ตัวอย่างการใช้งาน NEURON กับ Python

1.py

# The python version of the legacy NEURON tutorial
# 
# The model consists of an axon and soma with HH channels
# and 3 passive dendrite cones.
#
# Start with
# $ nrngui -python
# >>> execfile('neuron_example.py')
#
# - E. Muller 03.2009.


# import the hoc interpreter object
from neuron import h

# enable the gui
from neuron import gui

soma = h.Section()
axon = h.Section()

axon.L = 1000.0
axon.diam = 1.0
axon.nseg = 40

soma.L = 30.0
soma.diam = 30.0
soma.nseg = 1

# make a list of 3 dendrites
dendrites = [h.Section() for x in range(3)]


# connect them up

# In [x]: ? soma.connect
# ...
# childSection.connect(parentSection, [parentX], [childEnd])

axon.connect(soma,0,0) # hoc equiv: connect soma(0) axon(0)


for dend in dendrites:
    dend.connect(soma,1,0)



# add Hodgkin-Huxley channels in soma and axon
soma.insert('hh')
axon.insert('hh')

# configure dendrites

for d in dendrites:
    d.L = 200.0 # total length [micrometers]
    d.insert('pas')  # insert passive current

    d.nseg = 20  # number of spatial discretization segments
    
    # loop over segments setting parameters
    for seg in d:
        seg.pas.e = -65.0   #[mV]
        seg.pas.g = 0.002 #[S/cm^2]
    

    # set diameters as for a code with:
    # d = 10 at x=0
    # d = 3 at x=1

    f = lambda x: 5.0*(1-x) + 1.0*(x)

    for seg in d:
        seg.diam = f(seg.x)
        


#  biophysics
for sec in h.allsec ():
    sec.Ra = 100
    sec.cm = 1



# Stimulus

stim = h.IClamp(soma(0.5))
stim.delay = 1.0 # [ms]
stim.dur = 5 # [ms] duration
stim.amp = 0.5 # [nA] amplitude

# Record the voltage
v = h.Vector()
v.record(soma(0.5)._ref_v)

#v1 = h.Vector()
#v1.record(axon(1.0)._ref_v)

#v2 = h.Vector()
#v2.record(dendrites[0](1.0)._ref_v)


# set
h.dt = 0.01

# run for 50 ms
tstop = 50.0

# Use NEURON's graphing functionality
# as there may still be issues importing neuron in ipython.
                                      
g = h.Graph()
g.size(0 , 10, -80 , 40)            
g.addvar('v(0.5)', sec = soma) 
#g.addvar('v(0.5)', sec=axon)
#g.addvar('v(1.0)', sec=axon)

#g.addvar('v(0.9)', sec=dendrites[0])




# run the simulation

def go():
    h.finitialize()

    g.begin()
    while h.t<tstop:
        h.fadvance()
        # update the graph while simulating
        g.plot(h.t)

    g.flush()


go()




# g.size(0 , tstop, -80 , 40) 
# stim.amp = 3
# stim.dur = 20


# use pylab to get a better plot

def pyplot():
    global v,tstop

    # convert neuron vector to array
    v_arr = array(v)
    # time bins
    t = linspace(0,tstop,len(v_arr))

    # normal pylab plot
    plot(t,v_arr)


#pyplot()
#stim.amp = 2
#stim.dur = 20.0
#go()
#pyplot()