From e7eda838a1f81426655d074b0b456740505ca72b Mon Sep 17 00:00:00 2001 From: Harsha Rani <hrani@ncbs.res.in> Date: Thu, 17 Nov 2016 16:15:28 +0530 Subject: [PATCH] duplicate rdesigneur folder exists one level above --- docs/user/py/rdesigneur/index.rst | 763 ------------------------------ 1 file changed, 763 deletions(-) delete mode 100644 docs/user/py/rdesigneur/index.rst diff --git a/docs/user/py/rdesigneur/index.rst b/docs/user/py/rdesigneur/index.rst deleted file mode 100644 index 796bfddd..00000000 --- a/docs/user/py/rdesigneur/index.rst +++ /dev/null @@ -1,763 +0,0 @@ -Rdesigneur: Building multiscale models -====================================== - -:Date: 2015-12-28 -:Authors: - - Upi Bhalla - -Introduction ------------- - -**Rdesigneur** (Reaction Diffusion and Electrical SIGnaling in NEURons) -is an interface to the multiscale modeling capabilities in MOOSE. It is -designed to build models incorporating biochemical signaling pathways in -dendrites and spines, coupled to electrical events in neurons. -Rdesigneur assembles models from predefined parts: it delegates the -details to specialized model definition formats. Rdesigneur combines one -or more of the following cell parts to build models: - -- Neuronal morphology -- Dendritic spines -- Ion channels -- Reaction systems - -Rdesigneur's main role is to specify how these are put together, -including assigning parameters to do so. Rdesigneur also helps with -setting up the simulation input and output. - -Quick Start ------------ - -Here we provide a few use cases, building up from a minimal model to a -reasonably complete multiscale model spanning chemical and electrical -signaling. - -Bare Rdesigneur: single passive compartment -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -If we don't provide any arguments at all to the Rdesigneur, it makes a -model with a single passive electrical compartment in the MOOSE path -``/model/elec/soma``. Here is how to do this: - -:: - - import moose - import rdesigneur as rd - rdes = rd.rdesigneur() - rdes.buildModel() - -To confirm that it has made a compartment with some default values we -can add a line: - -:: - - moose.showfields( rdes.soma ) - -This should produce the output: - -:: - - [ /model[0]/elec[0]/soma[0] ] - diameter = 0.0005 - fieldIndex = 0 - Ra = 7639437.26841 - y0 = 0.0 - Rm = 424413.177334 - index = 0 - numData = 1 - inject = 0.0 - initVm = -0.065 - Em = -0.0544 - y = 0.0 - numField = 1 - path = /model[0]/elec[0]/soma[0] - dt = 0.0 - tick = -2 - z0 = 0.0 - name = soma - Cm = 7.85398163398e-09 - x0 = 0.0 - Vm = -0.06 - className = ZombieCompartment - idValue = 465 - length = 0.0005 - Im = 1.3194689277e-08 - x = 0.0005 - z = 0.0 - -Simulate and display current pulse to soma -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -A more useful script would run and display the model. Rdesigneur can -help with the stimulus and the plotting. This simulation has the same -passive compartment, and current is injected as the simulation runs. -This script displays the membrane potential of the soma as it charges -and discharges. - -:: - - import moose - import rdesigneur as rd - rdes = rd.rdesigneur( - stimList = [['soma', '1', 'inject', '(t>0.1 && t<0.2) * 2e-8']], - plotList = [['soma', '1', 'Vm', 'Soma membrane potential']], - ) - rdes.buildModel() - moose.reinit() - moose.start( 0.3 ) - rdes.display() - -The *stimList* defines a stimulus. Each entry has four arguments: - -:: - - `[region_in_cell, region_expression, parameter, expression_string]` - -- ``region_in_cell`` specifies the objects to stimulate. Here it is - just the soma. -- ``region_expression`` specifies a geometry based calculation to - decide whether to apply the stimulus. The value must be >0 for the - stimulus to be present. Here it is just 1. -- ``parameter`` specifies the simulation parameter to assign. Here it - is the injection current to the compartment. -- ``expression_string`` calculates the value of the parameter, - typically as a function of time. Here we use the function sign(x), - where sign(x) == +1 for x > 0, 0 for x = 0 and -1 for x < 0. - -To summarise this, the *stimList* here means *inject a current of 20nA -to the soma between the times of 0.1 and 0.2 s*. - -The *plotList* defines what to plot. It has a similar set of arguments: - -:: - - `[region_in_cell, region_expression, parameter, title_of_plot]` - -These mean the same thing as for the stimList except for the title of -the plot. - -The *rdes.display()* function causes the plots to be displayed. - -.. figure:: ../../images/rdes2_passive_squid.png - :alt: Plot for current input to passive compartment - - Plot for current input to passive compartment -When we run this we see an initial depolarization as the soma settles -from its initial -65 mV to a resting Em = -54.4 mV. These are the -original HH values, see the example above. At t = 0.1 seconds there is -another depolarization due to the current injection, and at t = 0.2 -seconds this goes back to the resting potential. - -HH Squid model in a single compartment -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Here we put the Hodgkin-Huxley squid model channels into a passive -compartment. The HH channels are predefined as prototype channels for -Rdesigneur, - -:: - - import moose - import pylab - import rdesigneur as rd - rdes = rd.rdesigneur( - chanProto = [['make_HH_Na()', 'Na'], ['make_HH_K()', 'K']], - chanDistrib = [ - ['Na', 'soma', 'Gbar', '1200' ], - ['K', 'soma', 'Gbar', '360' ]], - stimList = [['soma', '1', 'inject', '(t>0.1 && t<0.2) * 1e-8' ]], - plotList = [['soma', '1', 'Vm', 'Membrane potential']] - ) - - rdes.buildModel() - moose.reinit() - moose.start( 0.3 ) - rdes.display() - -Here we introduce two new model specification lines: - -- **chanProto**: This specifies which ion channels will be used in the - model. Each entry here has two fields: the source of the channel - definition, and (optionally) the name of the channel. In this example - we specify two channels, an Na and a K channel using the original - Hodgkin-Huxley parameters. As the source of the channel definition we - use the name of the Python function that builds the channel. The - *make\_HH\_Na()* and *make\_HH\_K()* functions are predefined but we - can also specify our own functions for making prototypes. We could - also have specified the channel prototype using the name of a channel - definition file in ChannelML (a subset of NeuroML) format. -- **chanDistrib**: This specifies *where* the channels should be placed - over the geometry of the cell. Each entry in the chanDistrib list - specifies the distribution of parameters for one channel using four - entries: - - ``[object_name, region_in_cell, parameter, expression_string]`` - - In this case the job is almost trivial, since we just have a single - compartment named *soma*. So the line - - ``['Na', 'soma', 'Gbar', '1200' ]`` - - means *Put the Na channel in the soma, and set its maximal - conductance density (Gbar) to 1200 Siemens/m^2*. - -As before we apply a somatic current pulse. Since we now have HH -channels in the model, this generates action potentials. - -.. figure:: ../../images/rdes3_squid.png - :alt: Plot for HH squid simulation - - Plot for HH squid simulation -Reaction system in a single compartment -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Here we use the compartment as a place in which to embed a chemical -model. The chemical oscillator model is predefined in the rdesigneur -prototypes. - -:: - - import moose - import pylab - import rdesigneur as rd - rdes = rd.rdesigneur( - turnOffElec = True, - diffusionLength = 1e-3, # Default diffusion length is 2 microns - chemProto = [['make_Chem_Oscillator()', 'osc']], - chemDistrib = [['osc', 'soma', 'install', '1' ]], - plotList = [['soma', '1', 'dend/a', 'conc', 'a Conc'], - ['soma', '1', 'dend/b', 'conc', 'b Conc']] - ) - rdes.buildModel() - b = moose.element( '/model/chem/dend/b' ) - b.concInit *= 5 - moose.reinit() - moose.start( 200 ) - - rdes.display() - -In this special case we set the turnOffElec flag to True, so that -Rdesigneur only sets up chemical and not electrical calculations. This -makes the calculations much faster, since we disable electrical -calculations and delink chemical calculations from them. - -We also have a line which sets the ``diffusionLength`` to 1 mm, so that -it is bigger than the 0.5 mm squid axon segment in the default -compartment. If you don't do this the system will subdivide the -compartment into 2 micron voxels for the purposes of putting in a -reaction-diffusion system, which we discuss below. - -There are a couple of lines to change the initial concentration of the -molecular pool b. It is scaled up 5x to give rise to slowly decaying -oscillations. - -.. figure:: ../../images/rdes4_osc.png - :alt: Plot for single-compartment reaction simulation - - Plot for single-compartment reaction simulation -Reaction-diffusion system -~~~~~~~~~~~~~~~~~~~~~~~~~ - -In order to see what a reaction-diffusion system looks like, delete the -``diffusionLength`` expression in the previous example and add a couple -of lines to set up 3-D graphics for the reaction-diffusion product: - -:: - - import moose - import pylab - import rdesigneur as rd - rdes = rd.rdesigneur( - turnOffElec = True, - chemProto = [['make_Chem_Oscillator()', 'osc']], - chemDistrib = [['osc', 'soma', 'install', '1' ]], - plotList = [['soma', '1', 'dend/a', 'conc', 'Concentration of a'], - ['soma', '1', 'dend/b', 'conc', 'Concentration of b']], - moogList = [['soma', '1', 'dend/a', 'conc', 'a Conc', 0, 360 ]] - ) - - rdes.buildModel() - bv = moose.vec( '/model/chem/dend/b' ) - bv[0].concInit *= 2 - bv[-1].concInit *= 2 - moose.reinit() - - rdes.displayMoogli( 1, 400, 0.001 ) - -This is the line we deleted. - -:: - - `diffusionLength = 1e-3,` - -With this change we permit *rdesigneur* to use the default diffusion -length of 2 microns. The 500-micron axon segment is now subdivided into -250 voxels, each of which has a reaction system and diffusing molecules. -To make it more picturesque, we have added a line after the plotList, to -display the outcome in 3-D: - -:: - - 'moogList = [['soma', '1', 'dend/a', 'conc', 'a Conc', 0, 360 ]]' - -This line says: take the model compartments defined by ``soma`` as the -region to display, do so throughout the the geometry (the ``1`` -signifies this), and over this range find the chemical entity defined by -``dend/a``. For each ``a`` molecule, find the ``conc`` and dsiplay it. -There are two optional arguments, ``0`` and ``360``, which specify the -low and high value of the displayed variable. - -In order to initially break the symmetry of the system, we change the -initial concentration of molecule b at each end of the cylinder: - -:: - - bv[0].concInit *= 2 - bv[-1].concInit *= 2 - -If we didn't do this the entire system would go through a few cycles of -decaying oscillation and then reach a boring, spatially uniform, steady -state. Try putting an initial symmetry break elsewhere to see what -happens. - -To display the concenctration changes in the 3-D soma as the simulation -runs, we use the line - -:: - - `rdes.displayMoogli( 1, 400, 0.001 )` - -The arguments mean: *displayMoogli( frametime, runtime, rotation )* -Here, - -:: - - frametime = time by which simulation advances between display updates - runtime = Total simulated time - rotation = angle by which display rotates in each frame, in radians. - -When we run this, we first get a 3-D display with the oscillating -reaction-diffusion system making its way inward from the two ends. After -the simulation ends the plots for all compartments for the whole run -come up. - -.. figure:: ../../images/rdes5_reacdiff.png - :alt: Display for oscillatory reaction-diffusion simulation - - Display for oscillatory reaction-diffusion simulation -Make a toy multiscale model with electrical and chemical signaling. -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Now we put together chemical and electrical models. In this toy model we -have an HH-squid type single compartment electrical model, cohabiting -with a chemical oscillator. The chemical oscillator regulates K+ channel -amounts, and the average membrane potential regulates the amounts of a -reactant in the chemical oscillator. This is a recipe for some strange -firing patterns. - -:: - - import moose - import pylab - import rdesigneur as rd - rdes = rd.rdesigneur( - # We want just one compartment so we set diffusion length to be - # bigger than the 0.5 mm HH axon compartment default. - diffusionLength = 1e-3, - chanProto = [['make_HH_Na()', 'Na'], ['make_HH_K()', 'K']], - chanDistrib = [ - ['Na', 'soma', 'Gbar', '1200' ], - ['K', 'soma', 'Gbar', '360' ]], - chemProto = [['make_Chem_Oscillator()', 'osc']], - chemDistrib = [['osc', 'soma', 'install', '1' ]], - # These adaptor parameters give interesting-looking but - # not particularly physiological behaviour. - adaptorList = [ - [ 'dend/a', 'conc', 'Na', 'modulation', 1, -5.0 ], - [ 'dend/b', 'conc', 'K', 'modulation', 1, -0.2], - [ 'dend/b', 'conc', '.', 'inject', -1.0e-7, 4e-7 ], - [ '.', 'Vm', 'dend/s', 'conc', 2.5, 20.0 ] - ], - plotList = [['soma', '1', 'dend/a', 'conc', 'a Conc'], - ['soma', '1', 'dend/b', 'conc', 'b Conc'], - ['soma', '1', 'dend/s', 'conc', 's Conc'], - ['soma', '1', 'Na', 'Gk', 'Na Gk'], - ['soma', '1', '.', 'Vm', 'Membrane potential'] - ] - ) - - rdes.buildModel() - moose.reinit() - moose.start( 250 ) # Takes a few seconds to run this. - - rdes.display() - -We've already modeled the HH squid model and the oscillator -individually, and you should recognize the parts of those models above. -The new section that makes this work the *adaptorList* which specifies -how the electrical and chemical parts talk to each other. This entirely -fictional set of interactions goes like this: - -:: - - [ 'dend/a', 'conc', 'Na', 'modulation', 1, -5.0 ] - -- *dend/a*: The originating variable comes from the 'a' pool on the - 'dend' compartment. - - *conc*: This is the originating variable name on the 'a' pool. - - *Na*: This is the target variable - - *modulation*: scale the Gbar of Na up and down. Use 'modulation' - rather than direct assignment of Gbar since Gbar is different for - each differently-sized compartment. - - *1*: This is the initial offset - - *-5.0*: This is the scaling from the input to the parameter updated - in the simulation. - -A similar set of adaptor entries couple the molecule *dend/b* to the K -channel, *dend/b* again to the current injection into the soma, and the -membrane potential to the concentration of *dend/s*. - -.. figure:: ../../images/rdes6_multiscale.png - :alt: Plot for toy multiscale model - - Plot for toy multiscale model -Morphology: Load .swc morphology file and view it -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Here we build a passive model using a morphology file in the .swc file -format (as used by NeuroMorpho.org). The morphology file is predefined -for Rdesigneur and resides in the directory ``./cells``. We apply a -somatic current pulse, and view the somatic membrane potential in a -plot, as before. To make things interesting we display the morphology in -3-D upon which we represent the membrane potential as colors. - -:: - - import moose - import rdesigneur as rd - rdes = rd.rdesigneur( - cellProto = [['./cells/h10.CNG.swc', 'elec']], - stimList = [['soma', '1', '.', 'inject', 't * 25e-9' ]], - plotList = [['#', '1', '.', 'Vm', 'Membrane potential'], - ['#', '1', 'Ca_conc', 'Ca', 'Ca conc (uM)']], - moogList = [['#', '1', '.', 'Vm', 'Soma potential']] - ) - - rdes.buildModel() - - moose.reinit() - rdes.displayMoogli( 0.0002, 0.1 ) - -Here the new concept is the cellProto line, which loads in the specified -cell model: - -:: - - `[ filename, cellname ]` - -The system recognizes the filename extension and builds a model from the -swc file. It uses the cellname **elec** in this example. - -We use a similar line as in the reaction-diffusion example, to build up -a Moogli display of the cell model: - -:: - - `moogList = [['#', '1', '.', 'Vm', 'Soma potential']]` - -Here we have: - -:: - - *#*: the path to use for selecting the compartments to display. - This wildcard means use all compartments. - *1*: The expression to use for the compartments. Again, `1` means use - all of them. - *.*: Which object in the compartment to display. Here we are using the - compartment itself, so it is just a dot. - *Vm*: Field to display - *Soma potential*: Title for display. - -.. figure:: ../../images/rdes7_passive.png - :alt: 3-D display for passive neuron - - 3-D display for passive neuron -Build an active neuron model by putting channels into a morphology file -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -We load in a morphology file and distribute voltage-gated ion channels -over the neuron. Here the voltage-gated channels are obtained from a -number of channelML files, located in the ``./channels`` subdirectory. -Since we have a spatially extended neuron, we need to specify the -spatial distribution of channel densities too. - -:: - - import moose - import rdesigneur as rd - rdes = rd.rdesigneur( - chanProto = [ - ['./chans/hd.xml'], - ['./chans/kap.xml'], - ['./chans/kad.xml'], - ['./chans/kdr.xml'], - ['./chans/na3.xml'], - ['./chans/nax.xml'], - ['./chans/CaConc.xml'], - ['./chans/Ca.xml'] - ], - cellProto = [['./cells/h10.CNG.swc', 'elec']], - chanDistrib = [ \ - ["hd", "#dend#,#apical#", "Gbar", "50e-2*(1+(p*3e4))" ], - ["kdr", "#", "Gbar", "p < 50e-6 ? 500 : 100" ], - ["na3", "#soma#,#dend#,#apical#", "Gbar", "850" ], - ["nax", "#soma#,#axon#", "Gbar", "1250" ], - ["kap", "#axon#,#soma#", "Gbar", "300" ], - ["kap", "#dend#,#apical#", "Gbar", - "300*(H(100-p*1e6)) * (1+(p*1e4))" ], - ["Ca_conc", "#", "tau", "0.0133" ], - ["kad", "#soma#,#dend#,#apical#", "Gbar", "50" ], - ["Ca", "#", "Gbar", "50" ] - ], - stimList = [['soma', '1', '.', 'inject', '(t>0.02) * 1e-9' ]], - plotList = [['#', '1', '.', 'Vm', 'Membrane potential'], - ['#', '1', 'Ca_conc', 'Ca', 'Ca conc (uM)']], - moogList = [['#', '1', 'Ca_conc', 'Ca', 'Calcium conc (uM)', 0, 120], - ['#', '1', '.', 'Vm', 'Soma potential']] - ) - - rdes.buildModel() - - moose.reinit() - rdes.displayMoogli( 0.0002, 0.052 ) - -Here we make more extensive use of two concepts which we've already seen -from the single compartment squid model: - -1. *chanProto*: This defines numerous channels, each of which is of the - form: - - ``[ filename ]`` - - or - - ``[ filename, channelname ]`` - -If the *channelname* is not specified the system uses the last part of -the channel name, before the filetype suffix. - -2. *chanDistrib*: This defines the spatial distribution of each channel - type. Each line is of a form that should be familiar now: - - ``[channelname, region_in_cell, parameter, expression_string]`` - -- The *channelname* is the name of the prototype from *chanproto*. This - is usually an ion channel, but in the example above you can also see - a calcium concentration pool defined. -- The *region\_in\_cell* is typically defined using wildcards, so that - it generalizes to any cell morphology. For example, the plain - wildcard ``#`` means to consider all cell compartments. The wildcard - ``#dend#`` means to consider all compartments with the string - ``dend`` somewhere in the name. Wildcards can be comma-separated, so - ``#soma#,#dend#`` means consider all compartments with either soma or - dend in their name. The naming in MOOSE is defined by the model file. - Importantly, in **.swc** files MOOSE generates names that respect the - classification of compartments into axon, soma, dendrite, and apical - dendrite compartments respectively. SWC files generate compartment - names such as: - - :: - - soma_<number> - dend_<number> - apical_<number> - axon_<number> - -where the number is automatically assigned by the reader. In order to -select all dendritic compartments, for example, one would use *"#dend#"* -where the *"#"* acts as a wildcard to accept any string. - The -*parameter* is usually Gbar, the channel conductance density in *S/m^2*. -If *Gbar* is zero or less, then the system economizes by not -incorporating this channel mechanism in this part of the cell. -Similarly, for calcium pools, if the *tau* is below zero then the -calcium pool object is simply not inserted into this part of the cell. - -The *expression\_string* defines the value of the parameter, such as -Gbar. This is typically a function of position in the cell. The -expression evaluator knows about several parameters of cell geometry. -All units are in metres: - -- *x*, *y* and *z* coordinates. -- *g*, the geometrical distance from the soma -- *p*, the path length from the soma, measured along the dendrites. -- *dia*, the diameter of the dendrite. -- *L*, The electrotonic length from the soma (no units). - -Along with these geometrical arguments, we make liberal use of the -Heaviside function H(x) to set up the channel distributions. The -expression evaluator also knows about pretty much all common algebraic, -trignometric, and logarithmic functions, should you wish to use these. - -Also note the two Moogli displays. The first is the calcium -concentration. The second is the membrane potential in each compartment. -Easy! - -.. figure:: ../../images/rdes8_active.png - :alt: 3-D display for active neuron - - 3-D display for active neuron -Build a spiny neuron from a morphology file and put active channels in it. -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -This model is one step elaborated from the previous one, in that we now -also have dendritic spines. MOOSE lets one decorate a bare neuronal -morphology file with dendritic spines, specifying various geometric -parameters of their location. As before, we use an swc file for the -morphology, and the same ion channels and distribution. - -:: - - import moose - import pylab - import rdesigneur as rd - rdes = rd.rdesigneur( - chanProto = [ - ['./chans/hd.xml'], - ['./chans/kap.xml'], - ['./chans/kad.xml'], - ['./chans/kdr.xml'], - ['./chans/na3.xml'], - ['./chans/nax.xml'], - ['./chans/CaConc.xml'], - ['./chans/Ca.xml'] - ], - cellProto = [['./cells/h10.CNG.swc', 'elec']], - spineProto = [['make_active_spine()', 'spine']], - chanDistrib = [ - ["hd", "#dend#,#apical#", "Gbar", "50e-2*(1+(p*3e4))" ], - ["kdr", "#", "Gbar", "p < 50e-6 ? 500 : 100" ], - ["na3", "#soma#,#dend#,#apical#", "Gbar", "850" ], - ["nax", "#soma#,#axon#", "Gbar", "1250" ], - ["kap", "#axon#,#soma#", "Gbar", "300" ], - ["kap", "#dend#,#apical#", "Gbar", - "300*(H(100-p*1e6)) * (1+(p*1e4))" ], - ["Ca_conc", "#", "tau", "0.0133" ], - ["kad", "#soma#,#dend#,#apical#", "Gbar", "50" ], - ["Ca", "#", "Gbar", "50" ] - ], - spineDistrib = [['spine', '#dend#,#apical#', '20e-6', '1e-6']], - stimList = [['soma', '1', '.', 'inject', '(t>0.02) * 1e-9' ]], - plotList = [['#', '1', '.', 'Vm', 'Membrane potential'], - ['#', '1', 'Ca_conc', 'Ca', 'Ca conc (uM)']], - moogList = [['#', '1', 'Ca_conc', 'Ca', 'Calcium conc (uM)', 0, 120], - ['#', '1', '.', 'Vm', 'Soma potential']] - ) - - rdes.buildModel() - - moose.reinit() - rdes.displayMoogli( 0.0002, 0.023 ) - -Spines are set up in a familiar way: we first define one (or more) -prototype spines, and then distribute these around the cell. Here is the -prototype string: - -:: - - [spine_proto, spinename] - -*spine\_proto*: This is typically a function. One can define one's own, -but there are several predefined ones in rdesigneur. All these define a -spine with the following parameters: - -- head diameter 0.5 microns -- head length 0.5 microns -- shaft length 1 micron -- shaft diameter of 0.2 microns -- RM = 1.0 ohm-metre square -- RA = 1.0 ohm-meter -- CM = 0.01 Farads per square metre. - -Here are the predefined spine prototypes: - -- *make\_passive\_spine()*: This just makes a passive spine with the - default parameters -- *make\_exc\_spine()*: This makes a spine with NMDA and glu receptors, - and also a calcium pool. The NMDA channel feeds the Ca pool. -- *make\_active\_spine()*: This adds a Ca channel to the exc\_spine. - and also a calcium pool. - -The spine distributions are specified in a familiar way for the first -few arguments, and then there are multiple (optional) spine-specific -parameters: - -*[spinename, region\_in\_cell, spacing, spacing\_distrib, size, -size\_distrib, angle, angle\_distrib ]* - -Only the first two arguments are mandatory. - -- *spinename*: The prototype name -- *region\_in\_cell*: Usual wildcard specification of names of - compartments in which to put the spines. -- *spacing*: Math expression to define spacing between spines. In the - current implementation this evaluates to - ``1/probability_of_spine_per_unit_length``. Defaults to 10 microns. - Thus, there is a 10% probability of a spine insertion in every - micron. This evaluation method has the drawback that it is possible - to space spines rather too close to each other. If spacing is zero or - less, no spines are inserted. -- *spacing\_distrib*: Math expression for distribution of spacing. In - the current implementation, this specifies the interval at which the - system samples from the spacing probability above. Defaults to 1 - micron. -- *size*: Linear scale factor for size of spine. All dimensions are - scaled by this factor. The default spine head here is 0.5 microns in - diameter and length. If the scale factor were to be 2, the volume - would be 8 times as large. Defaults to 1.0. -- *size\_distrib*: Range for size of spine. A random number R is - computed in the range 0 to 1, and the final size used is - ``size + (R - 0.5) * size_distrib``. Defaults to 0.5 -- *angle*: This specifies the initial angle at which the spine sticks - out of the dendrite. If all angles were zero, they would all point - away from the soma. Defaults to 0 radians. -- *angle\_distrib*: Specifies a random number to add to the initial - angle. Defaults to 2 PI radians, so the spines come out in any - direction. - -One may well ask why we are not using a Python dictionary to handle all -these parameters. Short answer is: terseness. Longer answer is that the -rdesigneur format is itself meant to be an intermediate form for an -eventual high-level, possibly XML-based multiscale modeling format. - -.. figure:: ../../images/rdes9_spiny_active.png - :alt: 3-D display for spiny active neuron - - 3-D display for spiny active neuron -Build a spiny neuron from a morphology file and put a reaction-diffusion system in it. -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Rdesigneur is specially designed to take reaction systems with a -dendrite, a spine head, and a spine PSD compartment, and embed these -systems into neuronal morphologies. This example shows how this is done. - -The dendritic molecules diffuse along the dendrite in the region -specified by the *chemDistrib* keyword. In this case they are placed on -all apical and basal dendrites, but only at distances over 500 microns -from the soma. The spine head and PSD reaction systems are inserted only -into spines within this same *chemDistrib* zone. Diffusion coupling -between dendrite, and each spine head and PSD is also set up. It takes a -predefined chemical model file for Rdesigneur, which resides in the -``./chem`` subdirectory. As in an earlier example, we turn off the -electrical calculations here as they are not needed. Here we plot out -the number of receptors on every single spine as a function of time. - -.. todo:: (stuff here) - -Make a full multiscale model with complex spiny morphology and electrical and chemical signaling. -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. todo:: (stuff here) -- GitLab