def test_current_balance_synapse_at_section_end():
h('synapse.loc(0)')
cell.initialize(dt=0.025)
t, I = cell.integrate(1)
coord = cell.get_seg_coords()
assert (np.abs(total_current(coord, I))<1e-6).all()
def test_current_balance_synapse_at_section_end():
h('synapse.loc(0)')
cell.initialize(dt=0.025)
t, I = cell.integrate(1)
coord = cell.get_seg_coords()
assert (np.abs(total_current(coord, I)) < 1e-6).all()
def test_current_balance_synapse_in_segment():
h('synapse.loc(0.1)')
h('soma {insert pas}')
cell.initialize(dt=0.025)
t, I = cell.integrate(1)
coord = cell.get_seg_coords()
assert (np.abs(total_current(coord, I))<1e-6).all()
def test_current_balance_synapse_in_segment():
h('synapse.loc(0.1)')
h('soma {insert pas}')
cell.initialize(dt=0.025)
t, I = cell.integrate(1)
coord = cell.get_seg_coords()
assert (np.abs(total_current(coord, I)) < 1e-6).all()
def get_data(data_dir, simulate_new=True, run_original=True):
''' returns the data from the simulations, depending on the params, it either
runs new simulations or not.
run_original = True -> original Hallermann model,
False -> reduced Nav model'''
if run_original:
save_file = 'data_hallerman_raw_original.npz'
else:
save_file = 'data_hallerman_raw_redNav.npz'
save_file = os.path.join(data_dir, save_file)
if simulate_new:
# params
dt = 0.0125
sim_len = 60 # ms
stim_delay = 40 # ms
stim_dur = 1 # ms
postfix = ''
det = True
if run_original:
simulate_original_model(data_dir) # to simulate original model
else:
simulate_reduced_Nav_model(data_dir) # to simulate reduced Nav model
st = add_stimulation(st_delay=stim_delay,st_dur=stim_dur, stim_delay=stim_delay)
vecs_ais, vec_soma, vec_st = get_recording_vecs(st)
# simulate the cell
cell.initialize(dt=dt)
cell.h.finitialize(-85)
t, I, I_axial = cell.integrate(sim_len, i_axial=True)
# extract the data
v_soma = np.array(vec_soma)
h.define_shape()
seg_coords = cell.get_seg_coords()
#!!!! Need of current correction
areas = []
for sec in cell.h.allsec():
areas += [seg.area() for seg in sec]
surface = np.pi * coords['diam'] * coords['L']
currents_with_electrode = currents_with_electrode * areas / surface
#!!!!!
# save the data
time = (np.arange(len(v_soma))*dt)
np.savez(save_file, I=I, t=time, I_axial=I_axial, v_soma=v_soma, seg_coords=seg_coords,vecs_ais=vecs_ais,
dt=h.dt, stim_delay=stim_delay)
data = np.load(save_file)
return data
def test_axial_currents():
h('soma {insert pas}')
isim = h.IClamp(1, sec=h.cable)
isim.delay = 0
isim.dur = 1
isim.amp = 2 #nA
h.cable.Ra = 0.1
cell.initialize(0.1)
t, imem_density, iax_density = cell.integrate(0.2, i_axial=True)
coord = cell.get_seg_coords()
iax = iax_density*coord['diam'][None,:]**2*1e-8/4.*h.PI #mA
assert np.abs(iax[-1,1]*1e6 - isim.amp)<0.1*isim.amp
def test_axial_currents():
h('soma {insert pas}')
isim = h.IClamp(1, sec=h.cable)
isim.delay = 0
isim.dur = 1
isim.amp = 2 #nA
h.cable.Ra = 0.1
cell.initialize(0.1)
t, imem_density, iax_density = cell.integrate(0.2, i_axial=True)
coord = cell.get_seg_coords()
iax = iax_density * coord['diam'][None, :]**2 * 1e-8 / 4. * h.PI #mA
assert np.abs(iax[-1, 1] * 1e6 - isim.amp) < 0.1 * isim.amp
def simulate_cells(all_params): #synapses, seg_coords_cells, cells, parameters_from_file):
print 'record what is happening in the synapses'
timestep = all_params['timestep']
vec_soma = cell.h.Vector()
vec_soma.record(eval('h.soma[0]('+str(0.3)+')._ref_v'), sec = eval('h.soma[0]')) # vector for recording voltage at the soma
# initialize it with the timestep
cell.initialize(dt=timestep) # ms
cell.h.finitialize(all_params['memb_pot']) # setting initial membrane potential
# make calculations of I and I axial for each cell separately
t_length = all_params['sim_len']
t, I, I_axial = cell.integrate(t_length, i_axial=True)
results_sim = {'I': I, # transmembrane current
'I_axial': I_axial, # axial current
'v_soma': np.array(vec_soma)}
return results_sim
# Parameters
dist = 2.5 #mm
rho = 3.5 #conductivity, Ohm.m
alpha = 0. #angle, rad
cutoff = 800. #high-pass cutoff, Hz
order = 401 #filter order
# Electrode position and filter
pos = (dist*np.sin(alpha)*1000, dist*np.cos(alpha)*1000, 0)
fir = field.hp_fir(order, cutoff, dt)
# Simulation
cell.load_model('models/Mainen/demo_ext.hoc',
'models/Mainen/%s/.libs/libnrnmech.so' % ARCH)
cell.initialize(dt=dt)
t, I = cell.integrate(tstop)
coords = cell.get_seg_coords()
# Select segments
dend = field.select_sections(coords, "dend")
soma = field.select_sections(coords, "soma")
axon = field.select_sections(coords, "(node)|(myelin)")
iseg = field.select_sections(coords, "iseg")
hill = field.select_sections(coords, "hill")
all = field.select_sections(coords, ".*")
colors = {"dend": "r",
"soma": "c",
"axon": "b",
"all" : "k",
"iseg" : "g",
dt = 0.025
tstop=50
# Parameters
rho = 3.5 #conductivity, Ohm.m
cutoff = 800. #high-pass cutoff, Hz
order = 401 #filter order
Nsamp = 30
filter = None
# Simulation
cell.load_model('models/Mainen/demo_ext.hoc',
'models/Mainen/%s/.libs/libnrnmech.so' % ARCH)
cell.initialize(dt=dt)
t, I = cell.integrate(tstop)
coords = cell.get_seg_coords()
# Plots
fig = plt.figure(figsize=(6,6), facecolor=(0.7, 0.7, 0.7))
fig.subplots_adjust(left=0.05, right=0.95)
ax = plt.subplot(111, frameon=False)
S = np.pi*coords['diam']*coords['L'] #segment surface
p2p = np.abs(I).max(0)-np.abs(I).min(0)
norm = colors.LogNorm(vmin=p2p.min(), vmax=p2p.max())
col = graph.plot_neuron(coords, p2p, norm=norm)
plt.xticks([])
plt.yticks([])
plt.ylim((-200, 610))
plt.xlim((-550, 250))
stimlist[-1].interval = 1
stimlist[-1].noise = 0
nclist.append(
cell.h.NetCon(stimlist[-1],
syn,
con_thresh=0,
con_delay=1,
con_weight=.25e-3))
init_synapse(cell_no=0, syn_start=0.1)
init_synapse(cell_no=1, syn_start=0.5)
# initialise and run neuron simulations
cell.initialize(dt=dt)
t, I_cell = cell.integrate(t_length, i_axial=False, neuron_cells=cells)
# CALCULATION OF THE FIELD
seg_coords = cell.get_seg_coords()
n_samp = 40
x_range = [-210, 430]
y_range = [-255, 725]
# define grid
xx, yy = field.calc_grid(x_range, y_range, n_samp)
# join the currents together
I = I_cell.swapaxes(0, 1).reshape(I_cell.shape[1], -1)
v_ext = field.estimate_on_grid(seg_coords, I, xx, yy)
