Evidence map›Paper›PMID 35784184›Full record

ArticleFrontiers in neuroinformatics2022

Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models.

Alexander Ladd, Kyung Geun Kim, Jan Balewski, Kristofer Bouchard, Roy Ben-Shalom

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 31% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Alexander LaddElectrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States.
Kyung Geun KimElectrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States.
Jan BalewskiNERSC, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
Kristofer BouchardHelen Wills Neuroscience Institute & Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA, United States.
Roy Ben-ShalomNeurology Department, MIND Institute, University of California, Davis, Sacramento, CA, United States.
Lawrence Berkeley National Laboratory · USUniversity of California, Berkeley · USUniversity of California, Davis · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named

Indexed as

biophysical neuron modelelectrophysiologyevolutionary algorithmshigh performance computingnon-convex optimizationstrong scalingweak scaling

Identifiers

PMID35784184
PMCPMC9248031
OpenAlexW4283026181

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.