MUSIC (multi-simulation coordinator) is a standard for run-time exchange of data between parallel applications in a cluster environment. The standard is designed specifically for interconnecting large scale neuronal network simulators either with each-other or with other tools. MUSIC provides mechanisms to transfer massive amounts of event information and continuous values from one parallel application to another including data transfer between applications that use different time steps and different data allocation strategies.
New stable release (*version 2, beta 5*) of the NeuroML is available! Read more
NeuroML is a simulator-independent, XML-based standardized model description language for computational neuroscience that provides a common data format for defining and exchanging descriptions of neuronal cell and network models. NeuroML focuses on models which are based on the biophysical and anatomical properties of real neurons, i.e.
Connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in both small-scale and large-scale neuronal network models. It provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA can be used as a component of neuronal network simulators or other tools. A Python implementation is available on GitHub.
PyNN is a simulator-independent language for building neuronal network models. The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. PyNN provides a library of standard neuron, synapse, and synaptic plasticity models which have been verified to work the same on the different supported simulators. PyNN also provides a set of commonly-used connectivity algorithms (e.g.