Estimation of neuron parameters from imperfect observations.
The estimation of parameters controlling the electrical properties of biological neurons is essential to determine their complement of ion channels and to understand the function of biological circuits. By synchronizing conductance models to time series observations of the membrane voltage, one may...
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| Language: | English |
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Public Library of Science (PLoS)
2020-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008053&type=printable |
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| _version_ | 1850043457069907968 |
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| author | Joseph D Taylor Samuel Winnall Alain Nogaret |
| author_facet | Joseph D Taylor Samuel Winnall Alain Nogaret |
| author_sort | Joseph D Taylor |
| collection | DOAJ |
| description | The estimation of parameters controlling the electrical properties of biological neurons is essential to determine their complement of ion channels and to understand the function of biological circuits. By synchronizing conductance models to time series observations of the membrane voltage, one may construct models capable of predicting neuronal dynamics. However, identifying the actual set of parameters of biological ion channels remains a formidable theoretical challenge. Here, we present a regularization method that improves convergence towards this optimal solution when data are noisy and the model is unknown. Our method relies on the existence of an offset in parameter space arising from the interplay between model nonlinearity and experimental error. By tuning this offset, we induce saddle-node bifurcations from sub-optimal to optimal solutions. This regularization method increases the probability of finding the optimal set of parameters from 67% to 94.3%. We also reduce parameter correlations by implementing adaptive sampling and stimulation protocols compatible with parameter identifiability requirements. Our results show that the optimal model parameters may be inferred from imperfect observations provided the conditions of observability and identifiability are fulfilled. |
| format | Article |
| id | doaj-art-6ca5f02766d04a6396faa1a36e59f85e |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2020-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-6ca5f02766d04a6396faa1a36e59f85e2025-08-20T02:55:13ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-07-01167e100805310.1371/journal.pcbi.1008053Estimation of neuron parameters from imperfect observations.Joseph D TaylorSamuel WinnallAlain NogaretThe estimation of parameters controlling the electrical properties of biological neurons is essential to determine their complement of ion channels and to understand the function of biological circuits. By synchronizing conductance models to time series observations of the membrane voltage, one may construct models capable of predicting neuronal dynamics. However, identifying the actual set of parameters of biological ion channels remains a formidable theoretical challenge. Here, we present a regularization method that improves convergence towards this optimal solution when data are noisy and the model is unknown. Our method relies on the existence of an offset in parameter space arising from the interplay between model nonlinearity and experimental error. By tuning this offset, we induce saddle-node bifurcations from sub-optimal to optimal solutions. This regularization method increases the probability of finding the optimal set of parameters from 67% to 94.3%. We also reduce parameter correlations by implementing adaptive sampling and stimulation protocols compatible with parameter identifiability requirements. Our results show that the optimal model parameters may be inferred from imperfect observations provided the conditions of observability and identifiability are fulfilled.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008053&type=printable |
| spellingShingle | Joseph D Taylor Samuel Winnall Alain Nogaret Estimation of neuron parameters from imperfect observations. PLoS Computational Biology |
| title | Estimation of neuron parameters from imperfect observations. |
| title_full | Estimation of neuron parameters from imperfect observations. |
| title_fullStr | Estimation of neuron parameters from imperfect observations. |
| title_full_unstemmed | Estimation of neuron parameters from imperfect observations. |
| title_short | Estimation of neuron parameters from imperfect observations. |
| title_sort | estimation of neuron parameters from imperfect observations |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008053&type=printable |
| work_keys_str_mv | AT josephdtaylor estimationofneuronparametersfromimperfectobservations AT samuelwinnall estimationofneuronparametersfromimperfectobservations AT alainnogaret estimationofneuronparametersfromimperfectobservations |