ArticleNeuroinformatics2012
The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models.
Article in Neuroinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- Computational Concepts for Reconstructing and Simulating Brain Tissue.Advances in experimental medicine and biology · 2022Article
- Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions.PLoS computational biology · 2021Article
- ConGen-A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks.Frontiers in neuroinformatics · 2021Article
- Article
- Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0.Frontiers in neuroinformatics · 2018Article
- Article
- ANNarchy: a code generation approach to neural simulations on parallel hardware.Frontiers in neuroinformatics · 2015Article
- Python in neuroscience.Frontiers in neuroinformatics · 2015Article
- From model specification to simulation of biologically constrained networks of spiking neurons.Neuroinformatics · 2014Article
- Efficient generation of connectivity in neuronal networks from simulator-independent descriptions.Frontiers in neuroinformatics · 2014Article
- Equation-oriented specification of neural models for simulations.Frontiers in neuroinformatics · 2014Article
- The mind-brain relationship as a mathematical problem.ISRN neuroscience · 2013Review
- Neural Schematics as a unified formal graphical representation of large-scale Neural Network Structures.Frontiers in neuroinformatics · 2013Article
- Towards reproducible descriptions of neuronal network models.PLoS computational biology · 2009Article
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Authors and funding
1 author.
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Abstract
The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.
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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.