link to homepage

Institute of Neuroscience and Medicine

Navigation and service

Talk by Mikael Djurfeldt

1 PDC, KTH, S-100 44 Stockholm, Sweden

2 INCF, KI, S-171 77 Stockholm, Sweden

06 Aug 2012 14:00
06 Aug 2012 15:00
Bldg. 15.22, Seminar Room 3009, 1. OG

The Connection-set Algebra: A formalism for the representation of connectivity structure in neuronal network models

The connection-set algebra (CSA) [1] is a novel and general formalism for the description of connectivity in neuronal network models, from its small-scale to its large-scale structure. It provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets.


The CSA is expressive enough to describe a wide range of connectivities 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.


This talk will give an introduction to the basic concepts of the CSA, briefly demonstrate the CSA Python implementation [2] and describe how it can be interfaced to other tools such as NEST and PyNN.



1. Djurfeldt M: The Connection-set Algebra—A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models. Neuroinformatics 2012, 10(3):287-304 2. The Python CSA implementation []