Navigate neural circuit data at different levels of abstraction using schematic representations with NeuroScheme on your desktop. Representing neural structures as icons and glyphs allows interpretation of data features quickly and precisely.
NeuroScheme (NS) uses schematic representations, such as icons and glyphs to encode attributes of neural structures (i.e. neurons, columns, layers, populations, etc.). This abstraction alleviates problems with displaying, navigating, and analysing, large datasets. NS has been designed specifically to manage hierarchically organised neural structures; one can navigate through the levels of the hierarchy, and hone in on their desired level of details.
NS works using what we call "domains". These domains specify which entities, attributes and relationships are going to be used for a specific use case. NS currently has two built-in domains: “cortex” and “congen”. The “cortex” domain is designed for navigating and analysing cerebral cortex structures (i.e. neurons, micro-columns, columns, layers, etc.). The “congen” domain can be used to define the properties of both cells and connections, create circuits composed of neurons, and build populations. Groups of populations can be easily moved to a higher level of abstraction (such as column or layer), allowing one to create complex networks with little effort. These circuits can be exported afterwards and used for further analysis and simulations.
NS can be coupled with other applications, such as NeuroTessMesh or ViSimpl, in a coordinated multi-view framework.
The abstracted visualisation in NS can be coupled with NeuroTessMesh to allow realistic visualisation of the entities. NeuroTessMesh provides a visual environment for the generation of 3D polygonal meshes that approximate the membrane of neuronal cells, from the morphological tracings that describe the morphology of the neurons.
Neuroscheme coupled with ViSimpl lets you use the symbolic representations and different levels of abstraction to navigate through the dataset, and at the same time have the ability to visualise the spatiotemporal and electrophysiological components of the simulation.