Message-Relevant Dimension Reduction of Neural Populations
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911941022187520 |
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| author | Merkley, Amanda Nam, Alice Y. Hong, Y. Kate Grover, Pulkit |
| author_facet | Merkley, Amanda Nam, Alice Y. Hong, Y. Kate Grover, Pulkit |
| contents | Quantifying relevant interactions between neural populations is a prominent question in the analysis of high-dimensional neural recordings. However, existing dimension reduction methods often discuss communication in the absence of a formal framework, while frameworks proposed to address this gap are impractical in data analysis. This work bridges the formal framework of M-Information Flow with practical analysis of real neural data. To this end, we propose Iterative Regression, a message-dependent linear dimension reduction technique that iteratively finds an orthonormal basis such that each basis vector maximizes correlation between the projected data and the message. We then define 'M-forwarding' to formally capture the notion of a message being forwarded from one neural population to another. We apply our methodology to recordings we collected from two neural populations in a simplified model of whisker-based sensory detection in mice, and show that the low-dimensional M-forwarding structure we infer supports biological evidence of a similar structure between the two original, high-dimensional populations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02450 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Message-Relevant Dimension Reduction of Neural Populations Merkley, Amanda Nam, Alice Y. Hong, Y. Kate Grover, Pulkit Quantitative Methods Information Theory Neurons and Cognition Quantifying relevant interactions between neural populations is a prominent question in the analysis of high-dimensional neural recordings. However, existing dimension reduction methods often discuss communication in the absence of a formal framework, while frameworks proposed to address this gap are impractical in data analysis. This work bridges the formal framework of M-Information Flow with practical analysis of real neural data. To this end, we propose Iterative Regression, a message-dependent linear dimension reduction technique that iteratively finds an orthonormal basis such that each basis vector maximizes correlation between the projected data and the message. We then define 'M-forwarding' to formally capture the notion of a message being forwarded from one neural population to another. We apply our methodology to recordings we collected from two neural populations in a simplified model of whisker-based sensory detection in mice, and show that the low-dimensional M-forwarding structure we infer supports biological evidence of a similar structure between the two original, high-dimensional populations. |
| title | Message-Relevant Dimension Reduction of Neural Populations |
| topic | Quantitative Methods Information Theory Neurons and Cognition |
| url | https://arxiv.org/abs/2407.02450 |