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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.00824 |
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| _version_ | 1866909235216908288 |
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| author | Andrey, Kuratov |
| author_facet | Andrey, Kuratov |
| contents | This research focuses on an innovative task of extracting equations from incomplete data, moving away from traditional methods used for complete solutions. The study addresses the challenge of extracting equations from data, particularly in the study of brain activity using electrophysiological data, which is often limited by insufficient information. The study provides a brief review of existing open-source equation derivation approaches in the context of modeling brain activity. The section below introduces a novel algorithm that employs incomplete data and prior domain knowledge to recover differential equations. The algorithm's practicality in real-world scenarios is demonstrated through its application on both synthetic and real datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00824 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A data-driven approach to modeling brain activity using differential equations Andrey, Kuratov Neurons and Cognition Artificial Intelligence Machine Learning This research focuses on an innovative task of extracting equations from incomplete data, moving away from traditional methods used for complete solutions. The study addresses the challenge of extracting equations from data, particularly in the study of brain activity using electrophysiological data, which is often limited by insufficient information. The study provides a brief review of existing open-source equation derivation approaches in the context of modeling brain activity. The section below introduces a novel algorithm that employs incomplete data and prior domain knowledge to recover differential equations. The algorithm's practicality in real-world scenarios is demonstrated through its application on both synthetic and real datasets. |
| title | A data-driven approach to modeling brain activity using differential equations |
| topic | Neurons and Cognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.00824 |