- See Also
- Gwern
-
Links
- “Centaur: a Foundation Model of Human Cognition”, Binz et al 2024
- “Instruction-Tuning Aligns LLMs to the Human Brain”, Aw et al 2023
- “Improving Neural Network Representations Using Human Similarity Judgments”, Muttenthaler et al 2023
- “Performance Reserves in Brain-Imaging-Based Phenotype Prediction”, Schulz et al 2022
- “Brains and Algorithms Partially Converge in Natural Language Processing”, Caucheteux & King 2022
- “Generative Models of Brain Dynamics—A Review”, Panahi et al 2021
- “Toward Conceptual Networks in Brain: Decoding Imagined Words from Word Reading”, He et al 2021
- “In Vitro Neurons Learn and Exhibit Sentience When Embodied in a Simulated Game-World”, Kagan et al 2021
- “Long-Range and Hierarchical Language Predictions in Brains and Algorithms”, Caucheteux et al 2021
- “Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks”, Harrington & Deza 2021
- “Fine-Tuning of Deep Language Models As a Computational Framework of Modeling Listeners’ Perspective during Language Comprehension”, Tikochinski et al 2021
- “Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks”, Ericsson et al 2021
- “Compositional Restricted Boltzmann Machines Unveil the Brain-Wide Organization of Neural Assemblies”, Plas et al 2021
- “Unsupervised Deep Learning Identifies Semantic Disentanglement in Single Inferotemporal Face Patch Neurons”, Higgins et al 2021
- “Your Head Is There to Move You Around: Goal-Driven Models of the Primate Dorsal Pathway”, Mineault et al 2021
- “Deep Learning Models of Cognitive Processes Constrained by Human Brain Connectomes”, Zhang et al 2021
- “Monkey Plays Pac-Man With Compositional Strategies and Hierarchical Decision-Making”, Yang et al 2021
- “Text2Brain: Synthesis of Brain Activation Maps from Free-Form Text Query”, Ngo et al 2021
- “Capturing the Objects of Vision With Neural Networks”, Peters & Kriegeskorte 2021
- “Fitting Summary Statistics of Neural Data With a Differentiable Spiking Network Simulator”, Bellec et al 2021
- “The Functional Specialization of Visual Cortex Emerges from Training Parallel Pathways With Self-Supervised Predictive Learning”, Bakhtiari et al 2021
- “Embracing New Techniques in Deep Learning for Estimating Image Memorability”, Needell & Bainbridge 2021
- “A Massive 7T FMRI Dataset to Bridge Cognitive and Computational Neuroscience”, Allen et al 2021
- “Brain-Computer Interface for Generating Personally Attractive Images”, Spape et al 2021
- “BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn from Massive Amounts of EEG Data”, Kostas et al 2021
- “Selective Eye-Gaze Augmentation To Enhance Imitation Learning In Atari Games”, Thammineni et al 2020
- “MoGaze: A Dataset of Full-Body Motions That Includes Workspace Geometry and Eye-Gaze”, Kratzer et al 2020
- “The Hearing Aid Dilemma: Amplification, Compression, and Distortion of the Neural Code”, Armstrong et al 2020
- “Self-Supervised Natural Image Reconstruction and Rich Semantic Classification from Brain Activity”, Gaziv et al 2020
- “Self-Supervised Learning through the Eyes of a Child”, Orhan et al 2020
- “Deep Neural Network Models of Sound Localization Reveal How Perception Is Adapted to Real-World Environments”, Francl & McDermott 2020
- “What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking”, Kröger et al 2020
- “Inducing Brain-Relevant Bias in Natural Language Processing Models”, Schwartz et al 2019
- “Low-Dimensional Embodied Semantics for Music and Language”, Raposo et al 2019
- “Improved Object Recognition Using Neural Networks Trained to Mimic the Brain’s Statistical Properties”, Federer et al 2019
- “Neural System Identification With Neural Information Flow”, Seeliger et al 2019
- “Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset”, Zhang et al 2019
- “Neural Population Control via Deep Image Synthesis”, Bashivan et al 2019
- “Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features”, Palazzo et al 2018
- “Humans Can Decipher Adversarial Images”, Zhou & Firestone 2018
- “A Neurobiological Evaluation Metric for Neural Network Model Search”, Blanchard et al 2018
- “Visceral Machines: Risk-Aversion in Reinforcement Learning With Intrinsic Physiological Rewards”, McDuff & Kapoor 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”, Rajalingham et al 2018
- “Towards Deep Modeling of Music Semantics Using EEG Regularizers”, Raposo et al 2017
- “Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction”, Lathuilière et al 2017
- “Predicting Driver Attention in Critical Situations”, Xia et al 2017
- “The Signature of Robot Action Success in EEG Signals of a Human Observer: Decoding and Visualization Using Deep Convolutional Neural Networks”, Behncke et al 2017
- “Towards Personalized Human AI Interaction—Adapting the Behavior of AI Agents Using Neural Signatures of Subjective Interest”, Shih et al 2017
- “Brain Responses During Robot-Error Observation”, Welke et al 2017
- “Using Human Brain Activity to Guide Machine Learning”, Fong et al 2017
- “Mapping Between FMRI Responses to Movies and Their Natural Language Annotations”, Vodrahalli et al 2016
- “Deep Learning Human Mind for Automated Visual Classification”, Spampinato et al 2016
- “Towards an Integration of Deep Learning and Neuroscience”, Marblestone et al 2016
- “Improving Sentence Compression by Learning to Predict Gaze”, Klerke et al 2016
- “Neural Encoding and Decoding With Deep Learning for Dynamic Natural Vision Cerebral Cortex”
- “Exploring Semantic Representation in Brain Activity Using Word Embeddings”
- “Sequence Classification With Human Attention”
- “Psych-101 Dataset [For Centaur]”
- “Deep Reinforcement Learning from Human Preferences”
- “Paths To High-Level Machine Intelligence”
- “Randal Koene on Brain Understanding Before Whole Brain Emulation”
- “The Science of Mind Reading”
- “The Man Who Controls Computers With His Mind”
- “Tracking Readers’ Eye Movements Can Help Computers Learn”
- “Monkeys Play Pac-Man”
- “AI and Neuroscience”
- Sort By Magic
- Wikipedia
- Miscellaneous
- Bibliography
See Also
Gwern
“Modular Brain AUNNs for Uploads”, Gwern 2023
“WBE and DRL: a Middle Way of Imitation Learning from the Human Brain”, Gwern 2018
WBE and DRL: a Middle Way of imitation learning from the human brain
Links
“Centaur: a Foundation Model of Human Cognition”, Binz et al 2024
“Instruction-Tuning Aligns LLMs to the Human Brain”, Aw et al 2023
“Improving Neural Network Representations Using Human Similarity Judgments”, Muttenthaler et al 2023
Improving neural network representations using human similarity judgments
“Performance Reserves in Brain-Imaging-Based Phenotype Prediction”, Schulz et al 2022
Performance reserves in brain-imaging-based phenotype prediction
“Brains and Algorithms Partially Converge in Natural Language Processing”, Caucheteux & King 2022
Brains and algorithms partially converge in natural language processing
“Generative Models of Brain Dynamics—A Review”, Panahi et al 2021
“Toward Conceptual Networks in Brain: Decoding Imagined Words from Word Reading”, He et al 2021
Toward Conceptual Networks in Brain: Decoding Imagined Words from Word Reading
“In Vitro Neurons Learn and Exhibit Sentience When Embodied in a Simulated Game-World”, Kagan et al 2021
In vitro neurons learn and exhibit sentience when embodied in a simulated game-world
“Long-Range and Hierarchical Language Predictions in Brains and Algorithms”, Caucheteux et al 2021
Long-range and hierarchical language predictions in brains and algorithms
“Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks”, Harrington & Deza 2021
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks
“Fine-Tuning of Deep Language Models As a Computational Framework of Modeling Listeners’ Perspective during Language Comprehension”, Tikochinski et al 2021
“Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks”, Ericsson et al 2021
Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks
“Compositional Restricted Boltzmann Machines Unveil the Brain-Wide Organization of Neural Assemblies”, Plas et al 2021
Compositional Restricted Boltzmann Machines Unveil the Brain-Wide Organization of Neural Assemblies
“Unsupervised Deep Learning Identifies Semantic Disentanglement in Single Inferotemporal Face Patch Neurons”, Higgins et al 2021
“Your Head Is There to Move You Around: Goal-Driven Models of the Primate Dorsal Pathway”, Mineault et al 2021
Your head is there to move you around: Goal-driven models of the primate dorsal pathway
“Deep Learning Models of Cognitive Processes Constrained by Human Brain Connectomes”, Zhang et al 2021
Deep learning models of cognitive processes constrained by human brain connectomes
“Monkey Plays Pac-Man With Compositional Strategies and Hierarchical Decision-Making”, Yang et al 2021
Monkey Plays Pac-Man with Compositional Strategies and Hierarchical Decision-making
“Text2Brain: Synthesis of Brain Activation Maps from Free-Form Text Query”, Ngo et al 2021
Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query
“Capturing the Objects of Vision With Neural Networks”, Peters & Kriegeskorte 2021
“Fitting Summary Statistics of Neural Data With a Differentiable Spiking Network Simulator”, Bellec et al 2021
Fitting summary statistics of neural data with a differentiable spiking network simulator
“The Functional Specialization of Visual Cortex Emerges from Training Parallel Pathways With Self-Supervised Predictive Learning”, Bakhtiari et al 2021
“Embracing New Techniques in Deep Learning for Estimating Image Memorability”, Needell & Bainbridge 2021
Embracing New Techniques in Deep Learning for Estimating Image Memorability
“A Massive 7T FMRI Dataset to Bridge Cognitive and Computational Neuroscience”, Allen et al 2021
A massive 7T fMRI dataset to bridge cognitive and computational neuroscience
“Brain-Computer Interface for Generating Personally Attractive Images”, Spape et al 2021
Brain-computer interface for generating personally attractive images
“BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn from Massive Amounts of EEG Data”, Kostas et al 2021
“Selective Eye-Gaze Augmentation To Enhance Imitation Learning In Atari Games”, Thammineni et al 2020
Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games
“MoGaze: A Dataset of Full-Body Motions That Includes Workspace Geometry and Eye-Gaze”, Kratzer et al 2020
MoGaze: A Dataset of Full-Body Motions that Includes Workspace Geometry and Eye-Gaze
“The Hearing Aid Dilemma: Amplification, Compression, and Distortion of the Neural Code”, Armstrong et al 2020
The hearing aid dilemma: amplification, compression, and distortion of the neural code
“Self-Supervised Natural Image Reconstruction and Rich Semantic Classification from Brain Activity”, Gaziv et al 2020
Self-Supervised Natural Image Reconstruction and Rich Semantic Classification from Brain Activity
“Self-Supervised Learning through the Eyes of a Child”, Orhan et al 2020
“Deep Neural Network Models of Sound Localization Reveal How Perception Is Adapted to Real-World Environments”, Francl & McDermott 2020
“What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking”, Kröger et al 2020
What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking
“Inducing Brain-Relevant Bias in Natural Language Processing Models”, Schwartz et al 2019
Inducing brain-relevant bias in natural language processing models
“Low-Dimensional Embodied Semantics for Music and Language”, Raposo et al 2019
“Improved Object Recognition Using Neural Networks Trained to Mimic the Brain’s Statistical Properties”, Federer et al 2019
“Neural System Identification With Neural Information Flow”, Seeliger et al 2019
“Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset”, Zhang et al 2019
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
“Neural Population Control via Deep Image Synthesis”, Bashivan et al 2019
“Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features”, Palazzo et al 2018
Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features
“Humans Can Decipher Adversarial Images”, Zhou & Firestone 2018
“A Neurobiological Evaluation Metric for Neural Network Model Search”, Blanchard et al 2018
A Neurobiological Evaluation Metric for Neural Network Model Search
“Visceral Machines: Risk-Aversion in Reinforcement Learning With Intrinsic Physiological Rewards”, McDuff & Kapoor 2018
Visceral Machines: Risk-Aversion in Reinforcement Learning with Intrinsic Physiological Rewards
“Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-Of-The-Art Deep Artificial Neural Networks”, Rajalingham et al 2018
“Towards Deep Modeling of Music Semantics Using EEG Regularizers”, Raposo et al 2017
Towards Deep Modeling of Music Semantics using EEG Regularizers
“Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction”, Lathuilière et al 2017
Neural Network Based Reinforcement Learning for Audio-Visual Gaze Control in Human-Robot Interaction
“Predicting Driver Attention in Critical Situations”, Xia et al 2017
“The Signature of Robot Action Success in EEG Signals of a Human Observer: Decoding and Visualization Using Deep Convolutional Neural Networks”, Behncke et al 2017
“Towards Personalized Human AI Interaction—Adapting the Behavior of AI Agents Using Neural Signatures of Subjective Interest”, Shih et al 2017
“Brain Responses During Robot-Error Observation”, Welke et al 2017
“Using Human Brain Activity to Guide Machine Learning”, Fong et al 2017
“Mapping Between FMRI Responses to Movies and Their Natural Language Annotations”, Vodrahalli et al 2016
Mapping Between fMRI Responses to Movies and their Natural Language Annotations
“Deep Learning Human Mind for Automated Visual Classification”, Spampinato et al 2016
Deep Learning Human Mind for Automated Visual Classification
“Towards an Integration of Deep Learning and Neuroscience”, Marblestone et al 2016
“Improving Sentence Compression by Learning to Predict Gaze”, Klerke et al 2016
“Neural Encoding and Decoding With Deep Learning for Dynamic Natural Vision Cerebral Cortex”
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Cerebral Cortex
“Exploring Semantic Representation in Brain Activity Using Word Embeddings”
Exploring Semantic Representation in Brain Activity Using Word Embeddings:
View External Link:
“Sequence Classification With Human Attention”
Sequence Classification with Human Attention:
View External Link:
“Psych-101 Dataset [For Centaur]”
“Deep Reinforcement Learning from Human Preferences”
“Paths To High-Level Machine Intelligence”
“Randal Koene on Brain Understanding Before Whole Brain Emulation”
Randal Koene on brain understanding before whole brain emulation:
“The Science of Mind Reading”
“The Man Who Controls Computers With His Mind”
“Tracking Readers’ Eye Movements Can Help Computers Learn”
“Monkeys Play Pac-Man”
“AI and Neuroscience”
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
neural-embodiment
goal-driven
brain-computation
Wikipedia
Miscellaneous
-
https://www.cs.cmu.edu/~afyshe/papers/acl2014/jnnse_acl2014.pdf: -
https://www.fhi.ox.ac.uk/brain-emulation-roadmap-report.pdf: -
https://www.quantamagazine.org/deep-neural-networks-help-to-explain-living-brains-20201028/: -
https://www.wired.com/story/the-long-search-for-a-computer-that-speaks-your-mind/:
Bibliography
-
https://www.nature.com/articles/s42003-022-03036-1: “Brains and Algorithms Partially Converge in Natural Language Processing”,