It is not ;)
This library is based on existing graph libraries (such as graph_tool, igraph, networkx, and possibly soon SNAP) and acts as a convenient interface to build various networks from efficient and verified algorithms.
Moreover, it also acts as an interface between those graph libraries and the NEST simulator.
For users that are in a hurry, you can go directly to the Tutorial section. For more specific and detailed examples, several topics are then detailed separately in the following pages:
Note
This library provides many tools which will (or not) be loaded on startup depending on the python packages available on your computer. The default behaviour of those tools is set in the ~/.nngt/nngt.conf file (see Configuration). Moreover, to see all potential messages related to the import of those tools, you can use the logging function of NNGT, either by setting the log_level value to INFO, or by setting log_to_file to True, and having a look at the log file in ~/.nngt/log/.
Neural networks are described by four graph classes which inherit from the main
class of the chosen graph library (gt.Graph
,
igraph.Graph
or networkx.DiGraph
):
Graph
: base for simple topological graphs with no spatial
structure, nor biological propertiesSpatialGraph
: subclass for spatial graphs without
biological propertiesNetwork
: subclass for topological graphs with biological
properties (to interact with NEST)SpatialNetwork
: subclass with spatial and biological
properties (to interact with NEST)Using these objects, the user can access to the topological structure of the network (including the connections’ type – inhibitory or excitatory – and its weight, which is always positive)
Warning
This object should never be directly modified through the initial library’s
methods but always using those of NNGT. If, for some reason, you should
directly use the methods from the graph library on the object, make sure they
do not modify its structure; any modification performed from a method other
than those of Graph
subclasses will lead to undefined
behaviour!
Nodes/neurons are defined by a unique index which can be used to access their properties and those of the connections between them.
The graph objects can have other attributes, such as:
shape
for SpatialGraph
and SpatialNetwork
,
which describes the spatial delimitations of the neurons’ environment (e.g.
many in vitro culture are contained in circular dishes),population
, for Network
, which contains informations on
the various groups of neurons that exist in the network (for instance
inhibitory and excitatory neurons can be grouped together),connections
which stores the informations about the synaptic connections
between the neurons.Several classical graphs are efficiently implemented and the generation procedures are detailed in the documentation.
SpatialGraph
with networkx does not work.