Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models
During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-07-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2018.00044/full |
| _version_ | 1823944425202515968 |
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| author | Andrei Maksimov Markus Diesmann Markus Diesmann Markus Diesmann Sacha J. van Albada |
| author_facet | Andrei Maksimov Markus Diesmann Markus Diesmann Markus Diesmann Sacha J. van Albada |
| author_sort | Andrei Maksimov |
| collection | DOAJ |
| description | During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done so without a systematic quantitative comparison with experimental data. Our study provides quantitative criteria for this purpose, by analyzing in-vitro and in-vivo neuronal activity and characterizing the dynamics on the neuronal and population levels. The criteria are defined with a tolerance that allows for differences between experiments, yet are sufficient to capture commonalities between persistently depolarized cortical network states and to help validate computational models of cortex. As test cases for the derived set of criteria, we analyze three widely used models of cortical circuits and find that each model possesses some of the experimentally observed features, but none satisfies all criteria simultaneously, showing that the criteria are able to identify weak spots in computational models. The criteria described here form a starting point for the systematic validation of cortical neuronal network models, which will help improve the reliability of future models, and render them better building blocks for larger models of the brain. |
| first_indexed | 2024-12-17T08:10:23Z |
| format | Article |
| id | doaj.art-486d779ca5a243c7aff5d060bf3a315d |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-17T08:10:23Z |
| publishDate | 2018-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-486d779ca5a243c7aff5d060bf3a315d2022-12-21T21:57:14ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882018-07-011210.3389/fncom.2018.00044322441Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational ModelsAndrei Maksimov0Markus Diesmann1Markus Diesmann2Markus Diesmann3Sacha J. van Albada4Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I (INM-10), Jülich Research Centre, Jülich, GermanyInstitute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I (INM-10), Jülich Research Centre, Jülich, GermanyDepartment of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, GermanyDepartment of Physics, Faculty 1, RWTH Aachen University, Aachen, GermanyInstitute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I (INM-10), Jülich Research Centre, Jülich, GermanyDuring ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done so without a systematic quantitative comparison with experimental data. Our study provides quantitative criteria for this purpose, by analyzing in-vitro and in-vivo neuronal activity and characterizing the dynamics on the neuronal and population levels. The criteria are defined with a tolerance that allows for differences between experiments, yet are sufficient to capture commonalities between persistently depolarized cortical network states and to help validate computational models of cortex. As test cases for the derived set of criteria, we analyze three widely used models of cortical circuits and find that each model possesses some of the experimentally observed features, but none satisfies all criteria simultaneously, showing that the criteria are able to identify weak spots in computational models. The criteria described here form a starting point for the systematic validation of cortical neuronal network models, which will help improve the reliability of future models, and render them better building blocks for larger models of the brain.https://www.frontiersin.org/article/10.3389/fncom.2018.00044/fullspiking neural networksup/down statesvalidationbenchmarkingcomputational modelsasynchronous irregular activity |
| spellingShingle | Andrei Maksimov Markus Diesmann Markus Diesmann Markus Diesmann Sacha J. van Albada Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models Frontiers in Computational Neuroscience spiking neural networks up/down states validation benchmarking computational models asynchronous irregular activity |
| title | Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models |
| title_full | Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models |
| title_fullStr | Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models |
| title_full_unstemmed | Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models |
| title_short | Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models |
| title_sort | criteria on balance stability and excitability in cortical networks for constraining computational models |
| topic | spiking neural networks up/down states validation benchmarking computational models asynchronous irregular activity |
| url | https://www.frontiersin.org/article/10.3389/fncom.2018.00044/full |
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