Theoretical connections between mathematical neuronal models corresponding to different expressions of noise
| hal.structure.identifier | Group for Neural Theory [Paris] | |
| dc.contributor.author | DUMONT, Grégory | |
| hal.structure.identifier | Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN] | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | HENRY, Jacques | |
| hal.structure.identifier | Faculty of Computer Science [Iași] | |
| dc.contributor.author | TARNICERIU, Carmen Oana | |
| dc.date.accessioned | 2024-04-04T03:12:17Z | |
| dc.date.available | 2024-04-04T03:12:17Z | |
| dc.date.issued | 2016 | |
| dc.identifier.issn | 0022-5193 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/193854 | |
| dc.description.abstractEn | Identifying the right tools to express the stochastic aspects of neural activity has proven to be one of the biggest challenges in computational neuroscience. Even if there is no definitive answer to this issue, the most common procedure to express this randomness is the use of stochastic models. In accordance with the origin of variability, the sources of randomness are classified as intrinsic or extrinsic and give rise to distinct mathematical frameworks to track down the dynamics of the cell. While the external variability is generally treated by the use of a Wiener process in models such as the Integrate-and-Fire model, the internal variability is mostly expressed via a random firing process. In this paper, we investigate how those distinct expressions of variability can be related. To do so, we examine the probability density functions to the corresponding stochastic models and investigate in what way they can be mapped one to another via integral transforms. Our theoretical findings offer a new insight view into the particular categories of variability and it confirms that, despite their contrasting nature, the mathematical formalization of internal and external variability are strikingly similar. | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.subject.en | Fokker-Planck equation | |
| dc.subject.en | Age structured model | |
| dc.subject.en | Neural noise | |
| dc.subject.en | Noisy Leaky Integrate-and-Fire model | |
| dc.subject.en | Escape rate | |
| dc.title.en | Theoretical connections between mathematical neuronal models corresponding to different expressions of noise | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1016/j.jtbi.2016.06.022 | |
| dc.subject.hal | Mathématiques [math]/Equations aux dérivées partielles [math.AP] | |
| bordeaux.journal | Journal of Theoretical Biology | |
| bordeaux.page | 31-41 | |
| bordeaux.volume | 406 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-01414929 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-01414929v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Theoretical%20Biology&rft.date=2016&rft.volume=406&rft.spage=31-41&rft.epage=31-41&rft.eissn=0022-5193&rft.issn=0022-5193&rft.au=DUMONT,%20Gr%C3%A9gory&HENRY,%20Jacques&TARNICERIU,%20Carmen%20Oana&rft.genre=article |
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