Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes
Summary: We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish be...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004224028128 |
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| author | Daniela Egas Santander Christoph Pokorny András Ecker Jānis Lazovskis Matteo Santoro Jason P. Smith Kathryn Hess Ran Levi Michael W. Reimann |
| author_facet | Daniela Egas Santander Christoph Pokorny András Ecker Jānis Lazovskis Matteo Santoro Jason P. Smith Kathryn Hess Ran Levi Michael W. Reimann |
| author_sort | Daniela Egas Santander |
| collection | DOAJ |
| description | Summary: We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity by contrasting its higher-order structure to a random control and confirmed its relevance in several openly available connectomes. Using a biologically detailed cortical model and an electron microscopic dataset, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexity play a supporting role in boosting the reliability of the network as a whole, softening the robustness-efficiency tradeoff. Crucially, we found that both types of subpopulations can and do coexist within a single connectome in biological neural networks, due to the heterogeneity of their connectivity. |
| format | Article |
| id | doaj-art-5aff29ff41314481bd3b87268e7f33c0 |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-5aff29ff41314481bd3b87268e7f33c02025-08-20T02:53:15ZengElsevieriScience2589-00422025-01-0128111158510.1016/j.isci.2024.111585Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codesDaniela Egas Santander0Christoph Pokorny1András Ecker2Jānis Lazovskis3Matteo Santoro4Jason P. Smith5Kathryn Hess6Ran Levi7Michael W. Reimann8Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 6 Geneva, Switzerland; Corresponding authorBlue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 6 Geneva, SwitzerlandBlue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 6 Geneva, SwitzerlandRiga Business School, Riga Technical University, 1010 Riga, LatviaScuola Internazionale Superiore di Studi Avanzati (SISSA), 34136 Trieste, ItalyDepartment of Mathematics, Nottingham Trent University, Nottingham NG1 4FQ, UKUPHESS, BMI, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, SwitzerlandDepartment of Mathematics, University of Aberdeen, Aberdeen AB24 3UE, UKBlue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 6 Geneva, SwitzerlandSummary: We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity by contrasting its higher-order structure to a random control and confirmed its relevance in several openly available connectomes. Using a biologically detailed cortical model and an electron microscopic dataset, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexity play a supporting role in boosting the reliability of the network as a whole, softening the robustness-efficiency tradeoff. Crucially, we found that both types of subpopulations can and do coexist within a single connectome in biological neural networks, due to the heterogeneity of their connectivity.http://www.sciencedirect.com/science/article/pii/S2589004224028128biological sciencescomputer sciencenatural sciences |
| spellingShingle | Daniela Egas Santander Christoph Pokorny András Ecker Jānis Lazovskis Matteo Santoro Jason P. Smith Kathryn Hess Ran Levi Michael W. Reimann Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes iScience biological sciences computer science natural sciences |
| title | Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes |
| title_full | Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes |
| title_fullStr | Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes |
| title_full_unstemmed | Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes |
| title_short | Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes |
| title_sort | heterogeneous and higher order cortical connectivity undergirds efficient robust and reliable neural codes |
| topic | biological sciences computer science natural sciences |
| url | http://www.sciencedirect.com/science/article/pii/S2589004224028128 |
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