Linear Readout of Neural Manifolds with Continuous Variables
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908879444508672 |
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| author | Slatton, Will Chou, Chi-Ning Chung, SueYeon |
| author_facet | Slatton, Will Chou, Chi-Ning Chung, SueYeon |
| contents | Brains and artificial neural networks compute with continuous variables such as object position or stimulus orientation. However, the complex variability in neural responses makes it difficult to link internal representational structure to task performance. We develop a statistical-mechanical theory of regression capacity that relates linear decoding efficiency of continuous variables to geometric properties of neural manifolds. Our theory handles complex neural variability and applies to real data, revealing increasing capacity for decoding object position and size along the monkey visual stream. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10956 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Linear Readout of Neural Manifolds with Continuous Variables Slatton, Will Chou, Chi-Ning Chung, SueYeon Neurons and Cognition Disordered Systems and Neural Networks Brains and artificial neural networks compute with continuous variables such as object position or stimulus orientation. However, the complex variability in neural responses makes it difficult to link internal representational structure to task performance. We develop a statistical-mechanical theory of regression capacity that relates linear decoding efficiency of continuous variables to geometric properties of neural manifolds. Our theory handles complex neural variability and applies to real data, revealing increasing capacity for decoding object position and size along the monkey visual stream. |
| title | Linear Readout of Neural Manifolds with Continuous Variables |
| topic | Neurons and Cognition Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2603.10956 |