Difference between revisions of "Human brain"
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* 2025-04: [https://www.nature.com/articles/s41586-025-08840-3 Functional connectomics reveals general wiring rule in mouse visual cortex] ([https://www.nature.com/articles/d41586-025-01088-x?utm_source=x&utm_medium=social&utm_campaign=nature&linkId=13897098 media summary]) | * 2025-04: [https://www.nature.com/articles/s41586-025-08840-3 Functional connectomics reveals general wiring rule in mouse visual cortex] ([https://www.nature.com/articles/d41586-025-01088-x?utm_source=x&utm_medium=social&utm_campaign=nature&linkId=13897098 media summary]) | ||
* 2025-08: [https://www.nature.com/articles/s41586-025-08985-1 Light-microscopy-based connectomic reconstruction of mammalian brain tissue] ([https://research.google/blog/a-new-light-on-neural-connections/ blog]) | * 2025-08: [https://www.nature.com/articles/s41586-025-08985-1 Light-microscopy-based connectomic reconstruction of mammalian brain tissue] ([https://research.google/blog/a-new-light-on-neural-connections/ blog]) | ||
| + | |||
| + | ===Related=== | ||
| + | * [https://v2.virtualflybrain.org 3D visualization of adult fruit fly brain] | ||
==Brain signal decoding== | ==Brain signal decoding== | ||
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* 2023-10: [https://ai.meta.com/blog/brain-ai-image-decoding-meg-magnetoencephalography/ Toward a real-time decoding of images from brain activity] (MEG) | * 2023-10: [https://ai.meta.com/blog/brain-ai-image-decoding-meg-magnetoencephalography/ Toward a real-time decoding of images from brain activity] (MEG) | ||
* 2024-06: [https://www.biorxiv.org/content/10.1101/2024.06.04.596589v1.full.pdf PAM: Predictive Attention Mechanism for Neural Decoding of Visual Perception] | * 2024-06: [https://www.biorxiv.org/content/10.1101/2024.06.04.596589v1.full.pdf PAM: Predictive Attention Mechanism for Neural Decoding of Visual Perception] | ||
| − | * 2024-07: [https://arxiv.org/abs/2407.07595 Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data] | + | * 2024-07: [https://arxiv.org/abs/2407.07595 Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data] (EEG) |
* 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] | ||
| + | * 2025-01: [https://arxiv.org/abs/2501.15322v2 Scaling laws for decoding images from brain activity] (EEG) | ||
* 2025-02: Meta: [https://ai.meta.com/research/publications/brain-to-text-decoding-a-non-invasive-approach-via-typing/ Brain-to-Text Decoding: A Non-invasive Approach via Typing] | * 2025-02: Meta: [https://ai.meta.com/research/publications/brain-to-text-decoding-a-non-invasive-approach-via-typing/ Brain-to-Text Decoding: A Non-invasive Approach via Typing] | ||
* 2025-02: Meta: [https://ai.meta.com/research/publications/from-thought-to-action-how-a-hierarchy-of-neural-dynamics-supports-language-production/ From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production] | * 2025-02: Meta: [https://ai.meta.com/research/publications/from-thought-to-action-how-a-hierarchy-of-neural-dynamics-supports-language-production/ From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production] | ||
* 2025-03: Google: [https://research.google/blog/deciphering-language-processing-in-the-human-brain-through-llm-representations/ Deciphering language processing in the human brain through LLM representations] | * 2025-03: Google: [https://research.google/blog/deciphering-language-processing-in-the-human-brain-through-llm-representations/ Deciphering language processing in the human brain through LLM representations] | ||
* 2025-03: [https://www.nature.com/articles/s41593-025-01905-6 A streaming brain-to-voice neuroprosthesis to restore naturalistic communication] | * 2025-03: [https://www.nature.com/articles/s41593-025-01905-6 A streaming brain-to-voice neuroprosthesis to restore naturalistic communication] | ||
| + | * 2025-08: [https://arxiv.org/abs/2508.11536 Language models align with brain regions that represent concepts across modalities] | ||
| + | * 2025-09: [https://arxiv.org/abs/2508.18226 Disentangling the Factors of Convergence between Brains and Computer Vision Models] (fMRI and MEG) | ||
=Computational Analysis= | =Computational Analysis= | ||
| + | |||
| + | ==Computational power of human brain== | ||
| + | * 2020-09: Joe Carlsmith: [https://www.openphilanthropy.org/research/how-much-computational-power-does-it-take-to-match-the-human-brain/ How Much Computational Power Does It Take to Match the Human Brain?] | ||
| + | |||
==Comparison to computer== | ==Comparison to computer== | ||
* [https://arxiv.org/abs/2208.12032 How (and Why) to Think that the Brain is Literally a Computer] | * [https://arxiv.org/abs/2208.12032 How (and Why) to Think that the Brain is Literally a Computer] | ||
| Line 52: | Line 62: | ||
=Comparisons= | =Comparisons= | ||
| + | * 2023-08: [https://arxiv.org/abs/2308.08708 Consciousness in Artificial Intelligence: Insights from the Science of Consciousness] | ||
* 2024-05: [https://arxiv.org/abs/2405.02325 Are Biological Systems More Intelligent Than Artificial Intelligence?] | * 2024-05: [https://arxiv.org/abs/2405.02325 Are Biological Systems More Intelligent Than Artificial Intelligence?] | ||
* 2025-03: Google: [https://research.google/blog/deciphering-language-processing-in-the-human-brain-through-llm-representations/ Deciphering language processing in the human brain through LLM representations] | * 2025-03: Google: [https://research.google/blog/deciphering-language-processing-in-the-human-brain-through-llm-representations/ Deciphering language processing in the human brain through LLM representations] | ||
| Line 58: | Line 69: | ||
** 2025-03: [https://www.nature.com/articles/s41562-025-02105-9 A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations] | ** 2025-03: [https://www.nature.com/articles/s41562-025-02105-9 A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations] | ||
* 2025-05: [https://ai.meta.com/research/publications/emergence-of-language-in-the-developing-brain/ Emergence of Language in the Developing Brain] | * 2025-05: [https://ai.meta.com/research/publications/emergence-of-language-in-the-developing-brain/ Emergence of Language in the Developing Brain] | ||
| + | |||
| + | ==Analogies== | ||
| + | * 2025-08: [https://arxiv.org/abs/2508.11536 Language models align with brain regions that represent concepts across modalities] | ||
| + | ===Speed-accuracy trade-off vs. Inference-compute=== | ||
| + | * 2007: [https://psycnet.apa.org/doi/10.1037/0096-3445.136.2.217 Focusing the spotlight: individual differences in visual attention control] | ||
| + | * 2014-07: [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2014.00150/full The speed-accuracy tradeoff: history, physiology, methodology, and behavior] | ||
=Simulate Brain= | =Simulate Brain= | ||
| Line 63: | Line 80: | ||
* 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] | ||
| + | * 2025-08: [https://www.arxiv.org/abs/2507.22229 TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction] | ||
| + | |||
==See Also== | ==See Also== | ||
* [[AI_and_Humans#Simulate_Humans|Simulate Humans (using LLM)]] | * [[AI_and_Humans#Simulate_Humans|Simulate Humans (using LLM)]] | ||
| Line 68: | Line 87: | ||
=Bio-brain Inspirations for AI= | =Bio-brain Inspirations for AI= | ||
* 2025-01: [https://arxiv.org/abs/2501.16396 TopoNets: High Performing Vision and Language Models with Brain-Like Topography] | * 2025-01: [https://arxiv.org/abs/2501.16396 TopoNets: High Performing Vision and Language Models with Brain-Like Topography] | ||
| + | |||
| + | =Theories of Consciousness= | ||
| + | * [https://www.consciousnessatlas.com/ Consciousness Atlas] | ||
=See Also= | =See Also= | ||
* [[AI_and_Humans#Simulate_Humans|LLM Simulate Humans]] | * [[AI_and_Humans#Simulate_Humans|LLM Simulate Humans]] | ||
Latest revision as of 09:40, 12 November 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)
Related
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 (EEG)
- 2024-12: Predicting Human Brain States with Transformer
- 2025-01: Scaling laws for decoding images from brain activity (EEG)
- 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
- 2025-08: Language models align with brain regions that represent concepts across modalities
- 2025-09: Disentangling the Factors of Convergence between Brains and Computer Vision Models (fMRI and MEG)
Computational Analysis
Computational power of human brain
- 2020-09: Joe Carlsmith: How Much Computational Power Does It Take to Match the Human Brain?
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
- 2023-08: Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
- 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
Analogies
Speed-accuracy trade-off vs. Inference-compute
- 2007: Focusing the spotlight: individual differences in visual attention control
- 2014-07: The speed-accuracy tradeoff: history, physiology, methodology, and behavior
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
- 2025-08: TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction
