def run_Ipulse_protocol(Model,
clamps=[{
'value': 0.,
'duration': 1000.
}],
with_plot=False,
ge=None):
t, neuron, SEGMENTS = initialize_sim(Model)
# recording and running
M = ntwk.StateMonitor(neuron, ('v', 'I'), record=[0])
t = 0
for clamp in clamps:
neuron.I[0] = clamp['value'] * ntwk.pA
ntwk.run(clamp['duration'] * ntwk.ms)
t += clamp['duration']
output = {
't': np.array(M.t / ntwk.ms),
'Ic': np.array(np.array(M.I / ntwk.pA)[0, :]),
'Vm_soma': np.array(M.v / ntwk.mV)[0, :]
}
return output
def adaptive_run(neuron, dt, tstop,
check_every=5, # ms
VCLIP=[-80, 0]):
Vm, i, last_t = [], 0, 0
while last_t<tstop:
ntwk.run(0.2*ntwk.ms)
neuron.v = np.clip(neuron.v/ntwk.mV, -80, 0)*ntwk.mV
print(np.max(neuron.v/ntwk.mV), np.min(neuron.v/ntwk.mV))
last_t = M.t[-1]/ntwk.ms
print(M.t[-1]/ntwk.ms)
pass
def run_single_sim(Model, args):
Model['VC-cmd'] = args.Vcmd
Model['alphaZn'] = args.alphaZn
Tstim = args.n_pulses/args.freq_pulses*1e3
stim_events = args.Tdiscard0+np.arange(args.n_pulses)*1e3/args.freq_pulses
Model['tstop'] = args.Tdiscard0+Tstim+args.Tend
Model['dt'] = args.dt
# initialize voltage-clamp sim
t, neuron, SEGMENTS = initialize_sim(Model, method='voltage-clamp')
np.random.seed(args.seed)
synapses_loc = LOCs[args.syn_location]+np.arange(args.Nsyn)
spike_IDs, spike_times = np.empty(0, dtype=int), np.empty(0, dtype=float)
for ie, te in enumerate(stim_events):
spike_times = np.concatenate([spike_times,
te*np.ones(args.Nsyn)])
spike_IDs = np.concatenate([spike_IDs, np.arange(args.Nsyn)])
Estim, ES = ntwk.process_and_connect_event_stimulation(neuron,
spike_IDs, spike_times,
synapses_loc,
EXC_SYNAPSES_EQUATIONS.format(**Model),
ON_EXC_EVENT.format(**Model))
# recording and running
M = ntwk.StateMonitor(neuron, ('v'), record=[0, synapses_loc[0]])
S = ntwk.StateMonitor(ES, ('X', 'gAMPA', 'gRiseNMDA', 'gDecayNMDA', 'bZn'), record=[0])
# # Run simulation
ntwk.run((args.Tdiscard0+Tstim+args.Tend)*ntwk.ms)
output = {'t':np.array(M.t/ntwk.ms), 'Vcmd':args.Vcmd}
output['synapses_loc'] = synapses_loc
output['Vm_soma'] = np.array(M.v/ntwk.mV)[0,:]
output['gAMPA_syn'] = np.array(S.gAMPA/ntwk.nS)[0,:]
output['X_syn'] = np.array(S.X)[0,:]
bZn, gRise, gDecay = np.array(S.bZn)[0,:], np.array(S.gRiseNMDA)[0,:], np.array(S.gDecayNMDA)[0,:]
output['Vm_syn'] = np.array(M.v/ntwk.mV)[1,:]
output['bZn_syn'] = bZn
output['gNMDA_syn'] = Model['qNMDA']*Model['nNMDA']*(gDecay-gRise)/(1+0.3*np.exp(-output['Vm_syn']/Model['V0NMDA']))*(1.-Model['alphaZn']*bZn)
output['Ic'] = (output['Vm_soma']-Model['VC-cmd'])*Model['VC-gclamp'] # nA
output['args'] = dict(vars(args))
return output
def run(neuron, Model, Estim, ES, Istim, IS):
# recording and running
M = ntwk.StateMonitor(neuron, ('v'), record=[0, # soma
Model['stim_apic_compartment_index']])
S = ntwk.StateMonitor(ES, ('X', 'gAMPA', 'gNMDA', 'bZn'), record=[0])
# # Run simulation
ntwk.run(Model['tstop']*ntwk.ms)
output = {'t':np.array(M.t/ntwk.ms),
'Vm_soma':np.array(M.v/ntwk.mV)[0,:],
'bZn_syn':np.array(S.bZn)[0,:],
'gAMPA_syn':np.array(S.gAMPA/ntwk.nS)[0,:],
'gNMDA_syn':np.array(S.gNMDA/ntwk.nS)[0,:],
'X_syn':np.array(S.X)[0,:],
'Vm_syn':np.array(M.v/ntwk.mV)[1,:],
'Model':Model}
output['Ic'] = (output['Vm_soma']-Model['VC-cmd'])*Model['VC-gclamp'] # nA
return output
# recording and running
if active:
M = ntwk.StateMonitor(neuron, ('v', 'InternalCalcium'), record=[0, synapses_loc[0]])
else:
M = ntwk.StateMonitor(neuron, ('v'), record=[0, synapses_loc[0]])
S = ntwk.StateMonitor(ES, ('X', 'gAMPA', 'gE_post', 'bZn'), record=[0])
# # Run simulation
start = time.time()
if active:
ntwk.defaultclock.dt = 0.01*ntwk.ms
else:
ntwk.defaultclock.dt = 0.025*ntwk.ms
ntwk.run(tstop*ntwk.ms)
# if not active:
# ntwk.run(tstop*ntwk.ms)
# else:
# adaptive_run(tstop, neuron, M)
# ntwk.run(tstop*ntwk.ms)
print('Runtime: %.2f s' % (time.time()-start))
from datavyz import ges as ge
fig, AX = ge.figure(axes=(1,2),figsize=(2,1))
AX[0].plot(np.array(M.t/ntwk.ms), np.array(M.v/ntwk.mV)[0,:], label='soma')
ge.plot(np.array(M.t/ntwk.ms), np.array(M.v/ntwk.mV)[1,:], label='dend', ax=AX[0])
if active:
def run_single_trial(Model):
np.random.seed(Model['seed']+Model['branch_index'])
# computing Nsyn_array
if Model['ISIB_log_Nsyn']:
Nsyn_array = np.logspace(np.log10(Model['ISIB_Nsyn1']), np.log10(Model['ISIB_Nsyn2']),
Model['ISIB_Nsyn_N'], dtype=int)
else:
Nsyn_array = np.linspace(np.log10(Model['ISIB_Nsyn1']), np.log10(Model['ISIB_Nsyn2']),
Model['ISIB_Nsyn_N'], dtype=int)
# morphology analysis
morpho = ntwk.Morphology.from_swc_file(Model['morpho_file'])
SEGMENTS = ntwk.morpho_analysis.compute_segments(morpho)
nseg = len(SEGMENTS['x'])
segments_of_branches = compute_branches_for_stimuli(SEGMENTS)
isegs_branch = segments_of_branches[Model['branch_index']]
basal_cond = ntwk.morpho_analysis.find_conditions(SEGMENTS,
comp_type='dend',
min_distance_to_soma=20e-6)
# spreading synapses for bg noise
Nsyn_Glut, pre_to_iseg_Glut,\
Nsyn_per_seg_Glut = ntwk.spread_synapses_on_morpho(SEGMENTS,
Model['DensityGlut_L23'],
cond=basal_cond,
density_factor=1./100./1e-12)
# simulation
Model['tstop'] = Model['ISIB_delay']+len(Nsyn_array)*Model['ISIB_window']
t, neuron, SEGMENTS = initialize_sim(Model)
if Model['Fexc_bg']>0:
spike_IDs, spike_times = ntwk.spikes_from_time_varying_rate(t,
0*t+Model['Fexc_bg'],
N=len(isegs_branch),
SEED=Model['seed']+1)
else:
spike_IDs, spike_times = np.empty(0, dtype=int), np.empty(0, dtype=float)
for i, n in enumerate(Nsyn_array):
nsyn = int(min([len(isegs_branch), n]))
synapses_loc = np.random.choice(isegs_branch, nsyn, replace=False)
spike_times = np.concatenate([spike_times, Model['ISIB_delay']+i*Model['ISIB_window']*np.ones(len(synapses_loc))])
spike_IDs = np.concatenate([spike_IDs, np.arange(len(synapses_loc))])
it = 0
while nsyn<n:
nsyn2 = int(min([len(isegs_branch), n-nsyn]))
synapses_loc = np.random.choice(isegs_branch, nsyn2, replace=False)
spike_times = np.concatenate([spike_times,
Model['ISIB_delay']+i*Model['ISIB_window']*np.ones(len(synapses_loc))+(it+1)*Model['dt']])
spike_IDs = np.concatenate([spike_IDs, np.arange(len(synapses_loc))])
nsyn += nsyn2
it+=1
spike_IDs, spike_times = ntwk.deal_with_multiple_spikes_per_bin(spike_IDs, spike_times, t)
Estim, ES = ntwk.process_and_connect_event_stimulation(neuron,
spike_IDs, spike_times,
isegs_branch,
EXC_SYNAPSES_EQUATIONS.format(**Model),
ON_EXC_EVENT.format(**Model))
# recording and running
M = ntwk.StateMonitor(neuron, ('v'), record=[0, synapses_loc[0]])
# # Run simulation
ntwk.run(Model['tstop']*ntwk.ms)
return np.array(M.t/ntwk.ms), np.array(M.v/ntwk.mV)[0,:]
def run_single_sim(Model, stim, Vcmd=20, Npicked=100, seed=0):
Model['VC-cmd'] = Vcmd
Model['dt'] = 0.01
# initialize voltage-clamp sim
t, neuron, SEGMENTS = initialize_sim(Model,
method='voltage-clamp',
tstop=stim['t'][-1])
# find synapses
basal_cond = ntwk.morpho_analysis.find_conditions(
SEGMENTS, comp_type='dend', min_distance_to_soma=20e-6)
Nsyn, pre_to_iseg,\
Nsyn_per_seg = ntwk.spread_synapses_on_morpho(SEGMENTS,
10, # density
cond=basal_cond,
density_factor=1./100./1e-12)
if type(Npicked) is int:
Npicked = Npicked * np.ones(14, dtype=int)
np.random.seed(seed)
synapses_loc = np.random.choice(pre_to_iseg, np.max(Npicked))
spike_IDs, spike_times = np.empty(0, dtype=int), np.empty(0, dtype=float)
for te, n in zip(stim['events'], Npicked):
spike_times = np.concatenate([spike_times, te * np.ones(n)])
spike_IDs = np.concatenate([spike_IDs, np.arange(n)])
Estim, ES = ntwk.process_and_connect_event_stimulation(
neuron, spike_IDs, spike_times, synapses_loc,
EXC_SYNAPSES_EQUATIONS.format(**Model), ON_EXC_EVENT.format(**Model))
# recording and running
M = ntwk.StateMonitor(neuron, ('v'), record=[0, synapses_loc[0]])
S = ntwk.StateMonitor(ES, ('X', 'gAMPA', 'gRiseNMDA', 'gDecayNMDA', 'bZn'),
record=[0])
# # Run simulation
print(Model['tstop'])
ntwk.run(Model['tstop'] * ntwk.ms)
output = {'t': np.array(M.t / ntwk.ms), 'Vcmd': Vcmd}
output['synapses_loc'] = synapses_loc
output['Npicked'] = Npicked
output['Vm_soma'] = np.array(M.v / ntwk.mV)[0, :]
output['gAMPA_syn'] = np.array(S.gAMPA / ntwk.nS)[0, :]
output['X_syn'] = np.array(S.X)[0, :]
bZn, gRise, gDecay = np.array(S.bZn)[0, :], np.array(
S.gRiseNMDA)[0, :], np.array(S.gDecayNMDA)[0, :]
output['Vm_syn'] = np.array(M.v / ntwk.mV)[1, :]
output['bZn_syn'] = bZn
output['gNMDA_syn'] = Model['qNMDA'] * Model['nNMDA'] * (
gDecay -
gRise) / (1 + 0.3 * np.exp(-output['Vm_syn'] / Model['V0NMDA'])) * (
1. - Model['alphaZn'] * bZn)
output['Ic'] = (output['Vm_soma'] -
Model['VC-cmd']) * Model['VC-gclamp'] # nA
for key in stim:
if key not in output:
output[key] = stim[key]
return output