Parametrizing analog multi-compartment neurons with genetic algorithms [version 2; peer review: 1 approved, 2 approved with reservations]
Background Finding appropriate model parameters for multi-compartmental neuron models can be challenging. Parameters such as the leak and axial conductance are not always directly derivable from neuron observations but are crucial for replicating desired observations. The objective of this study is...
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F1000 Research Ltd
2024-11-01
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| Series: | Open Research Europe |
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| Online Access: | https://open-research-europe.ec.europa.eu/articles/3-144/v2 |
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| author | Raphael Stock Eric Müller Jakob Kaiser Sebastian Schmitt Johannes Schemmel |
| author_facet | Raphael Stock Eric Müller Jakob Kaiser Sebastian Schmitt Johannes Schemmel |
| author_sort | Raphael Stock |
| collection | DOAJ |
| description | Background Finding appropriate model parameters for multi-compartmental neuron models can be challenging. Parameters such as the leak and axial conductance are not always directly derivable from neuron observations but are crucial for replicating desired observations. The objective of this study is to replicate the attenuation behavior of an excitatory postsynaptic potential (EPSP) traveling along a linear chain of compartments on the analog BrainScaleS-2 neuromorphic hardware platform. Methods In the present publication we use genetic algorithms to find suitable model parameters. They promise parameterization without domain knowledge of the neuromorphic substrate or underlying neuron model. To validate the results of the genetic algorithms, a comprehensive grid search was conducted. Furthermore, trial-to-trial variations in the analog system are counteracted utilizing spike-triggered averaging. Results and conclusions The algorithm successfully replicated the desired EPSP attenuation behavior in both single and multi-objective searches illustrating the applicability of genetic algorithms to parameterize analog neuromorphic hardware. |
| format | Article |
| id | doaj-art-47696dff20554c2bafd84de4d8a1fcea |
| institution | Kabale University |
| issn | 2732-5121 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | Open Research Europe |
| spelling | doaj-art-47696dff20554c2bafd84de4d8a1fcea2024-12-12T01:00:00ZengF1000 Research LtdOpen Research Europe2732-51212024-11-01320368Parametrizing analog multi-compartment neurons with genetic algorithms [version 2; peer review: 1 approved, 2 approved with reservations]Raphael Stock0https://orcid.org/0009-0008-5531-1072Eric Müller1Jakob Kaiser2https://orcid.org/0000-0002-3586-2634Sebastian Schmitt3Johannes Schemmel4Kirchhoff Institute for Physics, Heidelberg University, Heidelberg, 69120, GermanyEuropean Institute for Neuromorphic Computing, Heidelberg University, Heidelberg, 69120, GermanyKirchhoff Institute for Physics, Heidelberg University, Heidelberg, 69120, GermanyDepartment for Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, 37077, GermanyKirchhoff Institute for Physics, Heidelberg University, Heidelberg, 69120, GermanyBackground Finding appropriate model parameters for multi-compartmental neuron models can be challenging. Parameters such as the leak and axial conductance are not always directly derivable from neuron observations but are crucial for replicating desired observations. The objective of this study is to replicate the attenuation behavior of an excitatory postsynaptic potential (EPSP) traveling along a linear chain of compartments on the analog BrainScaleS-2 neuromorphic hardware platform. Methods In the present publication we use genetic algorithms to find suitable model parameters. They promise parameterization without domain knowledge of the neuromorphic substrate or underlying neuron model. To validate the results of the genetic algorithms, a comprehensive grid search was conducted. Furthermore, trial-to-trial variations in the analog system are counteracted utilizing spike-triggered averaging. Results and conclusions The algorithm successfully replicated the desired EPSP attenuation behavior in both single and multi-objective searches illustrating the applicability of genetic algorithms to parameterize analog neuromorphic hardware.https://open-research-europe.ec.europa.eu/articles/3-144/v2analog computing neuromorphic genetic algorithm multi-compartmenteng |
| spellingShingle | Raphael Stock Eric Müller Jakob Kaiser Sebastian Schmitt Johannes Schemmel Parametrizing analog multi-compartment neurons with genetic algorithms [version 2; peer review: 1 approved, 2 approved with reservations] Open Research Europe analog computing neuromorphic genetic algorithm multi-compartment eng |
| title | Parametrizing analog multi-compartment neurons with genetic algorithms [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_full | Parametrizing analog multi-compartment neurons with genetic algorithms [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_fullStr | Parametrizing analog multi-compartment neurons with genetic algorithms [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_full_unstemmed | Parametrizing analog multi-compartment neurons with genetic algorithms [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_short | Parametrizing analog multi-compartment neurons with genetic algorithms [version 2; peer review: 1 approved, 2 approved with reservations] |
| title_sort | parametrizing analog multi compartment neurons with genetic algorithms version 2 peer review 1 approved 2 approved with reservations |
| topic | analog computing neuromorphic genetic algorithm multi-compartment eng |
| url | https://open-research-europe.ec.europa.eu/articles/3-144/v2 |
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