Decomposing stimulus-specific sensory neural information via diffusion models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918165657681920 |
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| author | Laquitaine, Steeve Azeglio, Simone Paris, Carlo Ferrari, Ulisse Chalk, Matthew |
| author_facet | Laquitaine, Steeve Azeglio, Simone Paris, Carlo Ferrari, Ulisse Chalk, Matthew |
| contents | To understand sensory coding, we must ask not only how much information neurons encode, but also what that information is about. This requires decomposing mutual information into contributions from individual stimuli and stimulus features: a fundamentally ill-posed problem with infinitely many possible solutions. We address this by introducing three core axioms, additivity, positivity, and locality that any meaningful stimulus-wise decomposition should satisfy. We then derive a decomposition that meets all three criteria and remains tractable for high-dimensional stimuli. Our decomposition can be efficiently estimated using diffusion models, allowing for scaling up to complex, structured and naturalistic stimuli. Applied to a model of visual neurons, our method quantifies how specific stimuli and features contribute to encoded information. Our approach provides a scalable, interpretable tool for probing representations in both biological and artificial neural systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decomposing stimulus-specific sensory neural information via diffusion models Laquitaine, Steeve Azeglio, Simone Paris, Carlo Ferrari, Ulisse Chalk, Matthew Neurons and Cognition Machine Learning To understand sensory coding, we must ask not only how much information neurons encode, but also what that information is about. This requires decomposing mutual information into contributions from individual stimuli and stimulus features: a fundamentally ill-posed problem with infinitely many possible solutions. We address this by introducing three core axioms, additivity, positivity, and locality that any meaningful stimulus-wise decomposition should satisfy. We then derive a decomposition that meets all three criteria and remains tractable for high-dimensional stimuli. Our decomposition can be efficiently estimated using diffusion models, allowing for scaling up to complex, structured and naturalistic stimuli. Applied to a model of visual neurons, our method quantifies how specific stimuli and features contribute to encoded information. Our approach provides a scalable, interpretable tool for probing representations in both biological and artificial neural systems. |
| title | Decomposing stimulus-specific sensory neural information via diffusion models |
| topic | Neurons and Cognition Machine Learning |
| url | https://arxiv.org/abs/2505.11309 |