Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhu, Yu Song, Chunfeng Ouyang, Wanli Yu, Shan Huang, Tiejun |
| author_facet | Zhu, Yu Song, Chunfeng Ouyang, Wanli Yu, Shan Huang, Tiejun |
| contents | Individual brains exhibit striking structural and physiological heterogeneity, yet neural circuits can generate remarkably consistent functional properties across individuals, an apparent paradox in neuroscience. While recent studies have observed preserved neural representations in motor cortex through manual alignment across subjects, the zero-shot validation of such preservation and its generalization to more cortices remain unexplored. Here we present PNBA (Probabilistic Neural-Behavioral Representation Alignment), a new framework that leverages probabilistic modeling to address hierarchical variability across trials, sessions, and subjects, with generative constraints preventing representation degeneration. By establishing reliable cross-modal representational alignment, PNBA reveals robust preserved neural representations in monkey primary motor cortex (M1) and dorsal premotor cortex (PMd) through zero-shot validation. We further establish similar representational preservation in mouse primary visual cortex (V1), reflecting a general neural basis. These findings resolve the paradox of neural heterogeneity by establishing zero-shot preserved neural representations across cortices and species, enriching neural coding insights and enabling zero-shot behavior decoding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment Zhu, Yu Song, Chunfeng Ouyang, Wanli Yu, Shan Huang, Tiejun Neurons and Cognition Individual brains exhibit striking structural and physiological heterogeneity, yet neural circuits can generate remarkably consistent functional properties across individuals, an apparent paradox in neuroscience. While recent studies have observed preserved neural representations in motor cortex through manual alignment across subjects, the zero-shot validation of such preservation and its generalization to more cortices remain unexplored. Here we present PNBA (Probabilistic Neural-Behavioral Representation Alignment), a new framework that leverages probabilistic modeling to address hierarchical variability across trials, sessions, and subjects, with generative constraints preventing representation degeneration. By establishing reliable cross-modal representational alignment, PNBA reveals robust preserved neural representations in monkey primary motor cortex (M1) and dorsal premotor cortex (PMd) through zero-shot validation. We further establish similar representational preservation in mouse primary visual cortex (V1), reflecting a general neural basis. These findings resolve the paradox of neural heterogeneity by establishing zero-shot preserved neural representations across cortices and species, enriching neural coding insights and enabling zero-shot behavior decoding. |
| title | Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2505.04331 |