Academic Journal
Extraction of parameters of a stochastic integrate-and-fire model with adaptation from voltage recordings
| Title: | Extraction of parameters of a stochastic integrate-and-fire model with adaptation from voltage recordings |
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| Authors: | Kiessling, Lilli, Lindner, Benjamin |
| Source: | Biol Cybern |
| Publisher Information: | Springer Science and Business Media LLC, 2024. |
| Publication Year: | 2024 |
| Subject Terms: | Stochastic Processes [MeSH], Parameter extraction for neural models, Humans [MeSH], Adaptation, Physiological/physiology [MeSH], Membrane Potentials/physiology [MeSH], Spike-frequency adaptation, Computer Simulation [MeSH], Integrate-and-fire model, Animals [MeSH], Stochastic spiking, Models, Neurological [MeSH], Neurons/physiology [MeSH], Action Potentials/physiology [MeSH], Original Article, 0301 basic medicine, ddc:000, Models, Neurological, Action Potentials, 570 Biologie, spike-frequency adaptation, Membrane Potentials, stochastic spiking, 03 medical and health sciences, Animals, Humans, Computer Simulation, Neurons, Stochastic Processes, parameter extraction for neural models, 0303 health sciences, 500 Naturwissenschaften und Mathematik::570 Biowissenschaften, Biologie::570 Biowissenschaften, Biologie, integrate-and-fire model, Adaptation, Physiological, 000 Informatik, Informationswissenschaft, allgemeine Werke, ddc:570 |
| Description: | Integrate-and-fire models are an important class of phenomenological neuronal models that are frequently used in computational studies of single neural activity, population activity, and recurrent neural networks. If these models are used to understand and interpret electrophysiological data, it is important to reliably estimate the values of the model’s parameters. However, there are no standard methods for the parameter estimation of Integrate-and-fire models. Here, we identify the model parameters of an adaptive integrate-and-fire neuron with temporally correlated noise by analyzing membrane potential and spike trains in response to a current step. Explicit formulas for the parameters are analytically derived by stationary and time-dependent ensemble averaging of the model dynamics. Specifically, we give mathematical expressions for the adaptation time constant, the adaptation strength, the membrane time constant, and the mean constant input current. These theoretical predictions are validated by numerical simulations for a broad range of system parameters. Importantly, we demonstrate that parameters can be extracted by using only a modest number of trials. This is particularly encouraging, as the number of trials in experimental settings is often limited. Hence, our formulas may be useful for the extraction of effective parameters from neurophysiological data obtained from standard current-step experiments. |
| Document Type: | Article Other literature type |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 1432-0770 |
| DOI: | 10.1007/s00422-024-01000-2 |
| DOI: | 10.21203/rs.3.rs-4383393/v1 |
| DOI: | 10.18452/32963 |
| DOI: | 10.14279/depositonce-23794 |
| Access URL: | https://pubmed.ncbi.nlm.nih.gov/39738681 https://repository.publisso.de/resource/frl:6499593 |
| Rights: | CC BY |
| Accession Number: | edsair.doi.dedup.....8b8fbc3f1aae56cf3dd38070065f8dc6 |
| Database: | OpenAIRE |
| ISSN: | 14320770 |
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| DOI: | 10.1007/s00422-024-01000-2 |