Diffusion approximation of neuronal models revisited
Leaky integrate-and-fire neuronal models with reversal potentials have a number of different diffusion approximations, each depending on the form of the amplitudes of the postsynaptic potentials.Probability distributions of the first-passage times of the membrane potential in the original model and...
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| Language: | English |
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AIMS Press
2013-08-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.11 |
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| author | Jakub Cupera |
| author_facet | Jakub Cupera |
| author_sort | Jakub Cupera |
| collection | DOAJ |
| description | Leaky integrate-and-fire neuronal models with reversal potentials have a number of different diffusion approximations, each depending on the form of the amplitudes of the postsynaptic potentials.Probability distributions of the first-passage times of the membrane potential in the original model and itsdiffusion approximations are numerically compared in order to find whichof the approximations is the most suitable one.The properties of the random amplitudes of postsynapticpotentials are discussed.It is shown on a simple example that the quality of the approximation depends directly on them. |
| format | Article |
| id | doaj-art-c08070fd9dd744f7a1565d44d78dfa32 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2013-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-c08070fd9dd744f7a1565d44d78dfa322025-01-24T02:26:48ZengAIMS PressMathematical Biosciences and Engineering1551-00182013-08-01111112510.3934/mbe.2014.11.11Diffusion approximation of neuronal models revisitedJakub Cupera0Institute of Physiology, Academy of Sciences of the Czech Republic, Videnska 1083, 142 20 Prague 4Leaky integrate-and-fire neuronal models with reversal potentials have a number of different diffusion approximations, each depending on the form of the amplitudes of the postsynaptic potentials.Probability distributions of the first-passage times of the membrane potential in the original model and itsdiffusion approximations are numerically compared in order to find whichof the approximations is the most suitable one.The properties of the random amplitudes of postsynapticpotentials are discussed.It is shown on a simple example that the quality of the approximation depends directly on them.https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.11reversal potentialsintegrate-and-fire modelstein's modeldiffusion approximation. |
| spellingShingle | Jakub Cupera Diffusion approximation of neuronal models revisited Mathematical Biosciences and Engineering reversal potentials integrate-and-fire model stein's model diffusion approximation. |
| title | Diffusion approximation of neuronal models revisited |
| title_full | Diffusion approximation of neuronal models revisited |
| title_fullStr | Diffusion approximation of neuronal models revisited |
| title_full_unstemmed | Diffusion approximation of neuronal models revisited |
| title_short | Diffusion approximation of neuronal models revisited |
| title_sort | diffusion approximation of neuronal models revisited |
| topic | reversal potentials integrate-and-fire model stein's model diffusion approximation. |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.11 |
| work_keys_str_mv | AT jakubcupera diffusionapproximationofneuronalmodelsrevisited |