Evidence map›Paper›PMID 24550820›Full record

ArticleFrontiers in neuroinformatics2014

Equation-oriented specification of neural models for simulations.

Marcel Stimberg, Dan F M Goodman, Victor Benichoux, Romain Brette

Abstract read
In one paragraph

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 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
–field-weighted citation impact
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

53 citing papers in PubMed.

  1. Review
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  4. Long-Range Amplitude Coupling Is Optimized for Brain Networks That Function at Criticality.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022
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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

4 authors.

Marcel StimbergLaboratoire Psychologie de la Perception, CNRS and Université Paris Descartes Paris, France ; Département d'Etudes Cognitives, Ecole Normale Supérieure Paris, France.
Dan F M GoodmanEaton Peabody Laboratory, Massachusetts Eye and Ear Infirmary Boston, MA, USA ; Department of Otology and Laryngology, Harvard Medical School Boston, MA, USA.
Victor BenichouxLaboratoire Psychologie de la Perception, CNRS and Université Paris Descartes Paris, France ; Département d'Etudes Cognitives, Ecole Normale Supérieure Paris, France.
Romain BretteLaboratoire Psychologie de la Perception, CNRS and Université Paris Descartes Paris, France ; Département d'Etudes Cognitives, Ecole Normale Supérieure Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Simulating biological neuronal networks is a core method of research in computational neuroscience. A full specification of such a network model includes a description of the dynamics and state changes of neurons and synapses, as well as the synaptic connectivity patterns and the initial values of all parameters. A standard approach in neuronal modeling software is to build network models based on a library of pre-defined components and mechanisms; if a model component does not yet exist, it has to be defined in a special-purpose or general low-level language and potentially be compiled and linked with the simulator. Here we propose an alternative approach that allows flexible definition of models by writing textual descriptions based on mathematical notation. We demonstrate that this approach allows the definition of a wide range of models with minimal syntax. Furthermore, such explicit model descriptions allow the generation of executable code for various target languages and devices, since the description is not tied to an implementation. Finally, this approach also has advantages for readability and reproducibility, because the model description is fully explicit, and because it can be automatically parsed and transformed into formatted descriptions. The presented approach has been implemented in the Brian2 simulator.

Indexed as

computational neuroscienceneurosciencepythonsimulationsoftware

Identifiers

PMID24550820
PMCPMC3912318

What OpenQuestion holds

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Registered trials

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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.