The results of fitting a phenomenological spiking neuronal model (the adaptive exponential integrate-and-fire model) to capture experimental recordings with multiple traces.

<p>The plots in all four panels are analogous to those in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012039#pcbi.1012039.g001" target="_blank">Fig 1</a>. Panel A shows the results of a best-fitting model found by the CMAES algori...

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Bibliographic Details
Main Author:	Máté Mohácsi (20469514) (author)
Other Authors:	Márk Patrik Török (20469517) (author), Sára Sáray (10053139) (author), Luca Tar (18144163) (author), Gábor Farkas (20469520) (author), Szabolcs Káli (10053142) (author)
Published:	2024
Subjects:	Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified six distinct benchmarks represent typical scenarios particle swarm optimization optimization runs performed neuronal parameter search found good solutions current benchmarking study best solutions found additional use case detailed neuronal models common interface provided identified several algorithms simplest use cases performance computing architectures twenty different algorithms manual model tuning use cases subcellular model graphical interface different tools wide selection ubiquitous challenge take advantage recent years online database neuroscientific research neuroscience community involves tuning gradually replaced generic platform failed completely detailed comparison convergence speed complex problems biochemical pathways appropriate algorithm also demonstrate aid members advanced usage
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The results of fitting a phenomenological spiking neuronal model (the adaptive exponential integrate-and-fire model) to capture experimental recordings with multiple traces.

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