sim_refs = []
for gj_conn_type in ['2010', '2012']:
sim_config_name = 'input_resistance_' + gj_conn_type + 'gap'
sim_config = project.simConfigInfo.getSimConfig(sim_config_name)
project.neuronSettings.setNoConsole()
for trial in range(n_trials):
# generate
pm.doGenerate(sim_config_name, 1234)
while pm.isGenerating():
time.sleep(0.02)
print('network generated')
for amplitude in stim_amplitude_range:
sim_ref = utils.ir_sim_ref(timestamp, gj_conn_type, amplitude,
trial)
sim_refs.append(sim_ref)
sim_path = '../simulations/' + sim_ref
project.simulationParameters.setReference(sim_ref)
# set stim rate
amplitude_in_nA = amplitude / 1000.
for cell_type in ['Vervaeke', 'reduced']:
stim = project.elecInputInfo.getStim('cclamp_network_' +
cell_type)
stim.setAmp(NumberGenerator(amplitude_in_nA))
project.elecInputInfo.updateStim(stim)
# generate and compile neuron files
print "Generating NEURON scripts..."
project.neuronFileManager.setSuggestedRemoteRunTime(10)
simulator_seed = random.getrandbits(32)
project.neuronFileManager.generateTheNeuronFiles(
dash_styles = [(4, 1.5), (None, None)]
n_trials = 1
averages = {}
sigmas = {}
lines = []
for gj_conn_type, dashes in zip(gj_conn_types, dash_styles):
averages[gj_conn_type] = {}
sigmas[gj_conn_type] = {}
for cell_type, color in zip(cell_types, colors):
averages[gj_conn_type][cell_type] = []
sigmas[gj_conn_type][cell_type] = []
for stim_level in stim_range:
responses = []
for trial in range(n_trials):
sim_ref = utils.ir_sim_ref(timestamp, gj_conn_type, stim_level,
trial)
filename = '../simulations/{0}/Golgi_network_{1}_TTX_0.dat'.format(
sim_ref, cell_type)
try:
responses.append(np.loadtxt(filename)[-1])
except IOError:
print('Data file not found: {0}'.format(sim_ref))
responses = np.array(responses)
averages[gj_conn_type][cell_type].append(responses.mean())
sigmas[gj_conn_type][cell_type].append(responses.std())
baseline = averages[gj_conn_type][cell_type][4]
averages[gj_conn_type][
cell_type] = averages[gj_conn_type][cell_type] - baseline
label = '{}, {} net'.format(cell_type, gj_conn_type)
if n_trials > 1:
ax.errorbar(stim_range,
for gj_conn_type in ['2010', '2012']:
sim_config_name = 'input_resistance_' + gj_conn_type + 'gap'
sim_config = project.simConfigInfo.getSimConfig(sim_config_name)
project.neuronSettings.setNoConsole()
for trial in range(n_trials):
# generate
pm.doGenerate(sim_config_name, 1234)
while pm.isGenerating():
time.sleep(0.02)
print('network generated')
for amplitude in stim_amplitude_range:
sim_ref = utils.ir_sim_ref(timestamp,
gj_conn_type,
amplitude,
trial)
sim_refs.append(sim_ref)
sim_path = '../simulations/' + sim_ref
project.simulationParameters.setReference(sim_ref)
# set stim rate
amplitude_in_nA = amplitude/1000.
for cell_type in ['Vervaeke', 'reduced']:
stim = project.elecInputInfo.getStim('cclamp_network_' + cell_type)
stim.setAmp(NumberGenerator(amplitude_in_nA))
project.elecInputInfo.updateStim(stim)
# generate and compile neuron files
print "Generating NEURON scripts..."
project.neuronFileManager.setSuggestedRemoteRunTime(10)
simulator_seed = random.getrandbits(32)
project.neuronFileManager.generateTheNeuronFiles(sim_config, None, NeuronFileManager.RUN_HOC,simulator_seed)
