Approaches to Parameter Estimation from Model Neurons and Biological Neurons
Model optimization in neuroscience has focused on inferring intracellular parameters from time series observations of the membrane voltage and calcium concentrations. These parameters constitute the fingerprints of ion channel subtypes and may identify ion channel mutations from observed changes in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/5/168 |
| _version_ | 1797502159414099968 |
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| author | Alain Nogaret |
| author_facet | Alain Nogaret |
| author_sort | Alain Nogaret |
| collection | DOAJ |
| description | Model optimization in neuroscience has focused on inferring intracellular parameters from time series observations of the membrane voltage and calcium concentrations. These parameters constitute the fingerprints of ion channel subtypes and may identify ion channel mutations from observed changes in electrical activity. A central question in neuroscience is whether computational methods may obtain ion channel parameters with sufficient consistency and accuracy to provide new information on the underlying biology. Finding single-valued solutions in particular, remains an outstanding theoretical challenge. This note reviews recent progress in the field. It first covers well-posed problems and describes the conditions that the model and data need to meet to warrant the recovery of all the original parameters—even in the presence of noise. The main challenge is model error, which reflects our lack of knowledge of exact equations. We report on strategies that have been partially successful at inferring the parameters of rodent and songbird neurons, when model error is sufficiently small for accurate predictions to be made irrespective of stimulation. |
| first_indexed | 2024-03-10T03:28:56Z |
| format | Article |
| id | doaj.art-10e4ecef0a4e42158640e491ca3be70a |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T03:28:56Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-10e4ecef0a4e42158640e491ca3be70a2023-11-23T09:45:37ZengMDPI AGAlgorithms1999-48932022-05-0115516810.3390/a15050168Approaches to Parameter Estimation from Model Neurons and Biological NeuronsAlain Nogaret0Department of Physics, University of Bath, Claverton Down, Bath BA2 7AY, UKModel optimization in neuroscience has focused on inferring intracellular parameters from time series observations of the membrane voltage and calcium concentrations. These parameters constitute the fingerprints of ion channel subtypes and may identify ion channel mutations from observed changes in electrical activity. A central question in neuroscience is whether computational methods may obtain ion channel parameters with sufficient consistency and accuracy to provide new information on the underlying biology. Finding single-valued solutions in particular, remains an outstanding theoretical challenge. This note reviews recent progress in the field. It first covers well-posed problems and describes the conditions that the model and data need to meet to warrant the recovery of all the original parameters—even in the presence of noise. The main challenge is model error, which reflects our lack of knowledge of exact equations. We report on strategies that have been partially successful at inferring the parameters of rodent and songbird neurons, when model error is sufficiently small for accurate predictions to be made irrespective of stimulation.https://www.mdpi.com/1999-4893/15/5/168data assimilationparameter estimationnonlinear optimizationion channels |
| spellingShingle | Alain Nogaret Approaches to Parameter Estimation from Model Neurons and Biological Neurons Algorithms data assimilation parameter estimation nonlinear optimization ion channels |
| title | Approaches to Parameter Estimation from Model Neurons and Biological Neurons |
| title_full | Approaches to Parameter Estimation from Model Neurons and Biological Neurons |
| title_fullStr | Approaches to Parameter Estimation from Model Neurons and Biological Neurons |
| title_full_unstemmed | Approaches to Parameter Estimation from Model Neurons and Biological Neurons |
| title_short | Approaches to Parameter Estimation from Model Neurons and Biological Neurons |
| title_sort | approaches to parameter estimation from model neurons and biological neurons |
| topic | data assimilation parameter estimation nonlinear optimization ion channels |
| url | https://www.mdpi.com/1999-4893/15/5/168 |
| work_keys_str_mv | AT alainnogaret approachestoparameterestimationfrommodelneuronsandbiologicalneurons |