def make_NMDA( name ):
if moose.exists( '/library/' + name ):
return
NMDA = moose.NMDAChan( '/library/' + name )
NMDA.Ek = 0.0
NMDA.tau1 = 20.0e-3
NMDA.tau2 = 20.0e-3
NMDA.Gbar = 5 * SOMA_A
NMDA.CMg = 1.2 # [Mg]ext in mM
NMDA.KMg_A = 1.0/0.28
NMDA.KMg_B = 1.0/62
NMDA.temperature = 300 # Temperature in Kelvin.
NMDA.extCa = 1.5 # [Ca]ext in mM
NMDA.intCa = 0.00008 # [Ca]int in mM
NMDA.intCaScale = 1 # Scale factor from elec Ca units to mM
NMDA.intCaOffset = 0.00008 # Basal [Ca]int in mM
NMDA.condFraction = 0.02 # Fraction of conductance due to Ca
addmsg1 = moose.Mstring( NMDA.path + '/addmsg1' )
addmsg1.value = '. ICaOut ../Ca_conc current'
addmsg2 = moose.Mstring( NMDA.path + '/addmsg2' )
addmsg2.value = '../Ca_conc concOut . assignIntCa'
sh = moose.SimpleSynHandler( NMDA.path + '/sh' )
moose.connect( sh, 'activationOut', NMDA, 'activation' )
sh.numSynapses = 1
sh.synapse[0].weight = 1
return NMDA
def transformNMDAR( path ):
for i in moose.wildcardFind( path + "/##/#NMDA#[ISA!=NMDAChan]" ):
chanpath = i.path
pa = i.parent
i.name = '_temp'
if ( chanpath[-3:] == "[0]" ):
chanpath = chanpath[:-3]
nmdar = moose.NMDAChan( chanpath )
sh = moose.SimpleSynHandler( chanpath + '/sh' )
moose.connect( sh, 'activationOut', nmdar, 'activation' )
sh.numSynapses = 1
sh.synapse[0].weight = 1
nmdar.Ek = i.Ek
nmdar.tau1 = i.tau1
nmdar.tau2 = i.tau2
nmdar.Gbar = i.Gbar
nmdar.CMg = 12
nmdar.KMg_A = 1.0 / 0.28
nmdar.KMg_B = 1.0 / 62
nmdar.temperature = 300
nmdar.extCa = 1.5
nmdar.intCa = 0.00008
nmdar.intCaScale = 1
nmdar.intCaOffset = 0.00008
nmdar.condFraction = 0.02
moose.delete( i )
moose.connect( pa, 'channel', nmdar, 'channel' )
caconc = moose.wildcardFind( pa.path + '/#[ISA=CaConcBase]' )
if ( len( caconc ) < 1 ):
print('no caconcs found on ', pa.path)
else:
moose.connect( nmdar, 'ICaOut', caconc[0], 'current' )
moose.connect( caconc[0], 'concOut', nmdar, 'assignIntCa' )
def create_synaptic_channel(name,
Gbar,
tau1,
tau2,
ek,
synapse_count,
delay,
params=None):
lib = moose.Neutral('/library') if not moose.exists(
'/library') else moose.element('/library')
if name == 'nmda':
synchan = moose.NMDAChan(lib.path + '/' + name)
try:
synchan = set_nmda_magnesium_parameters(synchan, params)
synchan = set_ghk_equation_factors(synchan, params)
except:
raise ValueError("No NMDA params provided")
else:
synchan = moose.SynChan(lib.path + '/' + name)
synchan.Gbar = Gbar
synchan.tau1 = tau1
synchan.tau2 = tau2
synchan.Ek = ek
sh = create_synaptic_handle(synchan, synapse_count, delay)
moose.connect(sh, 'activationOut', synchan, 'activation')
return (synchan, sh)
def testNMDAChan(simtime=1.0, simdt=1e-5, plotdt=1e-5):
context = moose.PyMooseBase.getContext()
container = moose.Neutral('test_NMDA')
soma_b = moose.Compartment('B', container)
soma_b.Rm = 5.3e9 # GM = 2e-5 S/cm^2
soma_b.Cm = 8.4823001646924426e-12 # CM = 0.9 uF/cm^2
soma_b.Em = -65e-3
soma_b.initVm = -65e-3
soma_b.Ra = 282942.12 # RA = 250 Ohm-cm
nmda = moose.NMDAChan('nmda', container)
nmda.tau2 = 5e-3
nmda.tau1 = 130e-3
nmda.Gbar = 1e-9
nmda.saturation = 1e10
nmda.connect('channel', soma_b, 'channel')
spikegen = moose.SpikeGen('spike', container)
spikegen.threshold = 0.5
spikegen.connect('event', nmda, 'synapse')
spikegen.refractT = 0.0
nmda.delay[0] = 1e-3
nmda.weight[0] = 1.0
pulsegen = moose.PulseGen('pulse', container)
pulsegen.setCount(3)
pulsegen.level[0] = 1.0
pulsegen.delay[0] = 10e-3
pulsegen.width[0] = 1e-3
pulsegen.level[1] = 1.0
pulsegen.delay[1] = 2e-3
pulsegen.width[1] = 1e-3
pulsegen.delay[2] = 1e9
pulsegen.connect('outputSrc', spikegen, 'Vm')
data = moose.Neutral('data')
vmB = moose.Table('Vm_B', data)
vmB.stepMode = 3
vmB.connect('inputRequest', soma_b, 'Vm')
pulse = moose.Table('pulse', data)
pulse.stepMode = 3
pulse.connect('inputRequest', pulsegen, 'output')
gNMDA = moose.Table('G', data)
gNMDA.stepMode = 3
gNMDA.connect('inputRequest', nmda, 'Gk')
context.setClock(0, simdt)
context.setClock(1, simdt)
context.setClock(2, simdt)
context.setClock(3, plotdt)
context.reset()
context.step(simtime)
# gNMDA.dumpFile('gNMDA.dat', False)
# vmA.dumpFile('Va.dat', False)
# vmB.dumpFile('Vb.dat', False)
ts = np.linspace(0, simtime, len(gNMDA))
pylab.plot(ts, pulse)
pylab.plot(ts, np.asarray(gNMDA) * 1e9, label='gNMDA')
pylab.show()
np.savetxt('../data/two_comp_nmda.plot', np.transpose(np.vstack((ts, vmB, gNMDA))))
def test_synintegration(filename, target_cell, source_type):
sim = Simulation('synintegration')
cell = SpinyStellate(SpinyStellate.prototype,
sim.model.path + '/SpinyStellate')
vm_table = cell.comp[cell.presyn].insertRecorder('Vm', 'Vm', sim.data)
# Create a common spike gen object
sp = moose.SpikeGen('spike', sim.model)
sp.threshold = 0.0
sp.edgeTriggered = 1
sptab = moose.Table('spike', sim.data)
sptab.stepMode = 3
sptab.connect('inputRequest', sp, 'state')
(comp_indices, syntype, gbar, tau1, tau2, Ek, ) = \
get_syninfo(filename, target_cell, source_type)
# Set up the synapses
for ii in range(len(comp_indices)):
print '%d\t%s\t%g\t%g\t%g\t%g' % (comp_indices[ii],
syntype[ii],
gbar[ii],
tau1[ii],
tau2[ii],
Ek[ii])
comp = cell.comp[comp_indices[ii]]
weight = 1.0
if syntype[ii] == 'nmda':
chan = moose.NMDAChan('nmda_from_%s' % (source_type), comp)
chan.MgConc = 1.5
weight = gbar[ii]
else:
chan = moose.SynChan('%s_from_%s' % (syntype[ii], source_type),
comp)
chan.Gbar = gbar[ii]
chan.tau1 = tau1[ii]
chan.tau2 = tau2[ii]
chan.Ek = Ek[ii]
comp.connect('channel', chan, 'channel')
sp.connect('event', chan, 'synapse')
count = chan.numSynapses
if source_type == 'TCR':
chan.delay[count-1] = 1e-3 # thalamocortical delay
else:
chan.delay[count-1] = 0.05e-3 # default delay
chan.weight[count-1] = weight
gktable = moose.Table('%s_%s_%s' % (cell.name, comp.name, chan.name), sim.data)
gktable.stepMode = 3
gktable.connect('inputRequest', chan, 'Gk')
pulsegen = moose.PulseGen('pulse', sim.model)
pulsegen.firstDelay = 3.0
pulsegen.firstLevel = 1.0
pulsegen.firstWidth = 1e-3
pulsegen.trigMode = moose.FREE_RUN
pulsegen.connect('outputSrc', sp, 'Vm')
ptable = moose.Table('pulse', sim.data)
ptable.stepMode = 3
ptable.connect('inputRequest', pulsegen, 'output')
sim.schedule(simdt=1e-6, plotdt=1e-6)
sim.run(10.0)
sim.save_data_h5(filename.replace('network_', 'synintegration_'))
def synintegration2(filename, target_cell):
raise NotImplementedError
syntab = None
netfile = h5.File(filename, 'r')
syntab = netfile['network/synapse'][:]
idx = np.char.startswith(syntab['dest'], target_cell)
syntab = syntab[idx]
sim = Simulation('synintegration')
cell = SpinyStellate(SpinyStellate.prototype,
sim.model.path + '/SpinyStellate')
vm_table = cell.comp[cell.presyn].insertRecorder('Vm', 'Vm', sim.data)
# Create a common spike gen object
sp = moose.SpikeGen('spike', sim.model)
sp.threshold = 0.0
sp.edgeTriggered = 1
sptab = moose.Table('spike', sim.data)
sptab.stepMode = 3
sptab.connect('inputRequest', sp, 'state')
for ii in range(len(syntab)):
source = syntab['source'][ii].partition('/')[0]
dest, compname = syntab['dest'][ii].split('/')
if source == dest:
print 'source = dest', source, filename
continue
comp_no = compname.split('_')[-1]
comp = cell[int(comp_no)]
weight = 1.0
if syntab['type'][ii] == 'nmda':
chan = moose.NMDAChan('nmda_from_%s' %
(source.split('_')[0]), comp)
chan.MgConc = 1.5
weight = syntab['Gbar'][ii]
else:
chan = moose.SynChan('%s_from_%s' %
(syntab['type'][ii], source.split('_')[0]),
comp)
chan.Gbar = syntab['Gbar'][ii]
chan.tau1 = syntab['tau1'][ii]
chan.tau2 = syntab['tau2'][ii]
chan.Ek = syntab['Ek'][ii]
comp.connect('channel', chan, 'channel')
sp.connect('event', chan, 'synapse')
count = chan.numSynapses
if source_type == 'TCR':
chan.delay[count-1] = 1e-3 # thalamocortical delay
else:
chan.delay[count-1] = 0.05e-3 # default delay
chan.weight[count-1] = weight
gktable = moose.Table('%s_%s_%s' % (cell.name, comp.name, chan.name), sim.data)
gktable.stepMode = 3
gktable.connect('inputRequest', chan, 'Gk')
def makeSynapse(sg, comp, syntype, name, Ek, Gbar, tau1, tau2, delay=0.0):
syn = None
if syntype == 'nmda':
syn = moose.NMDAChan(name, comp)
else:
syn = moose.SynChan(name, comp)
syn.Ek = Ek
syn.Gbar = Gbar
syn.tau1 = tau1
syn.tau2 = tau2
comp.connect('channel', syn, 'channel')
sg.threshold = 0.0
sg.absRefract = 0.0
if not sg.connect('event', syn, 'synapse'):
raise Exception('Error creating connection: %s->%s' %
(sg.path, syn.path))
return syn
def make_synchan(model, chanpath, synparams):
# for AMPA or GABA - just make the channel, no connections/messages
log.info('synparams: {} {}', chanpath, synparams)
if synparams.NMDA:
synchan = moose.NMDAChan(chanpath)
else:
synchan = moose.SynChan(chanpath)
synchan.tau1 = synparams.tau1
synchan.tau2 = synparams.tau2
synchan.Ek = synparams.Erev
#for NMDA, assign MgBlock parameters, and then parameters for calcium current using GHK
if synparams.NMDA and synparams.MgBlock:
synchan.KMg_A = synparams.MgBlock.A
synchan.KMg_B = synparams.MgBlock.B
synchan.CMg = synparams.MgBlock.C
if (model.calYN):
synchan.condFraction = synparams.nmdaCaFrac
synchan.temperature = constants.celsius_to_kelvin(model.Temp)
synchan.extCa = model.ConcOut
return synchan
import moose
import numpy as np
from matplotlib import pyplot as plt
from pprint import pprint # allows for one to see nested dictionaries clearer
plt.ion()
nmdachan = moose.NMDAChan('/nmda')
moose.showfield(nmdachan)
nmdachan.KMg_A = 0.17
nmdachan.KMg_B = 0.012
nmdachan.CMg = 1.4
# Function to plot and see magnesium effect on NMDA channel
def magnesium_term(A, B, C):
eta = 1.0 / A
gamma = 1.0 / B
v = np.linspace(-100e-3, 60e-3, 1000) # voltage vector
mg_term = 1 / (1 + eta * C * np.exp(-gamma * v))
plt.plot(v, mg_term)
synparams = {}
mgparams = {'A': (1 / 6.0), 'B': (1 / 80.0), 'conc': 1.4}
synparams['ampa'] = {'Erev': 5e-3, 'tau1': 1.0e-3, 'tau2': 5e-3, 'Gbar': 1e-9}
synparams['nmda'] = {
'Erev': 5e-3,
'tau1': 1.1e-3,
'tau2': 37.5e-3,
'Gbar': 2e-9,
'mgparams': mgparams,
def addNMDA(comp, name):
synchan = moose.NMDAChan(comp.path + "/" + name)
def testNMDAChan(simtime=1.0, simdt=1e-5, plotdt=1e-5):
context = moose.PyMooseBase.getContext()
container = moose.Neutral('test_NMDA')
soma_a = moose.Compartment('A', container)
soma_a.Rm = 5.3e9 # GM = 2e-5 S/cm^2
soma_a.Cm = 8.4823001646924426e-12 # CM = 0.9 uF/cm^2
soma_a.Em = -65e-3
soma_a.initVm = -65e-3
soma_a.Ra = 282942.12
soma_b = moose.Compartment('B', container)
soma_b.Rm = 5.3e9 # GM = 2e-5 S/cm^2
soma_b.Cm = 8.4823001646924426e-12 # CM = 0.9 uF/cm^2
soma_b.Em = -65e-3
soma_b.initVm = -65e-3
soma_b.Ra = 282942.12 # RA = 250 Ohm-cm
nmda = moose.NMDAChan('nmda', container)
nmda.tau2 = 5e-3
nmda.tau1 = 130.5e-3
nmda.Gbar = 1
nmda.saturation = 1e10
nmda.connect('channel', soma_b, 'channel')
spikegen = moose.SpikeGen('spike', container)
spikegen.threshold = 0.0
soma_a.connect('VmSrc', spikegen, 'Vm')
spikegen.connect('event', nmda, 'synapse')
nmda.delay[0] = 0.05e-3
nmda.weight[0] = 0.25e-9
pulsegen = moose.PulseGen('pulse', container)
pulsegen.firstLevel = 0.1e-9
pulsegen.firstDelay = 20e-3
pulsegen.firstWidth = 20e-3
pulsegen.secondDelay = 1e9
pulsegen.connect('outputSrc', soma_a, 'injectMsg')
data = moose.Neutral('data')
vmB = moose.Table('Vm_B', data)
vmB.stepMode = 3
vmB.connect('inputRequest', soma_b, 'Vm')
vmA = moose.Table('Vm_A', data)
vmA.stepMode = 3
vmA.connect('inputRequest', soma_a, 'Vm')
gNMDA = moose.Table('G', data)
gNMDA.stepMode = 3
gNMDA.connect('inputRequest', nmda, 'Gk')
context.setClock(0, simdt)
context.setClock(1, simdt)
context.setClock(2, simdt)
context.setClock(3, plotdt)
context.reset()
context.step(simtime)
# gNMDA.dumpFile('gNMDA.dat', False)
# vmA.dumpFile('Va.dat', False)
# vmB.dumpFile('Vb.dat', False)
ts = np.linspace(0, simtime, len(gNMDA))
np.savetxt('../data/two_comp_nmda.plot',
np.transpose(np.vstack((ts, vmA, vmB, gNMDA))))
