths, Thomas L. (2020)
Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision by Spoerer, Courtney J.; Kietzmann, Tim C.; Mehrer, Johannes; Charest, Ian; Kriegeskorte, Nikolaus (2020)
Diverse deep neural networks all predict human IT well, after training and fitting by Storrs, Katherine R.; Kietzmann, Tim C.; Walther, Alexander; Mehrer, Johannes; Kriegeskorte, Nikolaus (2020)
Visual perception of liquids: Insights from deep neural networks by van Assen, Jan Jaap R.; Nishida, Shin’ya; Fleming, Roland W. (2020)
Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation by van Dyck, Leonard E.; Gruber, Walter R. (2020)
Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception by Vinken, Kasper; Boix, Xavier; Kreiman, Gabriel (2020)
Deep Neural Networks Point to Mid-level Complexity of Rodent Object Vision by Vinken, Kasper; Op de Beeck, Hans (2020)
A neural basis of probabilistic computation in visual cortex by Walker, Edgar Y.; Cotton, R. James; Ma, Wei Ji; Tolias, Andreas S. (2020)
Recent advances in understanding object recognition in the human brain: deep neural networks, temporal dynamics, and context by Wardle, Susan G.; Baker, Chris I. (2020)
Explainable Deep Learning: A Field Guide for the Uninitiated by Xie, Ning; Ras, Gabrielle; van Gerven, Marcel A. J.; Doran, Derek (2020)
Efficient inverse graphics in biological face processing by Yildirim, Ilker; Belledonne, Mario; Freiwald, Winrich; Tenenbaum, Joshua B. (2020)
Orthogonal Representations of Object Shape and Category in Deep Convolutional Neural Networks and Human Visual Cortex by Zeman, Astrid A.; Ritchie, J. Brendan; Bracci, Stefania; Op de Beeck, Hans (2020)
Semantic transparency is not invisibility: A computational model of perceptually-grounded conceptual combination in word processing by Günther, Fritz; Petilli, Marco A.; Marelli, Marco (2020)
Images of the unseen: extrapolating visual representations for abstract and concrete words in a data-driven computational model by Günther, Fritz; Petilli, Marco A.; Vergallito, Alessandra; Marelli, Marco (2020)
But Still It Moves: Static Image Statistics Underlie How We See Motion by Rideaux, Reuben;Welchman, Andrew E. (2020)
Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence by Schrimpf, Martin; Kubilius, Jonas; Lee, Michael J.; Ratan Murty, N. Apurva; Ajemian, Robert; DiCarlo, James J. (2020)
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream by Geiger, Franziska; Schrimpf, Martin; Marques, Tiago; DiCarlo, James J. (2020)
Generative Adversarial Phonology: Modeling Unsupervised Phonetic and Phonological Learning With Neural Networks by Beguš, Gašper (2020)
Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision by Avberšek, Lev Kiar; Zeman, Astrid A.; Op de Beeck, Hans (2021)
The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning by Bakhtiari, Shahab; Mineault, Patrick; Lillicrap, Tim; Pack, Christopher C.; Richards, Blake A. (2021)
From convolutional neural networks to models of higher-level cognition (and back again) by Battleday, Ruairidh M.; Peterson, Joshua C.; Griffiths, Thomas L. (2021)
Object-scene conceptual regularities reveal fundamental differences between 3 biological and artificial object vision by Bracci, Stefania; Mraz, Jakob; Zeman, Astrid A.; Leys, Gaëlle; Op de Beeck, Hans (2021)
Learning divisive normalization in primary visual cortex by Burg, Max F.; Cadena, Santiago A.; Denfield, George H.; Walker, Edgar Y.; Tolias, Andreas S.; Bethge, Matthias; Ecker, Alexander S. (2021)
Unveiling functions of the visual cortex using task-specific deep neural networks by Dwivedi, Kshitij; Bonner, Michael F.; Cichy, Radoslaw M.; Roig, Gemma (2021)
Five points to check when comparing visual perception in humans and machines by Funke, Christina M.; Borowski, Judy; Stosio, Karolina; Brendel, Wieland; Wallis, Thomas S. A.; Bethge, Matthias (2021)
Partial success in closing the gap between human and machine vision by Geirhos, Robert; Narayanappa, Kantharaju; Mitzkus, Benjamin; Thieringer, Tizian; Bethge, Matthias; Wichmann, Felix A.; Brendel, Wieland (2021)
The visual and semantic features that predict object memory: Concept property norms for 1,000 object images by Hovhannisyan, Mariam; Clarke, Alex; Geib, Benjamin R.; Cicchinelli, Rosalie; Monge, Zachary; Worth, Tory; Szymanski, Amanda; Cabeza, Roberto; Davis, Simon W. (2021)
Noise-robust recognition of objects by humans and deep neural networks by Jang, Hojin; McCormack, Devin; Tong, Frank (2021)
Convolutional neural networks trained with a developmental sequence of blurry to clear images reveal core differences between face and object processing by Jang, Hojin; Tong, Frank (2021)
General object-based features account for letter perception better than specialized letter features by Janini, Daniel; Hamblin, Chris; Deza, Arturo; Konkle, Talia (2021)
Face dissimilarity judgements are predicted by representational distance in deep neural networks and principal-component face space by Jozwik, Kamila M.; O’Keeffe, Jonathan; Storrs, Katherine R.; Kriegeskorte, Nikolaus (2021)
Beyond category-supervision: instance-level contrastive learning models predict human visual system responses to objects by Konkle, Talia; Alvarez, George A. (2021)
Passive attention in artificial neural networks predicts human visual selectivity by Langlois, Thomas A.; Charles Zhao, H.; Grant, Erin; Dasgupta, Ishita; Griffiths, Thomas L.; Jacoby, Nori (2021)
Tuning in scene-preferring cortex for mid-level visual features gives rise to selectivity across multiple levels of stimulus complexity by Li, Shi Pui Donald; Bonner, Michael F. (2021)
A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities by Lonnqvist, Ben; Bornet, Alban; Doerig, Adrien; Herzog, Michael H. (2021)
An ecologically motivated image dataset for deep learning yields better models of human vision by Mehrer, Johannes; Spoerer, Courtney J.; Jones, Emer C.; Kriegeskorte, Nikolaus; Kietzmann, Tim C. (2021)
Computational models of category-selective brain regions enable high-throughput tests of selectivity by Murty, N. Apurva Ratan; Bashivan, Pouya; Abate, Alex; DiCarlo, James J.; Kanwisher, Nancy (2021)
THINGSvision: a Python toolbox for streamlining the extraction of activations from deep neural networks by Muttenthaler, Lukas; Hebart, Martin N. (2021)
Unsupervised Models of Mouse Visual Cortex by Nayebi, Aran; Kong, Nathan C. L.; Zhuang, Chengxu; Norcia, Anthony M.; Gardner, Justin L.; Yamins, Daniel L. K. (2021)
Deep Neural Network Models of Object Recognition Exhibit Human-Like Limitations when Performing Visual Search Tasks by Nicholson, David A.; Prinz, Astrid A. (2021)
If deep learning is the answer, what is the question? by Saxe, Andrew; Nelli, Stephanie; Summerfield, Christopher (2021)
End-to-end neural system identification with neural information flow by Seeliger, Katja; Ambrogioni, L.; Güçlütürk, Yagmur; van den Bulk, L. M.; Güçlü, Umut; van Gerven, Marcel A. J. (2021)
From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction by Singer, Johannes J. D.; Seeliger, Katja; Kietzmann, Tim C.; Hebart, Martin N. (2021)
The Geometry of Concept Learning by Sorscher, Ben; Ganguli, Surya; Sompolinsky, Haim (2021)
Using deep neural networks to evaluate object vision tasks in rats by Vinken, Kasper; Op de Beeck, Hans (2021)
Correspondence between Monkey Visual Cortices and Layers of a Saliency Map Model Based on a Deep Convolutional Neural Network for Representations of Natural Images by Wagatsuma, Nobuhiko; Hidaka, Akinori; Tamura, Hiroshi (2021)
Convolutional neural networks do not develop brain-like transformation tolerant visual representations by Xu, Yaoda; Vaziri-Pashkam, Maryam (2021)
Examining the Coding Strength of Object Identity and Nonidentity Features in Human Occipito-Temporal Cortex and Convolutional Neural Networks by Xu, Yaoda; Vaziri-Pashkam, Maryam (2021)
Limits to visual representational correspondence between convolutional neural networks and the human brain by Xu, Yaoda; Vaziri-Pashkam, Maryam (2021)
Unsupervised neural network models of the ventral visual stream by Zhuang, Chengxu; Yan, Siming; Nayebi, Aran; Schrimpf, Martin; Frank, Michael C.; DiCarlo, James J.; Yamins, Daniel L. K. (2021)
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? by Zimmermann, Roland S.; Borowski, Judy; Geirhos, Robert; Bethge, Matthias; Wallis, Thomas S. A.; Brendel, Wieland (2021)
Qualitative similarities and differences in visual object representations between brains and deep networks by Jacob,Georgin ; R. T., Pramod; Katti, Harish; S. P., Arun; (2021)
Data-driven computational models reveal perceptual simulation in word processing by Petilli, Marco A.; Günther, Fritz; Vergallito, Alessandra; Ciapparelli, Marco; Marelli, Marco (2021)
Unveiling functions of the visual cortex using task-specific deep neural networks by Dwivedi, Kshitij; Bonner, Michael F.; Cichy, Radoslaw Martin; Gemma Roig (2021)
The neural architecture of language: Integrative modeling converges on predictive processing by Schrimpf, Martin; Blank, Idan Asher; Tuckute, Greta; Kauf, Carina; Hosseini, Eghbal A.; Kanwisher, Nancy; Tenenbaum, Joshua B.; Fedorenko, Evelina (2021)
The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motion by Cichy, Radoslaw Martin; Dwivedi, Kshitij; Lahner, Benjamin; Lascelles, Alex; Iamshchinina, Polina; Graumann, Monika; Andonian, Alex; Ratan Murty, N Apurva; Kay, Kendrick; Roig, Gemma; Oliva, Aude (2021)
Deep neural networks and visuo-semantic models explain complementary components of human ventral-stream representational dynamics by Jozwik, K. M., Kietzmann, T. C., Cichy, R. M., Kriegeskorte, N., & Mur, M. (2021)
Not so fast: Limited validity of deep convolutional neural networks as in silico models for human naturalistic face processing by Jiahui, Guo; Feilong, Ma; Visconti di Oleggio Castello, Matteo; Nastase, Samuel A.; Haxby, James V.; Gobbini, M. Ida (2021)
Grounding deep neural network predictions of human categorization behavior in understandable functional features: The case of face identity by Daube, Christoph; Xu, Tian; Zhan, Jiayu; Webb, Andrew; Ince, Robin A. A.; Garrod, Oliver G. B.; Schyns, Philippe (2021)
A self-supervised deep neural network for image completion resembles early visual cortex fMRI activity patterns for occluded scenes by Svanera, Michele; Morgan, Andrew T; Petro, Lucy S; Muckli, Lars (2021)
Comparing Object Recognition in Humans and Deep Convolutional Neural Networks – An Eye Tracking Study by van Dyck, Leonard E.; Kwitt, Roland; Denzler, Sebastian J.; Gruber, Walter R. (2021)
Local and non-local dependency learning and emergence of rule-like representations in speech data by deep convolutional generative adversarial networks by Beguš, Gašper (2022)
Brain-like functional specialization emerges spontaneously in deep neural networks by Dobs, Katharina; Martinez, Julio; Kell, Alexander; Kanwisher, Nancy (2022)
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements. by van Dyck, Leonard E.; Denzler, Sebastian J.; Gruber, Walter R. (2022)

E. FROM MILLIDGE'S LIST:

Surveys

What does the free energy principle tell us about the brain?, (2019) by Samuel J Gershman
The free-energy principle: a unified brain theory?, (2010) by Karl Friston
A tutorial on the free-energy framework for modelling perception and learning, (2017) by Rafal Bogacz
The free energy principle for action and perception: A mathematical review, (2017) by Christopher L Buckley and Chang Sub Kim and Simon McGregor and Anil K Seth
A Step-by-Step Tutorial on Active Inference and its Application to Empirical Data, (2021) by Ryan Smith and Karl Friston and Christopher Whyte

Classics

A free energy principle for a particular physics, (2019) by Karl Friston
A free energy principle for the brain, (2006) by Karl Friston and James Kilner and Lee Harrison
A theory of cortical responses, (2005) by Karl Friston
Learning and inference in the brain, (2003) by Karl Friston
Reinforcement learning or active inference?, (2009) by Karl J Friston and Jean Daunizeau and Stefan J Kiebel
Action understanding and active inference, (2011) by Karl Friston and J{'e}r{'e}mie Mattout and James Kilner
A free energy principle for biological systems, (2012) by Friston Karl
Of woodlice and men, (2018) by Martin Fortier and Daniel A Friedman
The history of the future of the Bayesian brain, (2012) by Karl Friston
Free energy, value, and attractors, (2012) by Karl Friston and Ping Ao
Attention, uncertainty, and free-energy, (2010) by Harriet Feldman and Karl Friston
Hierarchical models in the brain, (2008) by Karl Friston
DEM: a variational treatment of dynamic systems, (2008) by Karl J Friston and N Trujillo-Barreto and Jean Daunizeau
Generalised filtering, (2010) by Karl Friston and Klaas Stephan and Baojuan Li and Jean Daunizeau
Surfing uncertainty: Prediction, action, and the embodied mind, (2015) by Andy Clark
Variational filtering, (2008) by Karl J Friston
Action and behavior: a free-energy formulation, (2010) by Karl J Friston and Jean Daunizeau and James Kilner and Stefan J Kiebel

Philosophical Analyses

The Markov Blanket Trick: On the Scope of the Free Energy Principle and Active Inference, (2021) by Vicente Raja and Dinesh Valluri and Edward Baggs and Anthony Chemero and Michael L Aderson
How particular is the physics of the Free Energy Principle?, (2021) by Miguel Aguilera and Beren Millidge and Alexander Tschantz and Christopher L Buckley
A tale of two densities: Active inference is enactive inference, (2020) by Maxwell JD Ramstead and Michael D Kirchhoff and Karl J Friston
Answering Schr{"o}dinger's question: A free-energy formulation, (2018) by Maxwell James D{'e}sormeau Ramstead and Paul Benjamin Badcock and Karl John Friston
Thinking through other minds: A variational approach to cognition and culture, (2020) by Samuel PL Veissi{`e}re and Axel Constant and Maxwell JD Ramstead and Karl J Friston and Laurence J Kirmayer
TTOM in action: Refining the variational approach to cognition and culture, (2020) by Samuel PL Veissi{`e}re and Axel Constant and Maxwell JD Ramstead and Karl J Friston and Laurence J Kirmayer
What does the free energy principle tell us about the brain?, (2019) by Samuel J Gershman
The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective, (2018) by Jelle Bruineberg and Julian Kiverstein and Erik Rietveld
Predictive processing and the representation wars, (2018) by Daniel Williams
Whatever next? Predictive brains, situated agents, and the future of cognitive science, (2013) by Andy Clark
Predictions in the eye of the beholder: an active inference account of Watt governors, (2020) by Manuel Baltieri and Christopher L Buckley and Jelle Bruineberg
From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition, (2020) by Andrew W Corcoran and Giovanni Pezzulo and Jakob Hohwy
Interoceptive inference, emotion, and the embodied self, (2013) by Anil K Seth
Active interoceptive inference and the emotional brain, (2016) by Anil K Seth and Karl J Friston
The cybernetic Bayesian brain, (2014) by Anil K Seth
Presence, objecthood, and the phenomenology of predictive perception, (2015) by Anil K Seth
The Math is not the Territory: Navigating the Free Energy Principle, (2020) by Mel Andrews

Self-Organisation and Markov Blankets

Life as we know it, (2013) by Karl Friston
Knowing one's place: a free-energy approach to pattern regulation, (2015) by Karl Friston and Michael Levin and Biswa Sengupta and Giovanni Pezzulo
Morphogenesis as Bayesian inference: A variational approach to pattern formation and control in complex biological systems, (2019) by Franz Kuchling and Karl Friston and Georgi Georgiev and Michael Levin
Neural and phenotypic representation under the free-energy principle, (2020) by Maxwell JD Ramstead and Casper Hesp and Alexander Tschantz and Ryan Smith and Axel Constant and Karl Friston
Parcels and particles: Markov blankets in the brain, (2020) by Karl J Friston and Erik D Fagerholm and Tahereh S Zarghami and Thomas Parr and In{^e}s Hip{'o}lito and Lo{"\i}c Magrou and Adeel Razi
Markov blankets in the brain, (2020) by Ines Hipolito and Maxwell Ramstead and Laura Convertino and Anjali Bhat and Karl Friston and Thomas Parr
Modules or Mean-Fields?, (2020) by Thomas Parr and Noor Sajid and Karl J Friston
Biological self-organisation and Markov blankets, (2017) by Ensor Rafael Palacios and Adeel Razi and Thomas Parr and Michael Kirchhoff and Karl Friston
The Emperor’s New Markov Blankets, (2020) by Jelle Bruineberg and Krzysztof Dolega and Joe Dewhurst and Manuel Baltieri

Information Geometry

Markov blankets, information geometry and stochastic thermodynamics, (2020) by Thomas Parr and Lancelot Da Costa and Karl Friston

Active Inference Outline

Surveys and Tutorials

Active inference on discrete state-spaces: a synthesis, (2020) by Lancelot Da Costa and Thomas Parr and Noor Sajid and Sebastijan Veselic and Victorita Neacsu and Karl Friston

Discrete State Space Formulation

Active inference and epistemic value, (2015) by Karl Friston and Francesco Rigoli and Dimitri Ognibene and Christoph Mathys and Thomas Fitzgerald and Giovanni Pezzulo
Active inference and learning, (2016) by Karl Friston and Thomas FitzGerald and Francesco Rigoli and Philipp Schwartenbeck and Giovanni Pezzulo and others
Active inference and agency: optimal control without cost functions, (2012) by Karl Friston and Spyridon Samothrakis and Read Montague
Active inference: a process theory, (2017) by Karl Friston and Thomas FitzGerald and Francesco Rigoli and Philipp Schwartenbeck and Giovanni Pezzulo
Uncertainty, epistemics and active inference, (2017) by Thomas Parr and Karl J Friston
Deep temporal models and active inference, (2018) by Karl J Friston and Richard Rosch and Thomas Parr and Cathy Price and Howard Bowman
Sophisticated Inference, (2020) by Karl Friston and Lancelot Da Costa and Danijar Hafner and Casper Hesp and Thomas Parr
Active inference: demystified and compared, (2019) by Noor Sajid and Philip J Ball and Karl J Friston
The relationship between dynamic programming and active inference: The discrete, finite-horizon case, (2020) by Lancelot Da Costa and Noor Sajid and Thomas Parr and Karl Friston and Ryan Smith

Continuous Time Formulation

Reinforcement learning or active inference?, (2009) by Karl J Friston and Jean Daunizeau and Stefan J Kiebel
An active inference implementation of phototaxis, (2017) by Manuel Baltieri and Christopher L Buckley

Active inference in plants!!!

PID control as a process of active inference with linear generative models, (2019) by Manuel Baltieri and Christopher L Buckley
On Kalman-Bucy filters, linear quadratic control and active inference, (2020) by Manuel Baltieri and Christopher L Buckley
Application of the Free Energy Principle to Estimation and Control, (2019) by Thijs van de Laar and Ay{\c{c}}a {"O}z{\c{c}}elikkale and Henk Wymeersch
The State Space Formulation of Active Inference: Towards Brain-Inspired Robot Control, (2019) by Sherin Grimbergen
Hierarchical active inference: A theory of motivated control, (2018) by Giovanni Pezzulo and Francesco Rigoli and Karl J Friston

Message Passing and Free Energies

The graphical brain: belief propagation and active inference, (2017) by Karl J Friston and Thomas Parr and Bert de Vries
Neuronal message passing using Mean-field, Bethe, and Marginal approximations, (2019) by Thomas Parr and Dimitrije Markovic and Stefan J Kiebel and Karl J Friston
Active inference, belief propagation, and the bethe approximation, (2018) by Sarah Schw{"o}bel and Stefan Kiebel and Dimitrije Markovi{'c}
Generalised free energy and active inference, (2019) by Thomas Parr and Karl J Friston
Whence the Expected Free Energy?, (2020) by Beren Millidge and Alexander Tschantz and Christopher L Buckley
On the Relationship Between Active Inference and Control as Inference, (2020) by Beren Millidge and Alexander Tschantz and Anil K Seth and Christopher L Buckley

Active Inference for Control Theory/Robotics

Active inference and robot control: a case study, (2016) by L{'e}o Pio-Lopez and Ange Nizard and Karl Friston and Giovanni Pezzulo
Active inference body perception and action for humanoid robots, (2019) by Guillermo Oliver and Pablo Lanillos and Gordon Cheng
End-to-end pixel-based deep active inference for body perception and action, (2019) by Cansu Sancaktar and Pablo Lanillos
Active inference for robot control: A factor graph approach, (2019) by Mees Vanderbroeck and Mohamed Baioumy and Daan van der Lans and Rens de Rooij and Tiis van der Werf
A novel adaptive controller for robot manipulators based on active inference, (2020) by Corrado Pezzato and Riccardo Ferrari and Carlos Hern{'a}ndez Corbato
Adaptive robot body learning and estimation through predictive coding, (2018) by Pablo Lanillos and Gordon Cheng

Neuroscience and Computational Psychiatry Applications

Recent advances in the application of predictive coding and active inference models within clinical neuroscience, (2020) by Ryan Smith and Paul Badcock and Karl J Friston
The Predictive Global Neuronal Workspace: A Formal Active Inference Model of Visual Consciousness, (2020) by Christopher J Whyte and Ryan Smith
Neurocomputational mechanisms underlying emotional awareness: insights afforded by deep active inference and their potential clinical relevance, (2019) by Ryan Smith and Richard D Lane and Thomas Parr and Karl J Friston
Simulating emotions: An active inference model of emotional state inference and emotion concept learning, (2019) by Ryan Smith and Thomas Parr and Karl J Friston
The hierarchical basis of neurovisceral integration, (2017) by Ryan Smith and Julian F Thayer and Sahib S Khalsa and Richard D Lane
Active inference in OpenAI gym: a paradigm for computational investigations into psychiatric illness, (2018) by Maell Cullen and Ben Davey and Karl J Friston and Rosalyn J Moran

Deep Active Inference

Reinforcement Learning through Active Inference, (2020) by Alexander Tschantz and Beren Millidge and Anil K Seth and Christopher L Buckley
Scaling active inference, (2020) by Alexander Tschantz and Manuel Baltieri and Anil K Seth and Christopher L Buckley
Deep active inference as variational policy gradients, (2020) by Beren Millidge
Deep active inference, (2018) by Kai Ueltzh{"o}ffer
Deep active inference agents using Monte-Carlo methods, (2020) by Zafeirios Fountas and Noor Sajid and Pedro AM Mediano and Karl Friston

Acknowledgments

This list was based on:

ckarageorgkaneen/neuroaid

neuroaid

A. FROM NEURO-AI COURSE:

1. ANNs and the Brain

1.1. The Zoo of Networks

1.2. Modeling Introduction

1.3. ANNs as Brain Models

1.4. Feedforward Networks

1.5. Convolutional Neural Networks

1.6. Recurrent Neural Networks

1.7. Long Short Term Memory Networks

1.8. RNNs and Decision-Making

1.9. Spiking Neural Networks

1.10. SNNs and Bayesian Brain

2. Learning

2.1. Learning

2.2. How Brains Learn

2.3. How ANNs Learn

2.4. Backpropagation and Brains

2.5. Unsupervised Representation Learning

2.6. Autoencoders

2.7. GANs and Consciousness

2.8. Reinforcement Learning 1

2.9. Reinforcement Learning 2

2.10. Deep RL and Decision-Making

2.11. Deep RL and Cognitive Maps

2.12. Deep Meta-Reinforcement Learning

2.13. Innate Knowledge

Extra (from Q&As)

STDP

Neuronal subtypes

Dendrites

Glia, computation, cognition

Sleep

Misc.

B. FROM AWESOME NEURO-AI PAPERS:

Papers

Reviews

Blogs

C. FROM INNOCENTI'S NEURO-AI PAPERS

Outline

Surveys

Deep learning

Reviews & perspectives

Philosophical takes

Vision

Audition

Somatosensation

Motor control

Validation methods

Closed-loop experiments

Model benchmarks

Backprop in the brain?

Artificial & biological neurons

Spiking neural networks

Nature & nurture

Reinforcement learning

Reviews & perspectives

Experiments

The Thousand Brains Theory

Background sources

Neuroscience

AI

D. FROM HEBART LAB LIST:

E. FROM MILLIDGE'S LIST:

Surveys

Classics

Philosophical Analyses

Self-Organisation and Markov Blankets

Information Geometry

Active Inference Outline

Surveys and Tutorials

Discrete State Space Formulation

Continuous Time Formulation

Message Passing and Free Energies

Active Inference for Control Theory/Robotics

Neuroscience and Computational Psychiatry Applications

Deep Active Inference

Acknowledgments