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Modeling interference-free neuron spikes with optogenetic stimulation
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Noel, Adam, Monabbati, Shayan, Makrakis, Dimitrios and Eckford, Andrew W. (2019) Modeling interference-free neuron spikes with optogenetic stimulation. IEEE Transactions on Molecular, Biological, and Multi-Scale Communications, 5 (2). pp. 100-111. doi:10.1109/TMBMC.2020.2981655
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WRAP-modeling-interference-free-neuron-spikes-optogenetic-Noel-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1908Kb) | Preview |
Official URL: https://doi.org/10.1109/TMBMC.2020.2981655
Abstract
This paper predicts the ability to externally control the firing times of a cortical neuron whose behavior follows the Izhikevich neuron model. The Izhikevich neuron model provides an efficient and biologically plausible method to track a cortical neuron's membrane potential and its firing times. The external control is a simple optogenetic model represented by an illumination source that stimulates a saturating or decaying membrane current. This paper considers firing frequencies that are sufficiently low for the membrane potential to return to its resting potential after it fires. The time required for the neuron to charge and for the neuron to recover to the resting potential are numerically fitted to functions of the Izhikevich neuron model parameters and the peak input current. Results show that simple functions of the model parameters and maximum input current can be used to predict the charging and recovery times, even when there are deviations in the actual parameter values. Furthermore, the predictions lead to lower bounds on the firing frequency that can be achieved without significant distortion.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > T Technology (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Optogenetics , Neurosciences -- Mathematical models, Neurons , Neurosciences -- Data processing | ||||||||
| Journal or Publication Title: | IEEE Transactions on Molecular, Biological, and Multi-Scale Communications | ||||||||
| Publisher: | Institute of Electrical and Electronics Engineers | ||||||||
| ISSN: | 2332-7804 | ||||||||
| Official Date: | November 2019 | ||||||||
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| Volume: | 5 | ||||||||
| Number: | 2 | ||||||||
| Page Range: | pp. 100-111 | ||||||||
| DOI: | 10.1109/TMBMC.2020.2981655 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| RIOXX Funder/Project Grant: |
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| Open Access Version: |
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