ReviewPLoS computational biology2022
Connectivity concepts in neuronal network modeling.
Review in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- A brain-constrained neural model of cognition and language with NEST: transitioning from the Felix framework.Cognitive neurodynamics · 2026Article
- Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains.PLoS computational biology · 2026Article
- WhenAPL bioengineering · 2025Review
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Modeling neuron-astrocyte interactions in neural networks using distributed simulation.PLoS computational biology · 2025Article
- A simplified model of NMDA-receptor-mediated dynamics in leaky integrate-and-fire neurons.Journal of computational neuroscience · 2025Article
- Suprachiasmatic nucleus-wide estimation of oscillatory temporal dynamics.PLoS computational biology · 2025Article
- NESTML: a generic modeling language and code generation tool for the simulation of spiking neural networks with advanced plasticity rules.Frontiers in neuroinformatics · 2025Article
- NeuroCarta: An automated and quantitative approach to mapping cellular networks in the mouse brain.Network neuroscience (Cambridge, Mass.) · 2025Article
- Computational Modeling of the Prefrontal-Cingulate Cortex to Investigate the Role of Coupling Relationships for Balancing Emotion and Cognition.Neuroscience bulletin · 2025Article
- Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Multi-scale spiking network model of human cerebral cortex.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- A layered microcircuit model of somatosensory cortex with three interneuron types and cell-type-specific short-term plasticity.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Stereotactic Electroencephalogram Recordings in Temporal Lobectomy Patients Demonstrates the Predictive Value of Interictal Cross-Frequency Correlations: A Retrospective Study.Brain sciences · 2024Article
- Encoding integers and rationals on neuromorphic computers using virtual neuron.Scientific reports · 2023Article
- Analysis of Network Models with Neuron-Astrocyte Interactions.Neuroinformatics · 2023Article
- NNMT: Mean-Field Based Analysis Tools for Neuronal Network Models.Frontiers in neuroinformatics · 2022Article
- Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution.Frontiers in neuroscience · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sustainable research on computational models of neuronal networks requires published models to be understandable, reproducible, and extendable. Missing details or ambiguities about mathematical concepts and assumptions, algorithmic implementations, or parameterizations hinder progress. Such flaws are unfortunately frequent and one reason is a lack of readily applicable standards and tools for model description. Our work aims to advance complete and concise descriptions of network connectivity but also to guide the implementation of connection routines in simulation software and neuromorphic hardware systems. We first review models made available by the computational neuroscience community in the repositories ModelDB and Open Source Brain, and investigate the corresponding connectivity structures and their descriptions in both manuscript and code. The review comprises the connectivity of networks with diverse levels of neuroanatomical detail and exposes how connectivity is abstracted in existing description languages and simulator interfaces. We find that a substantial proportion of the published descriptions of connectivity is ambiguous. Based on this review, we derive a set of connectivity concepts for deterministically and probabilistically connected networks and also address networks embedded in metric space. Beside these mathematical and textual guidelines, we propose a unified graphical notation for network diagrams to facilitate an intuitive understanding of network properties. Examples of representative network models demonstrate the practical use of the ideas. We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
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.