#########################################################################
## This program is part of 'MOOSE', the
## Messaging Object Oriented Simulation Environment.
## Copyright (C) 2015 Upinder S. Bhalla. and NCBS
## It is made available under the terms of the
## GNU Lesser General Public License version 2.1
## See the file COPYING.LIB for the full notice.
#########################################################################
import math
import time
import pylab
import numpy
import matplotlib.pyplot as plt
import moose
diffConst = 1e-11
chemdt = 0.001 # Tested various dts, this is reasonable.
diffdt = 0.001
plotdt = 0.01
animationdt = 0.01
runtime = 1
useGssa = True
def makeModel():
model = moose.Neutral( '/model' )
# Make neuronal model. It has no channels, just for geometry
cell = moose.loadModel( './spinyNeuron.p', '/model/cell', 'Neutral' )
# We don't want the cell to do any calculations. Disable everything.
for i in moose.wildcardFind( '/model/cell/##' ):
i.tick = -1
# create container for model
model = moose.element( '/model' )
chem = moose.Neutral( '/model/chem' )
# The naming of the compartments is dicated by the places that the
# chem model expects to be loaded.
compt0 = moose.NeuroMesh( '/model/chem/compt0' )
compt0.separateSpines = 1
compt0.geometryPolicy = 'cylinder'
compt1 = moose.SpineMesh( '/model/chem/compt1' )
moose.connect( compt0, 'spineListOut', compt1, 'spineList', 'OneToOne' )
compt2 = moose.PsdMesh( '/model/chem/compt2' )
moose.connect( compt0, 'psdListOut', compt2, 'psdList', 'OneToOne' )
#reacSystem = moose.loadModel( 'simpleOsc.g', '/model/chem', 'ee' )
makeChemModel( compt0, True ) # Populate all 3 compts with the chem system.
makeChemModel( compt1, False )
makeChemModel( compt2, True )
compt0.diffLength = 2e-6 # This will be over 100 compartments.
# This is the magic command that configures the diffusion compartments.
compt0.subTreePath = cell.path + "/#"
moose.showfields( compt0 )
# Build the solvers. No need for diffusion in this version.
ksolve0 = moose.Ksolve( '/model/chem/compt0/ksolve' )
if useGssa:
ksolve1 = moose.Gsolve( '/model/chem/compt1/ksolve' )
ksolve2 = moose.Gsolve( '/model/chem/compt2/ksolve' )
else:
ksolve1 = moose.Ksolve( '/model/chem/compt1/ksolve' )
ksolve2 = moose.Ksolve( '/model/chem/compt2/ksolve' )
dsolve0 = moose.Dsolve( '/model/chem/compt0/dsolve' )
dsolve1 = moose.Dsolve( '/model/chem/compt1/dsolve' )
dsolve2 = moose.Dsolve( '/model/chem/compt2/dsolve' )
stoich0 = moose.Stoich( '/model/chem/compt0/stoich' )
stoich1 = moose.Stoich( '/model/chem/compt1/stoich' )
stoich2 = moose.Stoich( '/model/chem/compt2/stoich' )
# Configure solvers
stoich0.compartment = compt0
stoich1.compartment = compt1
stoich2.compartment = compt2
stoich0.ksolve = ksolve0
stoich1.ksolve = ksolve1
stoich2.ksolve = ksolve2
stoich0.dsolve = dsolve0
stoich1.dsolve = dsolve1
stoich2.dsolve = dsolve2
stoich0.path = '/model/chem/compt0/#'
stoich1.path = '/model/chem/compt1/#'
stoich2.path = '/model/chem/compt2/#'
assert( stoich0.numVarPools == 1 )
assert( stoich0.numProxyPools == 0 )
assert( stoich0.numRates == 1 )
assert( stoich1.numVarPools == 1 )
assert( stoich1.numProxyPools == 0 )
if useGssa:
assert( stoich1.numRates == 2 )
assert( stoich2.numRates == 2 )
else:
assert( stoich1.numRates == 1 )
assert( stoich2.numRates == 1 )
assert( stoich2.numVarPools == 1 )
assert( stoich2.numProxyPools == 0 )
dsolve0.buildNeuroMeshJunctions( dsolve1, dsolve2 )
stoich0.buildXreacs( stoich1 )
stoich1.buildXreacs( stoich2 )
stoich0.filterXreacs()
stoich1.filterXreacs()
stoich2.filterXreacs()
Ca_input_dend = moose.vec( '/model/chem/compt0/Ca_input' )
print(len( Ca_input_dend ))
for i in range( 60 ):
Ca_input_dend[ 3 + i * 3 ].conc = 2.0
Ca_input_PSD = moose.vec( '/model/chem/compt2/Ca_input' )
print((len( Ca_input_PSD )))
for i in range( 5 ):
Ca_input_PSD[ 2 + i * 2].conc = 1.0
# Create the output tables
num = compt0.numDiffCompts - 1
graphs = moose.Neutral( '/model/graphs' )
makeTab( 'Ca_soma', '/model/chem/compt0/Ca[0]' )
makeTab( 'Ca_d1', '/model/chem/compt0/Ca[1]' )
makeTab( 'Ca_d2', '/model/chem/compt0/Ca[2]' )
makeTab( 'Ca_d3', '/model/chem/compt0/Ca[3]' )
makeTab( 'Ca_s3', '/model/chem/compt1/Ca[3]' )
makeTab( 'Ca_s4', '/model/chem/compt1/Ca[4]' )
makeTab( 'Ca_s5', '/model/chem/compt1/Ca[5]' )
makeTab( 'Ca_p3', '/model/chem/compt2/Ca[3]' )
makeTab( 'Ca_p4', '/model/chem/compt2/Ca[4]' )
makeTab( 'Ca_p5', '/model/chem/compt2/Ca[5]' )
def makeTab( plotname, molpath ):
tab = moose.Table2( '/model/graphs/' + plotname ) # Make output table
# connect up the tables
moose.connect( tab, 'requestOut', moose.element( molpath ), 'getConc' );
def makeDisplay():
plt.ion()
fig = plt.figure( figsize=(10,12) )
dend = fig.add_subplot( 411 )
plt.ylabel( 'Conc (mM)' )
plt.xlabel( 'Dend voxel #' )
plt.legend()
timeLabel = plt.text(200, 0.5, 'time = 0')
spine = fig.add_subplot( 412 )
plt.ylabel( 'Conc (mM)' )
plt.xlabel( 'Spine voxel #' )
plt.legend()
psd = fig.add_subplot( 413 )
plt.ylabel( 'Conc (mM)' )
plt.xlabel( 'PSD voxel #' )
plt.legend()
timeSeries = fig.add_subplot( 414 )
timeSeries.set_ylim( 0, 2 )
plt.ylabel( 'Conc (mM)' )
plt.xlabel( 'time (seconds)' )
plt.legend()
Ca = moose.vec( '/model/chem/compt0/Ca' )
Ca_input = moose.vec( '/model/chem/compt0/Ca_input' )
line1, = dend.plot( list(range( len( Ca ))), Ca.conc, label='Ca' )
line2, = dend.plot( list(range( len( Ca_input ))), Ca_input.conc, label='Ca_input' )
dend.set_ylim( 0, 2 )
Ca = moose.vec( '/model/chem/compt1/Ca' )
line3, = spine.plot( list(range( len( Ca ))), Ca.conc, label='Ca' )
spine.set_ylim( 0, 1 )
Ca = moose.vec( '/model/chem/compt2/Ca' )
Ca_input = moose.vec( '/model/chem/compt2/Ca_input' )
line4, = psd.plot( list(range( len( Ca ))), Ca.conc, label='Ca' )
line5, = psd.plot( list(range( len( Ca_input ))), Ca_input.conc, label='Ca_input' )
psd.set_ylim( 0, 1 )
fig.canvas.draw()
return ( timeSeries, dend, spine, psd, fig, line1, line2, line3, line4, line5, timeLabel )
def updateDisplay( plotlist ):
Ca = moose.vec( '/model/chem/compt0/Ca' )
Ca_input = moose.vec( '/model/chem/compt0/Ca_input' )
plotlist[5].set_ydata( Ca.conc )
plotlist[6].set_ydata( Ca_input.conc )
Ca = moose.vec( '/model/chem/compt1/Ca' )
plotlist[7].set_ydata( Ca.conc )
Ca = moose.vec( '/model/chem/compt2/Ca' )
Ca_input = moose.vec( '/model/chem/compt2/Ca_input' )
plotlist[8].set_ydata( Ca.conc )
plotlist[9].set_ydata( Ca_input.conc )
plotlist[4].canvas.draw()
def finalizeDisplay( plotlist, cPlotDt ):
for x in moose.wildcardFind( '/model/graphs/#[ISA=Table2]' ):
pos = numpy.arange( 0, x.vector.size, 1 ) * cPlotDt
line1, = plotlist[0].plot( pos, x.vector, label=x.name )
plotlist[4].canvas.draw()
print( "Hit '0' to exit" )
try:
raw_input()
except NameError as e: #python3
input( )
[docs]def makeChemModel( compt, doInput ):
"""
This function setus up a simple chemical system in which Ca input
comes to the dend and to selected PSDs. There is diffusion between
PSD and spine head, and between dend and spine head.
:: Ca_input ------> Ca // in dend and spine head only.
"""
# create molecules and reactions
Ca = moose.Pool( compt.path + '/Ca' )
Ca.concInit = 0.08*1e-3
Ca.diffConst = diffConst
if doInput:
Ca_input = moose.BufPool( compt.path + '/Ca_input' )
Ca_input.concInit = 0.08*1e-3
Ca_input.diffConst = diffConst
rInput = moose.Reac( compt.path + '/rInput' )
moose.connect( rInput, 'sub', Ca, 'reac' )
moose.connect( rInput, 'prd', Ca_input, 'reac' )
rInput.Kf = 100 # 1/sec
rInput.Kb = 100 # 1/sec
else:
Ca_sink = moose.BufPool( compt.path + '/Ca_sink' )
Ca_sink.concInit = 0.08*1e-3
rSink = moose.Reac( compt.path + '/rSink' )
moose.connect( rSink, 'sub', Ca, 'reac' )
moose.connect( rSink, 'prd', Ca_sink, 'reac' )
rSink.Kf = 10 # 1/sec
rSink.Kb = 10 # 1/sec
[docs]def main():
"""
This example illustrates and tests diffusion embedded in
the branching pseudo-1-dimensional geometry of a neuron.
An input pattern of Ca stimulus is applied in a periodic manner both
on the dendrite and on the PSDs of the 13 spines. The Ca levels in
each of the dend, the spine head, and the spine PSD are monitored.
Since the same molecule name is used for Ca in the three compartments,
these are automagially connected up for diffusion. The simulation
shows the outcome of this diffusion.
This example uses an external electrical model file with basal
dendrite and three branches on
the apical dendrite. One of those branches has the 13 spines.
The model is set up to run using the Ksolve for integration and the
Dsolve for handling diffusion.
The timesteps here are not the defaults. It turns out that the
chem reactions and diffusion in this example are sufficiently fast
that the chemDt has to be smaller than default. Note that this example
uses rates quite close to those used in production models.
The display has four parts:
a. animated line plot of concentration against main compartment#.
b. animated line plot of concentration against spine compartment#.
c. animated line plot of concentration against psd compartment#.
d. time-series plot that appears after the simulation has
ended.
"""
makeModel()
plotlist = makeDisplay()
# Schedule the whole lot - autoscheduling already does this.
for i in range( 11, 17 ):
moose.setClock( i, chemdt ) # for the chem objects
moose.setClock( 10, diffdt ) # for the diffusion
moose.setClock( 18, plotdt ) # for the output tables.
moose.reinit()
t1 = time.time( )
for i in numpy.arange( 0, runtime, animationdt ):
moose.start( animationdt )
plotlist[10].set_text( "time = %d" % i )
updateDisplay( plotlist )
print( 'Total time taken %g' % ( time.time() - t1 ) )
finalizeDisplay( plotlist, plotdt )
# Run the 'main' if this script is executed standalone.
if __name__ == '__main__':
main()