#stim2.number = 1 #stim2.start = 5 * ms #stim2.interval = 0.5 * ms # ## stimulator 2 #stim3 = h.NetStim() #stim3.number = 1 #stim3.start = 5 * ms #stim3.interval = 0.5 * ms # SYNAPSES # set 1 # excitatory synapse on relay cell - technically part of triad but named differently for clarity rc_exc1 = h.AlphaSynapse(relaycell.soma(0.28)) rc_exc1.onset = 5 * ms rc_exc1.tau = 0.2 * ms rc_exc1.gmax = 5 rc_exc1.i = 12 syns.append(rc_exc1) # proximal excitatory axodendritic synapse on interneuron in_exc1 = h.AlphaSynapse(interneuron.dend1_p(0.1)) in_exc1.onset = 5 * ms in_exc1.tau = 0.2 * ms in_exc1.gmax = 5 in_exc1.i = 12 syns.append(in_exc1) # distal excitatory axodendritic synapse on interneuron
from neuron import h, gui
from matplotlib import pyplot
soma = h.Section(name='soma')
h.psection()
soma.insert('pas')
print("type(soma) = {}".format(type(soma)))
print("type(soma(0.5)) ={}".format(type(soma(0.5))))
mech = soma(0.5).pas
print(dir(mech))
asyn = h.AlphaSynapse(soma(0.5))
print("asyn.e = {}".format(asyn.e))
print("asyn.gmax = {}".format(asyn.gmax))
print("asyn.onset = {}".format(asyn.onset))
print("asyn.tau = {}".format(asyn.tau))
asyn.onset = 20
asyn.gmax = 1
h.psection()
v_vec = h.Vector() # Membrane potential vector
t_vec = h.Vector() # Time stamp vector
v_vec.record(soma(0.5)._ref_v)
t_vec.record(h._ref_t)
h.tstop = 40.0
h.run()
#plotting
pyplot.figure(figsize=(8,4)) # Default figsize is (8,6)
pyplot.plot(t_vec, v_vec)
pyplot.xlabel('time (ms)')
pyplot.ylabel('mV')
pyplot.show()
basilar.nseg = 5
basilar.diam = 2
axon.L = 1000
axon.nseg = 37
axon.diam = 1
# biophysics
for sec in h.allsec():
sec.Ra = 100
sec.cm = 1
soma.insert('hh')
apical.insert('pas')
basilar.insert('pas')
for seg in chain(apical, basilar):
seg.pas.g = 0.0002
seg.pas.e = -65
axon.insert('hh')
# --------------------- Instrumentation ---------------------
# synaptic input
syn = h.AlphaSynapse(0.5, sec=soma)
syn.onset = 0.5
syn.gmax = 0.05
syn.e = 0
def insert_alphasynapse(segment, onset=200.0, gmax=1e-3, tau=0.1):
asyn = h.AlphaSynapse(segment)
asyn.onset = onset
asyn.gmax = gmax
asyn.tau = tau
return asyn
def __init__(self):
neuron_utils.createProject(name='Simple Cell')
self.soma = h.Section(name='soma')
self.dend = h.Section(name='dend')
self.dend.connect(self.soma(1))
# Surface area of cylinder is 2*pi*r*h (sealed ends are implicit).
# Makes a soma of 500 microns squared.
self.soma.L = self.soma.diam = 12.6157
self.dend.L = 200 # microns
self.dend.diam = 1 # microns
for sec in h.allsec():
sec.Ra = 100 # Axial resistance in Ohm * cm
sec.cm = 1 # Membrane capacitance in micro Farads / cm^2
# Insert active Hodgkin-Huxley current in the soma
self.soma.insert('hh')
self.soma.gnabar_hh = 0.12 # Sodium conductance in S/cm2
self.soma.gkbar_hh = 0.036 # Potassium conductance in S/cm2
self.soma.gl_hh = 0.0003 # Leak conductance in S/cm2
self.soma.el_hh = -54.3 # Reversal potential in mV
# Insert passive current in the dendrite
self.dend.insert('pas')
self.dend.g_pas = 0.001 # Passive conductance in S/cm2
self.dend.e_pas = -65 # Leak reversal potential mV
self.dend.nseg = 10
# add synapse (custom channel)
self.syn = h.AlphaSynapse(self.dend(1.0))
self.syn.e = 0 # equilibrium potential in mV
self.syn.onset = 20 # turn on after this time in ms
self.syn.gmax = 0.05 # set conductance in uS
self.syn.tau = 0.1 # set time constant
# self.stim = h.IClamp(self.dend(1.0))
# self.stim.amp = 0.3 # input current in nA
# self.stim.delay = 1 # turn on after this time in ms
# self.stim.dur = 1 # duration of 1 ms
# record soma voltage and time
self.t_vec = h.Vector()
self.t_vec.record(h._ref_t)
neuron_utils.createStateVariable(id='time',
name='time',
units='ms',
python_variable={
"record_variable": self.t_vec,
"segment": None
})
self.v_vec_soma = h.Vector()
self.v_vec_soma.record(self.soma(1.0)._ref_v) # change recoding pos
# TODO How do we extract the units?
neuron_utils.createStateVariable(id='v_vec_soma',
name='v_vec_soma',
units='mV',
python_variable={
"record_variable":
self.v_vec_soma,
"segment": self.soma(1.0)
})
self.v_vec_dend = h.Vector()
self.v_vec_dend.record(self.dend(1.0)._ref_v)
# TODO How do we extract the units?
neuron_utils.createStateVariable(id='v_vec_dend',
name='v_vec_dend',
units='mV',
python_variable={
"record_variable":
self.v_vec_dend,
"segment": self.dend(1.0)
})
# run simulation
h.tstop = 60 # ms
# h.run()
neuron_geometries_utils.extractGeometries()
sim_length = float(args[7])
try:
t_on = list(map(lambda x: float(x), args[8][1:len(args[8])-1].split(',')))
except:
t_on = [float(args[8])]
save_dir = args[-1]
axon.L = L
axon.diam = diam
axon.nseg = nseg
axon.Ra = Ra
axon.insert('hh')
asyns = [h.AlphaSynapse(axon(0)), h.AlphaSynapse(axon(0))]
for ix,t in enumerate(t_on):
asyns[ix].onset = t
asyns[ix].gmax = g_max
h.tstop = sim_length
t_vec = h.Vector() # Time stamp vector
t_vec.record(h._ref_t)
axon_segments = list(axon)
v_vec = []
for axon_segment in axon_segments:
v_vec.append(h.Vector())
v_vec[-1].record(axon_segment._ref_v)
h.cvode_active(1)
# if segIndex % disp_len == 0: # Demyelinated axon properties
# maxRange = 100
# for x in xrange(1,maxRange):
# xIndexNorm_ = float(segIndex+(disp_len*3/4)-int(maxRange/2)+x)/float(axonMotor.nseg)
# if segIndexNorm_+xIndexNorm_<1:
# print x
# # print "segIndex: %f" % (segIndex,)
# axonMotor(segIndexNorm_+xIndexNorm_).hh.gnabar = 0.012*chan_scale#1e-9 (constant for Myelin study // 0.012*chan_scale for channels study)
# axonMotor(segIndexNorm_+xIndexNorm_).hh.gkbar = 0.0036*chan_scale#1e-9 (constant for Myelin study // 0.0036*chan_scale for channels study)
# axonMotor(segIndexNorm_+xIndexNorm_).hh.gl = gL#*1e-9 (constant for Myelin study // gL for channels study)
# axonMotor(segIndexNorm_+xIndexNorm_).cm = 1.
'''
We tried current clamp, but it worked as same as the Alpha Synapse
'''
# Insert Alpha Synapse
syn = h.AlphaSynapse(somaMotor(1.0))
syn.e = 0 # equilibrium potential in mV
# syn.onset = 20 # turn on after this time in ms
syn.gmax = 1e6 # set conductance in uS
syn.tau = 1e-2 # set time constant
# Set up plot
t_vec = h.Vector() # record time
t_vec.record(h._ref_t)
v_vec_soma = h.Vector() # record soma
v_vec_soma.record(somaMotor(0.5)._ref_v)
# Setting up the recordings
probe_rate = 0.01
axon_locs = np.arange(probe_rate, 1,
spine_head.insert('pas') #active conductances?
spine_neck = h.Section(name='spine_neck')
spine_neck.L = spine_neck_length
spine_neck.diam = 0.200
spine_neck.Ra = 250
spine_neck.cm = 1
spine_neck.insert('pas')
spineLoc = int(spine_soma_distance) #den_l/2
spine_neck.connect(dend[spineLoc](0.5), 0)
spine_head.connect(spine_neck(1), 0)
h.define_shape() # Translate into 3D points.
syn = h.AlphaSynapse(spine_head(0.5))
syn.gmax = AMPAgmax #uS
spike_onset = 20 #ms
syn.onset = spike_onset #ms
syn.tau = 1.5 #ms off kinetic
syn.e = 0
# #uncomment for including of NMDA channels
# #insert additional channels to synpase (NMDA for now, add Ca?)
# NMDA_syn = h.Exp2SynNMDA(spine_head(0.5)) # conductance is set in uS
# NMDA_syn.tau1 = 1
# NMDA_syn.tau2 = 25
# NC = h.NetCon(None, NMDA_syn, 0, 0, NMDAgmax) # connection; from, to, threshold, delay, weight
# def synStim():
# NC.event(spike_onset)
def runsim_dendrite(
tstop=500.0,
dt=0.025,
temp=21.0,
somaL=15.0,
somaD=15.0,
somagleak=0.0003,
dendL=250.0,
dendD=2.0,
dendgleak=0.00001,
inputPos=0.9,
inputStart=10.0,
inputDur=1.0,
inputAmp=0.5,
syn1Pos=0.6,
syn1Start=50.0,
syn1g=0.01,
syn1Erev=0.0,
syn1Tau=25.0,
syn2Pos=0.4,
syn2Start=45.0,
syn2g=0.005,
syn2Erev=-80.0,
syn2Tau=40.0,
):
#DEFINE MODEL CELL
Soma = h.Section()
Soma.L = somaL
Soma.nseg = 7
Soma.diam = somaD
Soma.insert("hh")
Soma.el_hh = -65.0
Soma.gl_hh = somagleak
Soma.ena = 50
Soma.gnabar_hh = 0.13
Soma.gkbar_hh = 0.04
Dend = h.Section()
Dend.L = dendL
dendsegs = int(np.round(dendL / 10.0))
if dendsegs < 3:
dendsegs = 3
Dend.nseg = dendsegs
Dend.diam = dendD
Dend.insert("pas")
Dend.g_pas = dendgleak
Dend.e_pas = -65.0
for sec in h.allsec():
sec.Ra = 150
Dend.connect(Soma(1))
#SYNAPSES
syn1 = h.AlphaSynapse(Dend(syn1Pos))
syn1.onset = syn1Start
syn1.tau = syn1Tau
syn1.gmax = syn1g
syn1.e = syn1Erev
syn2 = h.AlphaSynapse(Dend(syn2Pos))
syn2.onset = syn2Start
syn2.tau = syn2Tau
syn2.gmax = syn2g
syn2.e = syn2Erev
#GENERAL SETTINGS
h.dt = dt # simulation (or "sampling") rate
h.celsius = temp # simulation global temperature
#INSTRUMENTATION
Electrode = h.IClamp(Dend(inputPos))
Electrode.delay = inputStart
Electrode.amp = inputAmp
Electrode.dur = inputDur
SVm = h.Vector()
SVm.record(Soma(0.5)._ref_v)
DVm = h.Vector()
DVm.record(Dend(inputPos)._ref_v)
nrngo(tstop, -65.0)
# PACK AND EXPORT DATA
t = np.linspace(0, tstop - dt, int(np.round(tstop / dt)))
SVm = np.array(SVm)
DVm = np.array(DVm)
return (t, SVm, DVm)
soma.insert('hh')
ap_dend.L = 500.0
ap_dend.diam = 2
ap_dend.nseg = 23
ap_dend.insert('hh')
ap_dend.gnabar_hh = 0.012
ap_dend.gkbar_hh = 0.0036
ap_dend.gl_hh = 0.00003
ap_dend.connect(soma, 1.0, 0)
# synaptic input
syn = h.AlphaSynapse(0.5, sec=ap_dend) #syn points to ap_dend,0.5
syn.onset = 5.0
syn.tau = 0.1
syn.gmax = 0.05
cvode = h.CVode()
cvode.active(1)
cvode.atol(1.0e-5)
a = []
b = np.zeros(4)
dt = 0.01
tstop = 20
v_init = -65
soma(0.5).hh.gnabar = 0.13
# Change the equilibrium potential of the passive mechanism in the middle segment of the dend to -65
dend(0.5).pas.e = -65
for sec in h.allsec():
for seg in sec:
for mech in seg:
print sec.name(), seg.x, mech.name()
vstim = h.SEClamp(dend(1.0))
vstim.dur1 = 2
vstim.amp1 = -30
# add synapse (custom channel)
syn = h.AlphaSynapse(dend(1.0))
syn.e = 0 # equilibrium potential in mV
syn.onset = 20 # turn on after this time in ms
syn.gmax = 0.1 # set conductance in uS
syn.tau = 0.1 # set time constant
# add current clamp stimulus
stim = h.IClamp(dend(1.0))
stim.amp = 0.3 # input current in nA
stim.delay = 40 # turn on after this time in ms
stim.dur = 1 # duration of 1 ms
# record soma voltage and time
t_vec = h.Vector()
v_vec_soma = h.Vector()
v_vec_dend = h.Vector()
from neuron import h, gui # h for neuron interaction, gui to run simulation
from matplotlib import pyplot # For plotting results
import pickle
soma = h.Section(name='soma')
soma.insert('pas') # passive channel in soma
asyn = h.AlphaSynapse(soma(0.5)) # insert an alpha synapse to soma segment
asyn.onset = 20
asyn.gmax = 1
# recording variables
v_vec = h.Vector()
t_vec = h.Vector()
v_vec.record(soma(0.5)._ref_v)
t_vec.record(h._ref_t)
h.tstop = 40.0
h.run()
# plotting
pyplot.figure(figsize=(8,4))
pyplot.plot(t_vec, v_vec)
pyplot.xlabel('time (ms)')
pyplot.ylabel('mV')
pyplot.show(block=False)
# save the vectors from simulation
with open('t_vec.p', 'w') as t_vec_file:
pickle.dump(t_vec.to_python(), t_vec_file)
with open('v_vec.p', 'w') as v_vec_file:
pickle.dump(v_vec.to_python(), v_vec_file)
print "type(soma(0.5)) =", type(soma(0.5))
## Observing diff attributes of segments and their variables:
mech = soma(0.5).pas
print "Soma section .5, passive curent attributes: ", dir(mech)
print " Soma section .5, passive curent conductance (g):", mech.g
print " Soma section .5, passive curent resting potential (e): ", soma(
0.5).e_pas
## POINT PROCESS ####################################################
#Inserting alpha synapse. This is equivalent to NEURON GUI point process menu
asyn = h.AlphaSynapse(
0.5, sec=soma
) ## new point process. Pass the a-synapse the segment to whic it will bind
'''
print "asyn.e", asyn.e
print "asyn.gmax", asyn.gmax
print "asyn.onset", asyn.onset
print "asyn.tau", asyn.tau
'''
print "Alpha synapse attributes: ", dir(asyn)
asyn.onset = 20
asyn.gmax = 5
print " "
print "CURRENT STATE OF SOMA:"