Introduction

morphforge is described in Mike Hull's Ph.D Thesis and this paper(link)

Modern simulators such as NEURON support a range of features; from modelling the internal diffusion of ions within a multicompartmental neuron to the calculation of extracellular potentials. It would require huge number of resources to implement a new interface that was the superset of these features. Rather than try to define a single monolithic system, the approach taken in morphforge is to provide a collection of classes and interfaces which form the core infrastructure, and then use a system of plugins which can be written to implement particular features. For example, morphforge is agnostic to how a synapse model is defined. The core of morphforge defines a minimal interface, and then plugins can be written, which allow a synapse specified in Python or MODL for example to be used with NEURON. This means morphforge naturally splits into two parts, morphforge-core, which contains the core infrastructure, and morphforge-contrib, which contains for example the plugins that define how a synapse model specified in a particular format is mapped to a particular simulator backend.

morphforge is split into four layers each defining a set of classes that work together as an object-model (Table. 1). The higher layers depend on the lower levels, but lower levels do not need the higher ones, for example, Morphology objects are used by the simulation-layer, but can also be used without it, for example for anatomical reconstructions. The core-layer provides a single point of access to control random number seeding, simulation settings and locations on the filesystem access as well as the plugin infrastructure and utility functions. The morphology-layer provides classes that represent neuronal morphologies as a tree of cylinders and functions for their creation, import, export, rendering, traversal and manipulation. The simulation-layer defines a simulator independent object-model on top of the morphology objects for defining multicompartmental neurons with complex channel distributions. Primitives for defining network connectivity and an interface for recording values during a simulation are also provided. It provides a set of component-libraries to allow objects, such as morphologies, channels and synapses,to be defined once and reused with different parameters as well as an extensible high-level object-model for representing analog signals with units. Finally, the simulationanalysis-layer provides functions for analysing the output of simulations such as spike detection, a visualisation system for easily viewing the outputs of simulations and infrastructure for automatically generating summaries of simulations including the details of components such as channels and synapses.

morphforge-core

morphforge-contrib

morphforge-NEURON

Reference

Indices and tables

• Index
• Module Index
• Search Page