XML for Model Specification: Introduction and Workshop

3. Text descriptions define the structure and semantics of the data. Language elements/schema/object classes. These are all potential benefits to using XML�.Text descriptions define the structure and semantics of the data. Language elements/schema/object classes. These are all potential benefits to using XML�.

4. Text descriptions define the structure and semantics of the data. Language elements/schema/object classes. These are all potential benefits to using XML�.Text descriptions define the structure and semantics of the data. Language elements/schema/object classes. These are all potential benefits to using XML�.

10. Scratch pad Ideas for new slides.

14. Language independent way to store complex structured information. Huge industry momentum. Not a programming language � so encourages declarative specifications. Possible to transform from one format to another � whereas programs have to be recoded by hand

15. Cumbersome to edit by hand Large text files, need to be compressed Harder to parse than ad hoc text formats Not suitable for binary data

18. Put XML model spec on your website, + links to code to run it. Plus links back to any experimental data used to derive parameters / validate results. See Robert Gentleman's campaign for �reproducible research� (and also ModelDB)

19. Lots of levels of scale and detail of models (from protein interactions to large scale networks of neurons) Different simulators have different and changing capabilities � which creates a moving target for attempts to build any standards

20. Should a model exchange format restrict itself to a standard subset of possible models, or cope with any possible model?

22. SBML : a standard for intracellular pathway models CellML MathML

23. �I'm writing a simulator and I'd like to get the models into NeuroML � what do I do?� (1) Separate out the declarative aspects of the model spec (2) Serialise the model into XML, using the NeuroML development kit (in Java) or your own code (3) If any other developers are creating similar models, see if you can agree on a common set of classes to describe the models by hand

25. Data binding

29. simulators adopting own XML formats for serialising model descriptions common standards working where the domain is stable (SBML, MorphML)

30. How much standardisation is useful? Just XML in any format? XML with uniform mapping from classes to <tags>? A set of rigid standards for compartmental neurons, channels, receptors, networks, ...? What features are needed from a development kit? C++, python, java?

50. Building 3D Network Models with neuroConstruct(Summary of main presentation) Padraig Gleeson University College London p.gleeson@ucl.ac.uk WAM-BAMM*05 31 March 2005 This is the presentation given at the 2005 WAM-BAMM meeting in San Antonio Texas. It's a general introduction to the functionality of neuroConstruct and should provide a good overview of what kind of modelling the application can be used for.This is the presentation given at the 2005 WAM-BAMM meeting in San Antonio Texas. It's a general introduction to the functionality of neuroConstruct and should provide a good overview of what kind of modelling the application can be used for.

51. Scope of Application Reuses existing base of models/modellers Adds functionality Graphical interface Checks on morphologies Network building capabilities Storage/replay/analysis of simulations Built with Java: runs on any platform Code produced is native GENESIS/NEURON Some of the ways neuroConstruct adds extra functionality to models developed in NEURON and GENESIS. The key is that the code produced is GENESIS/NEURON script as opposed to a new format. Modellers familiar wuith these formats can work easily with these generated files. The extra functionality neuroConstruct adds includes: a better 3D interface, checks on the cell morphology, ability to easily construct networks of cells and simulation management. Some of the ways neuroConstruct adds extra functionality to models developed in NEURON and GENESIS. The key is that the code produced is GENESIS/NEURON script as opposed to a new format. Modellers familiar wuith these formats can work easily with these generated files. The extra functionality neuroConstruct adds includes: a better 3D interface, checks on the cell morphology, ability to easily construct networks of cells and simulation management.

52. Visualization Single Cells can be viewed in 3D Information on morphology Checks on consistency of cell structure Segments can be edited Info on basic electrophysiology One of the most obvious advantages neuroConstruct is the better support for visualisation of the cells. Problems with cell morphology can be isolated quicker, and these issues rectified by editing the cells. Information on the biophysics of individual segments in the cells can be viewed, and figures for membrane capacitance/resistance, etc. given in the unit system of each of the target simulation environmentsOne of the most obvious advantages neuroConstruct is the better support for visualisation of the cells. Problems with cell morphology can be isolated quicker, and these issues rectified by editing the cells. Information on the biophysics of individual segments in the cells can be viewed, and figures for membrane capacitance/resistance, etc. given in the unit system of each of the target simulation environments

53. Screenshot: Cell Visualization A view of a single cell in 3D placed at random in a rectangular box.A view of a single cell in 3D placed at random in a rectangular box.

54. Packing in 3D Cell Groups are packed in 3D Regions Rectangular Box Spherical Various Packing Patterns Random Cubic close packed Hexagonal Single positioned Evenly spaced in 1D Each of the prototype cells in neuroConstruct can be placed in 3D Regions in Cell Groups. At the moment, Cells can be placed in either a rectangular box (cubloid) or a spherical region. These can overlap. Cells can be packed in a number of ways, such as random, regularly spaced or at specified locations.Each of the prototype cells in neuroConstruct can be placed in 3D Regions in Cell Groups. At the moment, Cells can be placed in either a rectangular box (cubloid) or a spherical region. These can overlap. Cells can be packed in a number of ways, such as random, regularly spaced or at specified locations.

55. Screenshot: Packing in 3D Screenshot of cells packed in 2 Regions Cells packed in the same region are packed around one another in the order the Cell Groups are listed in the tableScreenshot of cells packed in 2 Regions Cells packed in the same region are packed around one another in the order the Cell Groups are listed in the table

56. Simulator Interaction(1) Morphology files can be imported from GENESIS (*.p readcell format files) NEURON (most ntscable like files, with create, pt3dadd, connect) Cvapp (SWC format files) MorphML Imported cells are checked for validity: i.e. errors which may cause problems on some platforms zero length segments all except root segment have parents unique names, etc. The various formats in which The various formats in which

57. Simulator Interaction(2) Files can currently be exported to: NEURON, for simulation GENESIS, for simulation MorphML, for publishing/use by other simulators Cell info held in simulator independent format Can be mapped to other/future simulators

58. Cell Processes Generic models of Cell Process (channels/synapses) can be used in neuroConstruct Model separated from experimentally measured parameters Reuse of tried and tested template files Can be mapped on to any simulator Automatic handling of units

59. Modularity of Cell Processes (1)

60. Modularity of Cell Processes (2)

61. Screenshot: Cell Processes

62. Morphology mapping (1) neuroConstruct Concepts Section: unbranched part of axon/dendrite with the same biophysical properties Segment: Specifies one 3D point along Section, shaped like conical frustum Section specifies start point, Segments specify 3D points along

63. Morphology mapping (2) Going from GENESIS -> NEURON Reasonably straightforward Compartments in GENESIS mapped to segments in neuroConstruct Going from NEURON -> GENESIS Non-trivial: mapping conical sections to cylinders Simple mapping: each segment to compartment with equivalent surface area

64. NeuroML/MorphML interaction neuroConstruct currently allows: Import & export of MorphML morphologies Future support Greater support for specification of groups/Cell Process locations in MorphML format Importation of Cell Processes in NeuroML format Export of network structure in NeuroML format Generation of simulation code in NeuroML/NeoSim format