ArticleFrontiers in neuroinformatics2014
A flexible, interactive software tool for fitting the parameters of neuronal models.
Article in Frontiers in neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Extraction of parameters of a stochastic integrate-and-fire model with adaptation from voltage recordings.Biological cybernetics · 2024Article
- Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework.PLoS computational biology · 2024Article
- Automated customization of large-scale spiking network models to neuronal population activity.Nature computational science · 2024Article
- Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks.Brain sciences · 2024Article
- Synaptic and dendritic architecture of different types of hippocampal somatostatin interneurons.PLoS biology · 2024Article
- Ion-channel degeneracy and heterogeneities in the emergence of complex spike bursts in CA3 pyramidal neurons.The Journal of physiology · 2023Article
- Heterogeneous off-target impact of ion-channel deletion on intrinsic properties of hippocampal model neurons that self-regulate calcium.Frontiers in cellular neuroscience · 2023Article
- Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations.Neuroscience · 2022Review
- Article
- HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data.PLoS computational biology · 2021Article
- On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell.Frontiers in neuroinformatics · 2021Article
- Article
- Article
- Systematic generation of biophysically detailed models for diverse cortical neuron types.Nature communications · 2018Article
- Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes.Frontiers in neuroinformatics · 2018Article
- Arkheia: Data Management and Communication for Open Computational Neuroscience.Frontiers in neuroinformatics · 2018Article
- Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons.Journal of computational neuroscience · 2016Article
- BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.Frontiers in neuroinformatics · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.