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

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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items