菜单
NEURRATOR - Semantic narration of vision at single-cell resolution. Decodes spiking activity into natural-language descriptions using CLIP embeddings and multimodal language models.
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基于 SOC 职业分类
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| name | neurrate-neural-semantic-narration |
| description | NEURRATOR - Semantic narration of vision at single-cell resolution. Decodes spiking activity into natural-language descriptions using CLIP embeddings and multimodal language models. |
| version | 1 |
| authors | ["Arnau Marin-Llobet","Richard Hakim","Sara Matias","Venkatesh N. Murthy","Na Li","Demba Ba"] |
| arxiv_id | 2606.18667 |
| submission_date | 2026-06-17T00:00:00.000Z |
| subjects | ["q-bio.NC","q-bio.QM"] |
| keywords | ["neural decoding","semantic narration","CLIP","spiking activity","single-neuron","multimodal LLM","vision","natural language","Neuropixels","mouse visual cortex"] |
| activation_words | ["neurrate","neural semantic narration","single-cell semantic decoding","CLIP neural encoding","natural language neural decoding"] |
NEURRATOR is a groundbreaking framework that decodes spiking activity into free-form natural-language narration of viewed scenes at single-neuron resolution. This represents a paradigm shift from traditional parameterization approaches to semantic understanding of neural encoding.
Spike-to-CLIP Encoder
Frozen CLIP Model
Multimodal Language Model
Sparse Autoencoder (SAE)
Multi-scale decoding:
Quantitative analysis:
"Neurrating":
Decoding fidelity scaling:
Cell-type contributions:
Visual representation insights:
NEURRATOR transforms neural decoding from parameter fitting to semantic narration, providing a fundamentally new way to understand what individual neurons encode. The framework bridges spike trains and natural language, enabling biological insights at unprecedented resolution.
