ArticleFrontiers in neuroinformatics2014
Efficient generation of connectivity in neuronal networks from simulator-independent descriptions.
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 7 papers.
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Who cites it
7 citing papers in PubMed.
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- Event-Based Update of Synapses in Voltage-Based Learning Rules.Frontiers in neuroinformatics · 2021Article
- ConGen-A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks.Frontiers in neuroinformatics · 2021Article
- Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0.Frontiers in neuroinformatics · 2018Article
- Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.Frontiers in neuroinformatics · 2017Article
- Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.Neural computation · 2016Article
- Python in neuroscience.Frontiers in neuroinformatics · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Simulator-independent descriptions of connectivity in neuronal networks promise greater ease of model sharing, improved reproducibility of simulation results, and reduced programming effort for computational neuroscientists. However, until now, enabling the use of such descriptions in a given simulator in a computationally efficient way has entailed considerable work for simulator developers, which must be repeated for each new connectivity-generating library that is developed. We have developed a generic connection generator interface that provides a standard way to connect a connectivity-generating library to a simulator, such that one library can easily be replaced by another, according to the modeler's needs. We have used the connection generator interface to connect C++ and Python implementations of the previously described connection-set algebra to the NEST simulator. We also demonstrate how the simulator-independent modeling framework PyNN can transparently take advantage of this, passing a connection description through to the simulator layer for rapid processing in C++ where a simulator supports the connection generator interface and falling-back to slower iteration in Python otherwise. A set of benchmarks demonstrates the good performance of the interface.
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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.