Difference between revisions of "CSA"
(Python implementation of Connection-set Algebra) |
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| − | {{PAGENAME}} is a {{#show: {{PAGENAME}} |?description}}. | + | {{PAGENAME}} is a {{#show: {{PAGENAME}} |?description}}. Connection-set Algebra is a formalism for describing connectivity in neuronal networks. The CSA library provides elementary connection-sets and operators for combining them. It also provides an iteration interface to such connection-sets enabling efficient iteration over existing connections with a small memory footprint also for very large networks. The CSA can be used as a component of neuronal network simulators or other tools. |
See the following reference for more information: | See the following reference for more information: | ||
Latest revision as of 13:53, 23 April 2013
CSA is a Python implementation of the Connection-set Algebra (Djurfeldt 2012). Connection-set Algebra is a formalism for describing connectivity in neuronal networks. The CSA library provides elementary connection-sets and operators for combining them. It also provides an iteration interface to such connection-sets enabling efficient iteration over existing connections with a small memory footprint also for very large networks. The CSA can be used as a component of neuronal network simulators or other tools.
See the following reference for more information:
Mikael Djurfeldt (2012) "The Connection-set Algebra---A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models" Neuroinformatics 10(3), 1539-2791, <http://dx.doi.org/10.1007/s12021-012-9146-1>
General info
- Ease of Use: Intermediate
- Maturity:Intermediate
Prerequisites
- Python
Availability
No installations reported.
License
License: Free.
Experts
These experts have registered specific competence on this subject:
| Field | AE FTE | General activities | ||
|---|---|---|---|---|
| Mikael Djurfeldt (PDC) | PDC | Neuroinformatics | 100 |
