Difference between revisions of "Human brain"
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| + | =How Brain Works= | ||
| + | ==Predictive Coding== | ||
| + | * 2005-04: [https://royalsocietypublishing.org/doi/10.1098/rstb.2005.1622?utm_source=chatgpt.com A theory of cortical responses] | ||
| + | * 2014-09: [https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2014.00666/full Visual mismatch negativity: a predictive coding view] | ||
| + | * 2015-01: [https://www.sciencedirect.com/science/article/pii/S089662731401099X Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels] | ||
| + | * 2016-11: [https://www.sciencedirect.com/science/article/pii/S0896627316306997 Mismatch Receptive Fields in Mouse Visual Cortex] | ||
| + | * 2018-03: [https://www.nature.com/articles/s41598-018-21407-9 Frontal cortex function as derived from hierarchical predictive coding] | ||
| + | * 2024-02: [https://www.sciencedirect.com/science/article/pii/S0149763423004426 The empirical status of predictive coding and active inference] | ||
| + | |||
=Understanding= | =Understanding= | ||
* [https://arxiv.org/abs/2501.02950 Key-value memory in the brain] | * [https://arxiv.org/abs/2501.02950 Key-value memory in the brain] | ||
| Line 54: | Line 63: | ||
* 2024-12: [https://www.nature.com/articles/s43588-024-00731-3 Simulation and assimilation of the digital human brain] ([https://arxiv.org/abs/2211.15963 preprint], [https://github.com/DTB-consortium/Digital_twin_brain-open code]) | * 2024-12: [https://www.nature.com/articles/s43588-024-00731-3 Simulation and assimilation of the digital human brain] ([https://arxiv.org/abs/2211.15963 preprint], [https://github.com/DTB-consortium/Digital_twin_brain-open code]) | ||
* 2024-12: [https://arxiv.org/abs/2412.19814 Predicting Human Brain States with Transformer] | * 2024-12: [https://arxiv.org/abs/2412.19814 Predicting Human Brain States with Transformer] | ||
| + | ==See Also== | ||
| + | * [[AI_and_Humans#Simulate_Humans|Simulate Humans (using LLM)]] | ||
=Bio-brain Inspirations for AI= | =Bio-brain Inspirations for AI= | ||
Latest revision as of 11:58, 3 July 2025
Contents
How Brain Works
Predictive Coding
- 2005-04: A theory of cortical responses
- 2014-09: Visual mismatch negativity: a predictive coding view
- 2015-01: Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels
- 2016-11: Mismatch Receptive Fields in Mouse Visual Cortex
- 2018-03: Frontal cortex function as derived from hierarchical predictive coding
- 2024-02: The empirical status of predictive coding and active inference
Understanding
Brain mapping
- 2024-05: A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution (media summary)
- 2024-10: Neuronal wiring diagram of an adult brain (media summary); 140,000 neurons in fruit fly brain
- 2024-12: A roadmap to scale connectomics to entire mammalian brains
- 2025-04: Functional connectomics reveals general wiring rule in mouse visual cortex (media summary)
- 2025-08: Light-microscopy-based connectomic reconstruction of mammalian brain tissue (blog)
Brain signal decoding
- 2022-11: High-resolution image reconstruction with latent diffusion models from human brain activity
- 2023-08: Music can be reconstructed from human auditory cortex activity using nonlinear decoding models (intracranial EEG)
- 2023-09: DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation (external EEG)
- 2023-09: BrainLM: A foundation model for brain activity recordings
- 2023-10: Toward a real-time decoding of images from brain activity (MEG)
- 2024-06: PAM: Predictive Attention Mechanism for Neural Decoding of Visual Perception
- 2024-07: Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data
- 2024-12: Predicting Human Brain States with Transformer
- 2025-02: Meta: Brain-to-Text Decoding: A Non-invasive Approach via Typing
- 2025-02: Meta: From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production
- 2025-03: Google: Deciphering language processing in the human brain through LLM representations
- 2025-03: A streaming brain-to-voice neuroprosthesis to restore naturalistic communication
Computational Analysis
Comparison to computer
- How (and Why) to Think that the Brain is Literally a Computer
- Contextual feature extraction hierarchies converge in large language models and the brain (LLMs are becoming more brain-like as they advance)
Biological vs. artificial neuron
- Single cortical neurons as deep artificial neural networks: Each biological neuron can be simulated using DNN of 5-8 layers
- Mapping Biological Neuron Dynamics into an Interpretable Two-layer Artificial Neural Network
Data processing
- How Much the Eye Tells the Brain
- Representational geometry: integrating cognition, computation, and the brain
- Language is primarily a tool for communication rather than thought
- The Unbearable Slowness of Being: Why do we live at 10 bits/s? (preprint)
Extract manifold/geometry
Comparisons
- 2024-05: Are Biological Systems More Intelligent Than Artificial Intelligence?
- 2025-03: Google: Deciphering language processing in the human brain through LLM representations
- 2022-03: Shared computational principles for language processing in humans and deep language models
- 2024-03: Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns
- 2025-03: A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations
- 2025-05: Emergence of Language in the Developing Brain
Simulate Brain
- 2023-09: The Digital Twin Brain: A Bridge between Biological and Artificial Intelligence
- 2024-12: Simulation and assimilation of the digital human brain (preprint, code)
- 2024-12: Predicting Human Brain States with Transformer
