A Method to Present and Analyze Ensembles of Information Sources
Information theory is a powerful tool for analyzing complex systems. In many areas of neuroscience, it is now possible to gather data from large ensembles of neural variables (e.g., data from many neurons, genes, or voxels). The individual variables can be analyzed with information theory to provide...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/22/5/580 |
| _version_ | 1797567461441142784 |
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| author | Nicholas M. Timme David Linsenbardt Christopher C. Lapish |
| author_facet | Nicholas M. Timme David Linsenbardt Christopher C. Lapish |
| author_sort | Nicholas M. Timme |
| collection | DOAJ |
| description | Information theory is a powerful tool for analyzing complex systems. In many areas of neuroscience, it is now possible to gather data from large ensembles of neural variables (e.g., data from many neurons, genes, or voxels). The individual variables can be analyzed with information theory to provide estimates of information shared between variables (forming a network between variables), or between neural variables and other variables (e.g., behavior or sensory stimuli). However, it can be difficult to (1) evaluate if the ensemble is significantly different from what would be expected in a purely noisy system and (2) determine if two ensembles are different. Herein, we introduce relatively simple methods to address these problems by analyzing ensembles of information sources. We demonstrate how an ensemble built of mutual information connections can be compared to null surrogate data to determine if the ensemble is significantly different from noise. Next, we show how two ensembles can be compared using a randomization process to determine if the sources in one contain more information than the other. All code necessary to carry out these analyses and demonstrations are provided. |
| first_indexed | 2024-03-10T19:42:18Z |
| format | Article |
| id | doaj.art-e9639105a4e148fb83a2e35b4ba18676 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T19:42:18Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e9639105a4e148fb83a2e35b4ba186762023-11-20T01:11:20ZengMDPI AGEntropy1099-43002020-05-0122558010.3390/e22050580A Method to Present and Analyze Ensembles of Information SourcesNicholas M. Timme0David Linsenbardt1Christopher C. Lapish2Department of Psychology, Indiana University—Purdue University Indianapolis, Indianapolis, IN 46202, USADepartment of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM 87131, USADepartment of Psychology, Indiana University—Purdue University Indianapolis, Indianapolis, IN 46202, USAInformation theory is a powerful tool for analyzing complex systems. In many areas of neuroscience, it is now possible to gather data from large ensembles of neural variables (e.g., data from many neurons, genes, or voxels). The individual variables can be analyzed with information theory to provide estimates of information shared between variables (forming a network between variables), or between neural variables and other variables (e.g., behavior or sensory stimuli). However, it can be difficult to (1) evaluate if the ensemble is significantly different from what would be expected in a purely noisy system and (2) determine if two ensembles are different. Herein, we introduce relatively simple methods to address these problems by analyzing ensembles of information sources. We demonstrate how an ensemble built of mutual information connections can be compared to null surrogate data to determine if the ensemble is significantly different from noise. Next, we show how two ensembles can be compared using a randomization process to determine if the sources in one contain more information than the other. All code necessary to carry out these analyses and demonstrations are provided.https://www.mdpi.com/1099-4300/22/5/580information theoryinformation ensembleensemble comparisonpopulation codingmutual informationneural ensemble |
| spellingShingle | Nicholas M. Timme David Linsenbardt Christopher C. Lapish A Method to Present and Analyze Ensembles of Information Sources Entropy information theory information ensemble ensemble comparison population coding mutual information neural ensemble |
| title | A Method to Present and Analyze Ensembles of Information Sources |
| title_full | A Method to Present and Analyze Ensembles of Information Sources |
| title_fullStr | A Method to Present and Analyze Ensembles of Information Sources |
| title_full_unstemmed | A Method to Present and Analyze Ensembles of Information Sources |
| title_short | A Method to Present and Analyze Ensembles of Information Sources |
| title_sort | method to present and analyze ensembles of information sources |
| topic | information theory information ensemble ensemble comparison population coding mutual information neural ensemble |
| url | https://www.mdpi.com/1099-4300/22/5/580 |
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