ArticleNeuron2021
Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments.
Article in Neuron, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Orbitofrontal-sensory cortical interactions in learning and adaptive decision-making.Trends in cognitive sciences · 2026Review
- Data-derived agents reveal dynamical reservoirs in mouse cortex for adaptive behavior.bioRxiv : the preprint server for biology · 2026Article
- Text-to-music generation models capture musical semantic representations in the human brain.Nature communications · 2025Article
- Tabula rasa agents display emergent in-group behavior.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Overlapping Cortical Substrate of Biomechanical Control and Subjective Agency.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- A unified neural representation model for spatial and conceptual computations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Advances in deep reinforcement learning enable better predictions of human behavior in time-continuous tasks.PloS one · 2025Article
- Understanding human amygdala function with artificial neural networks.bioRxiv : the preprint server for biology · 2024Article
- Using games to understand the mind.Nature human behaviour · 2024Review
- Using unsupervised capsule neural network reveal spatial representations in the human brain.Human brain mapping · 2024Article
- Extracting and visualizing hidden activations and computational graphs of PyTorch models with TorchLens.Scientific reports · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- Article
- Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans.Human brain mapping · 2023Article
- CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning.Frontiers in neuroinformatics · 2023Article
- Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data.Computational psychiatry (Cambridge, Mass.) · 2023Article
- Emergent behaviour and neural dynamics in artificial agents tracking odour plumes.Nature machine intelligence · 2023Article
- Article
- Representation learning in the artificial and biological neural networks underlying sensorimotor integration.Science advances · 2022Article
- Same Cause; Different Effects in the Brain.Proceedings of machine learning research · 2022Article
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4 authors.
Funding
Abstract
Humans possess an exceptional aptitude to efficiently make decisions from high-dimensional sensory observations. However, it is unknown how the brain compactly represents the current state of the environment to guide this process. The deep Q-network (DQN) achieves this by capturing highly nonlinear mappings from multivariate inputs to the values of potential actions. We deployed DQN as a model of brain activity and behavior in participants playing three Atari video games during fMRI. Hidden layers of DQN exhibited a striking resemblance to voxel activity in a distributed sensorimotor network, extending throughout the dorsal visual pathway into posterior parietal cortex. Neural state-space representations emerged from nonlinear transformations of the pixel space bridging perception to action and reward. These transformations reshape axes to reflect relevant high-level features and strip away information about task-irrelevant sensory features. Our findings shed light on the neural encoding of task representations for decision-making in real-world situations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.