NITRC News Group Forum: the-connection-set-algebra---a-novel-formalism-for-the-representation-of-connectivity-structure-in-neuronal-network-models

NITRC News Group Forum: the-connection-set-algebra---a-novel-formalism-for-the-representation-of-connectivity-structure-in-neuronal-network-models http://www.nitrc.org/forum/forum.php?forum_id=2830 <div class="Abstract" lang="en"><a name="Abs1"></a>Abstract  <div class="normal">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, <cite>2008b</cite>) and an implementation in Python has been publicly released. </div> </div><ul> <li>Content Type Journal Article</li><li>Category Original Article</li><li>Pages 1-18</li><li>DOI 10.1007/s12021-012-9146-1</li><li>Authors<ul> <li>Mikael Djurfeldt, School of Computer Science and Communication, KTH, 10044 Stockholm, Sweden</li> </ul></li> </ul><ul class="parents"> <ul class="details"> <li>Journal <a href="http://www.springerlink.com/content/120559/">Neuroinformatics</a></li><li>Online ISSN 1559-0089</li><li>Print ISSN 1539-2791</li> </ul> </ul> en-us Copyright 2000-2026 NITRC OSI Sun, 19 Apr 2026 19:07:32 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator