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Unveiling Multi-level and Multi-modal Semantic Representations in the Human Brain using Large Language Models

https://doi.org/10.1101/2024.02.06.579077

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

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Encoding and decoding in fMRI

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Feature-space selection with banded ridge regression

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Enhanced Intersubject Correlations during Movie Viewing Correlate with Successful Episodic Encoding

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A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain

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Natural speech reveals the semantic maps that tile human cerebral cortex

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Generic decoding of seen and imagined objects using hierarchical visual features

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Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations

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A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence

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Shared computational principles for language processing in humans and deep language models

inferredcites →https://doi.org/10.1038/s41597-021-01033-3openalex-snapshot

The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

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Deep language algorithms predict semantic comprehension from brain activity

inferredcites →https://doi.org/10.1038/s42256-023-00753-yopenalex-snapshot

Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset

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The neural architecture of language: Integrative modeling converges on predictive processing

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Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

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Driving and suppressing the human language network using large language models

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Incorporating Context into Language Encoding Models for fMRI

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High-resolution image reconstruction with latent diffusion models from human brain activity

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Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding

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Improved Baselines with Visual Instruction Tuning

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Cortical response to naturalistic stimuli is largely predictable with deep neural networks

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Endogenous variation in ventromedial prefrontal cortex state dynamics during naturalistic viewing reflects affective experience

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Predicting speech from a cortical hierarchy of event-based time scales

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Simultaneously Uncovering the Patterns of Brain Regions Involved in Different Story Reading Subprocesses

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Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream

inferredcites →https://doi.org/10.1609/aaai.v37i9.26263openalex-snapshot

BridgeTower: Building Bridges between Encoders in Vision-Language Representation Learning

inferredcites →https://doi.org/10.18653/v1/p19-1507openalex-snapshot

Relating Simple Sentence Representations in Deep Neural Networks and the Brain

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AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

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Analysing Off-The-Shelf Options for Question Answering with Portuguese FAQs

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Efficient Estimation of Word Representations in Vector Space

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GIT: A Generative Image-to-text Transformer for Vision and Language

inferredcites →https://doi.org/10.48550/arxiv.2212.10898openalex-snapshot

Training language models to summarize narratives improves brain alignment

inferredcites →https://doi.org/10.48550/arxiv.2305.11863openalex-snapshot

Scaling laws for language encoding models in fMRI

inferredcites →https://doi.org/10.48550/arxiv.2305.13516openalex-snapshot

Scaling Speech Technology to 1,000+ Languages

inferredcites →https://doi.org/10.48550/arxiv.2307.09288openalex-snapshot

Behavioral Safety and Context Retention of Large Language Models in a Longitudinal ICU Simulation under Offline Conditions

inferredcites →https://doi.org/10.48550/arxiv.2310.04420openalex-snapshot

BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity

inferredcites →https://doi.org/10.7554/elife.32962openalex-snapshot

Distinct contributions of functional and deep neural network features to representational similarity of scenes in human brain and behavior

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.48550/arxiv.2307.10246openalex-snapshot

Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)

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