Architecture

Architecture documentation. The Add-on follows classical model-view-controller pattern.

ToDo list for the project:

• Additional support for NeuroML, NineML and SpineML
• Provide additional information about network in the GUI
• Allow for more biologically sound axon curvatures

Model

The model consists of the classes that represent an actual network:

• Cells
• Connections between them (Projections)
• Populations of cells
• Networks

as well as description language specific classes and helper classes, like ColorMap.

High level model

class neuron_visualization_addon.model.Network.Network(id)

Bases: object

This class represents a network. Network consists of populations and projections between them.

animateSpikes(spikes, colorMap='jet', animateAxons=True)

Animate network.

Parameters:spikes (dict) – Dictionary of cell IDs as keys and a tuple of time and intensity as values

highlightPopulation(population_id)

Highlights a population with a random color.

Parameters:population_id (string) – The population ID.

highlightPopulationsAll()

Highlights all populations with random colors.

location

Location of the center of mass.

pullProjections(populationID_1, populationID_2, strength)

Pull projections between two populations.

Parameters:

• populationID_1 (string) – First population ID
• populationID_2 (string) – Second population ID
• strength (int) – Pulling strength

pullProjectionsAll(strength)

Pulls all projections to the center of their mass.

Parameters:strength (int) – Pulling strength

removeHighlightAll()

Removes highlight from all populations.

class neuron_visualization_addon.model.Population.Population(id, size, cell_type)

Bases: object

This class represents a population of brain cells in the network

location

Location of the center of mass.

pullTogether()

Pull cells towards the center of mass.

removeColor()

Remove coloring from the population.

setColor(color=(0.0, 0.0, 0.0))

Set RGB color to the whole population

Parameters:color (tuple) – RGB color

class neuron_visualization_addon.model.Cell.Cell(id, location=(0, 0, 0))

Bases: object

This class represents a brain cell in the network

drawAxon(weight, destinationCell)

Draw an axon between this cell and destination cell.

Parameters:

• weight (float) – The weight of a connection.
• destinationCell (Cell) – Projection destionation cell.

Returns:

Projection – Created projection

isProjectedTo(projection)

Add projections from which the input is received.

Parameters:projection (Projection) – Projection to be added

location

Location of the cell.

setColor(color=(0.0, 0.0, 0.0))

Set RGB color

Parameters:color (tuple) – RGB color

setSpike(time, intensity, colorMap='jet', animateAxons=True)

Set cell to spike at a time with a specific intensity

Parameters:

• time (float) – Frame of a spike
• intensity (float) – Intensity of a spike from 0 to 1
• colorMap (string) – Color map for intensities Visualization

Todo:

Neuron swelling without axon

setSpikes(spikes, colorMap='jet', animateAxons=True)

Set cell to spike at specific timse with a specific intensities

Parameters:

• spikes (array) – Array with times is ms and spike intensities
• colorMap (string) – Color map for intensities Visualization

class neuron_visualization_addon.model.Projection.Projection(parent_object)

Bases: object

This class represents a projection between neurons in the network

bevel_objects = {}

curve_taper = None

destination

Destination of the projection (receiving soma)

initTaper()

Initialize global taper curve

makeProjection(weight, projection)

Make projection from defined bezier curve.

Parameters:

• weight (array) – Weight of the connection
• projection – Array of bezier points

makeSimpleProjection(weight, destination)

Make simple projection: straight line between parent object and destination object.

Parameters:

• weight (float) – Weight of the connection
• weight – Weight of the connection

middle

Middle of the projection

pullCenterTo(destination, pull)

Pull center of the projection to a specific position

Parameters:destination – Where to pull project to

setColor(color=(0.0, 0.0, 0.0))

Set RGB color.

Parameters:color (tuple) – RGB color

spike(time, minColor, maxColor)

Propagate potential from parent cell to receiving cells

Parameters:

• time (float) – Where to spike
• minColor (tuple) – Color for resting state
• maxColor (tuple) – Color for a spike

start

Start of the projection (soma).

taper = None

updateProjectionCurve(projection_points)

Updates projection curve (axon).

Parameters:projection_points (array) – Array of points that define a bezier curve

updateTaperCurve()

Updates taper curve (shape curve).

Parameters:taper_points (array) – Array of points that define a bezier curve

NeuroML2 model

class neuron_visualization_addon.model.NetworkNeuroML2.NetworkNeuroML2(network, scale=10, loaded_cells={})

Bases: neuron_visualization_addon.model.Network.Network

This class represents a network read from NeuroML file.

class neuron_visualization_addon.model.PopulationNeuroML2.PopulationNeuroML2(population, scale=10, loaded_cells={})

Bases: neuron_visualization_addon.model.Population.Population

This class represents a population of brain cells in the network based on NeuroML2

class neuron_visualization_addon.model.CellNeuroML2.CellNeuroML2(cell, scale=10)

Bases: neuron_visualization_addon.model.Cell.Cell

This class represents brain cells in the network parsed from NeuroML2 files

bevel_objects = {}

draw_segment(size, bezier)

Draw segment of the cell of given size and bezier points

Parameters:

• size (float) – Segment size or connection radius
• bezier (list) – List of bezier points [(control, handle_left, handle_right)]

location

Location

Parameters:location (Vector | tuple) – Cell location

make_soma(size, location)

Make soma of the cell

Parameters:

• size (float) – Soma size / radius
• location (Vector | tuple) – New location

parse_model(cell, scale)

Parse cell model

Parameters:cell (NeuroML2 Cell) – Cell description object

setSpikes(spikes, colorMap='jet')

Todo:Overwrite this method

Helper classes

class neuron_visualization_addon.model.ColorMap.ColorMap

Bases: object

Color map for the animation

In order to augment the list of color maps, define your own function within this class and add it to the mapsList dictionary

Parameters:mapsList (list) – list of available maps

getColor(colorMap='jet')

Return the color that corresponds to the intensity in a specified color map

Parameters:

• intensity (float) – Firing intensity between 0 and 1
• colorMap (string) – color map

Returns:

tuple – rgb color

jetColor()

Calculates jet color based on firing intensity

Parameters:intensity (float) – Firing intensity between 0 and 1 Returns:tuple – rgb color

mapsList = {'jet': <function ColorMap.jetColor at 0x000002497C725400>}

View

The view consists of classes that are responsible for generating UI. Currently, the only view that is available is the Blender Panel. It consists of the MainPanel class and helper classes.

Blender Panel

class neuron_visualization_addon.__init__.MainPanel(*args, **kwargs)

Main panel that the user sees

bl_idname = 'MainPanel'

bl_label = 'Network Visualization'

bl_region_type = 'TOOL_PROPS'

bl_space_type = 'VIEW_3D'

draw(context)

class neuron_visualization_addon.__init__.PanelSettings(*args, **kwargs)

Class of panel elements

Parameters:

• networkFileUpload – string filepath to model or animation file
• cameraRotation – boolean that indicates whether camera should be rotated
• populationsDropdown – dropdown of population IDs
• colorMapDropdown – dropdown of available color maps
• parser – pointer to the parser instance
• pullProjections – int that indicates whether the projections between populations should be pulled

animate

Used by autodoc_mock_imports.

animateAxons

Used by autodoc_mock_imports.

cameraRotation

Used by autodoc_mock_imports.

cameraSpeed

Used by autodoc_mock_imports.

colorMapDropdown

Used by autodoc_mock_imports.

modelScale

Used by autodoc_mock_imports.

networkFileUpload

Used by autodoc_mock_imports.

parser = <neuron_visualization_addon.controller.Parser.Parser object>

populationsDropdown

Used by autodoc_mock_imports.

pullProjections

Used by autodoc_mock_imports.

class neuron_visualization_addon.__init__.ParseOperator(*args, **kwargs)

Operator that parses the file into a model or animation

bl_idname = 'wm.parser'

bl_label = 'Parse'

execute(context)

updateDropdowns(parser)

class neuron_visualization_addon.__init__.ClearOperator(*args, **kwargs)

Operator that clears the objects from the scene

bl_idname = 'wm.clear'

bl_label = 'Clear All'

execute(context)

Controller

The controller is responsible for changing the model depending on user input.

Parser

class neuron_visualization_addon.controller.Parser.Parser

Bases: object

This class represents a brain cell in the network

adjustCameraSpeed(velocity=100)

Adjust camera rotation velocity

Parameters:velocity (int) – Rotation speed in frames

colorMaps

List of color maps

highlightPopulation(populationID)

Hightlight a population

Parameters:populationID (string) – population ID which to be highlighted

loadCellsNeuroML2(dirpath, includes, scale)

Load cells from neuroml2

Parameters:

• dirpath (string) – path to include directory
• includes (list) – list of included files
• scale (int) – Scale of a model (DEFAULT: 1:10)

Returns:

dict – dictionary of Cell objects

parse(filepath, scale, colorMap, animateAxons)

Parses nml files

Parameters:

• filepath (string) – file to be parsed
• scale (int) – Scale of a model (DEFAULT: 1:10)
• colorMap (String) – Color map for animation

Todo:

Include support for more description languages

Returns:

string – indicate what was parsed

populations

List of IDs of all populations

pullProjections(strength)

Pull projections between populations into a “sand-clock” form

Parameters:strength (int) – Pulling strength

rotateCamera()

Rotate camera around the network