def writeFiles(options):
# XML configuration file
config = lcg.XMLConfigurationFile(options['sampling_rate'],options['duration'])
config.add_entity(lcg.entities.H5Recorder(id=0, connections=(), compress=True))
config.add_entity(lcg.entities.RealNeuron(id=1, connections=(0,4), spikeThreshold=-20, V0=-65, deviceFile=os.environ['COMEDI_DEVICE'],
inputSubdevice=os.environ['AI_SUBDEVICE'],
outputSubdevice=os.environ['AO_SUBDEVICE'],
readChannel=options['ai'], writeChannel=options['ao'],
inputConversionFactor=os.environ['AI_CONVERSION_FACTOR_CC'],
outputConversionFactor=os.environ['AO_CONVERSION_FACTOR_CC'],
inputRange=os.environ['RANGE'], reference=os.environ['GROUND_REFERENCE'],
kernelFile='kernel.dat'))
config.add_entity(lcg.entities.PID(id=2, connections=(0,1), baseline=options['amplitude'],
gp=options['gp'], gi=options['gi'], gd=options['gd'], units='pA'))
config.add_entity(lcg.entities.Waveform(id=3, connections=(0,2), filename='frequency.stim', units='Hz'))
config.add_entity(lcg.entities.FrequencyEstimator(id=4, connections=(0,2), tau=options['tau'],
initial_frequency=options['min_freq']))
if 'current_std' in options:
config.add_entity(lcg.entities.Waveform(id=5, connections=(0,1), filename='current.stim', units='pA'))
# Noisy current stim-file
current = [[options['duration'],2,0,options['current_std'],options['current_tau'],0,0,0,0,0,0,1]]
lcg.writeStimFile('current.stim',current,False)
config.write(config_file)
# Frequency stim-file
frequency=[[options['duration'],-2,options['min_freq'],0,0,0,0,0,0,1,0,1],
[options['duration'],-2,options['max_freq']-options['min_freq'],0,0,0,0,0,0,7,1,1]]
lcg.writeStimFile('frequency.stim',frequency,False)
def hold_cell(opts, lastValueFilename):
print('Holding the cell with {0} pA'.format(opts['holding']))
if opts['model']:
with open(lastValueFilename, 'w') as fd:
fd.write('%e' % (opts['holding']))
return ''
else:
# Then use a RealNeuron to inject into the cell
tmp = lcg.XMLConfigurationFile(opts['srate'], 0.01)
insertRealNeuron(tmp, opts, [], lastValueFilename)
tmp.add_entity(
Constant(id=2, connections=[1], value=opts['holding'], units='pA'))
tmpName = 'holdNeuron.xml'
tmp.write(tmpName)
return 'lcg-experiment -c {0}'.format(tmpName)
def writeConfigurationFile(options):
config = lcg.XMLConfigurationFile(options['sampling_rate'],options['duration']+3.61)
config.add_entity(lcg.entities.H5Recorder(id=0, connections=(), compress=True))
config.add_entity(lcg.entities.RealNeuron(id=1, connections=(0), spikeThreshold=-20, V0=-65, deviceFile=os.environ['COMEDI_DEVICE'],
inputSubdevice=os.environ['AI_SUBDEVICE'],
outputSubdevice=os.environ['AO_SUBDEVICE'],
readChannel=options['ai'], writeChannel=options['ao'],
inputConversionFactor=os.environ['AI_CONVERSION_FACTOR_CC'],
outputConversionFactor=os.environ['AO_CONVERSION_FACTOR_CC'],
inputRange=os.environ['RANGE'], reference=os.environ['GROUND_REFERENCE'],
kernelFile='kernel.dat'))
config.add_entity(lcg.entities.Waveform(id=2, connections=(0,1), filename=current_file, units='pA'))
config.add_entity(lcg.entities.ConductanceStimulus(id=3, connections=(1), E=0))
config.add_entity(lcg.entities.ConductanceStimulus(id=4, connections=(1), E=-80))
current_id = 5
if options['filtered']:
if options['exc']:
config.add_entity(lcg.entities.OUNonStationary(id=current_id, connections=(0,3), tau=options['tau_exc'],
ic=options['Gm_exc'], units='nS', interval=[2.61,2.61+options['duration']]))
config.add_entity(lcg.entities.Waveform(id=current_id+1, connections=(0,current_id), filename='gexc_mean.stim', units='nS'))
config.add_entity(lcg.entities.Waveform(id=current_id+2, connections=(0,current_id), filename='gexc_stddev.stim', units='nS'))
current_id += 3
else:
config.add_entity(lcg.entities.Waveform(id=current_id, connections=(0,3), filename='gexc.stim', units='nS'))
current_id += 1
if options['inh']:
config.add_entity(lcg.entities.OUNonStationary(id=current_id, connections=(0,4), tau=options['tau_inh'],
ic=options['Gm_inh'], units='nS', interval=[2.61,2.61+options['duration']]))
config.add_entity(lcg.entities.Waveform(id=current_id+1, connections=(0,current_id), filename='ginh_mean.stim', units='nS'))
config.add_entity(lcg.entities.Waveform(id=current_id+2, connections=(0,current_id), filename='ginh_stddev.stim', units='nS'))
current_id += 3
else:
config.add_entity(lcg.entities.Waveform(id=current_id, connections=(0,4), filename='ginh.stim', units='nS'))
current_id += 1
else:
config.add_entity(lcg.entities.Waveform(id=current_id, connections=(0,3), filename='gexc.stim', units='nS'))
config.add_entity(lcg.entities.Waveform(id=current_id+1, connections=(0,4), filename='ginh.stim', units='nS'))
current_id += 2
config.write(config_file)
def writeConfigurationFile(options):
config = lcg.XMLConfigurationFile(
options['sampling_rate'],
len(options['dR']) * options['step_duration'] + options['pre'] +
options['post'])
config.add_entity(
lcg.entities.H5Recorder(id=0, connections=(), compress=True))
config.add_entity(
lcg.entities.RealNeuron(
id=1,
connections=(0),
spikeThreshold=-20,
V0=-65,
deviceFile=os.environ['COMEDI_DEVICE'],
inputSubdevice=os.environ['AI_SUBDEVICE'],
outputSubdevice=os.environ['AO_SUBDEVICE'],
readChannel=options['ai'],
writeChannel=options['ao'],
inputConversionFactor=os.environ['AI_CONVERSION_FACTOR_CC'],
outputConversionFactor=os.environ['AO_CONVERSION_FACTOR_CC'],
inputRange=os.environ['RANGE'],
reference=os.environ['GROUND_REFERENCE'],
kernelFile='kernel.dat'))
config.add_entity(
lcg.entities.Waveform(id=2,
connections=(0, 4),
filename=gexc_file,
units='nS'))
config.add_entity(
lcg.entities.Waveform(id=3,
connections=(0, 5),
filename=ginh_file,
units='nS'))
config.add_entity(
lcg.entities.ConductanceStimulus(id=4, connections=(1), E=0))
config.add_entity(
lcg.entities.ConductanceStimulus(id=5, connections=(1), E=-80))
config.write(config_file)
def writeConfigFile(options):
config = lcg.XMLConfigurationFile(
options['sampling_rate'],
options['duration'] + options['before'] + options['after'])
config.add_entity(
lcg.entities.H5Recorder(id=0, connections=(), compress=True))
config.add_entity(
lcg.entities.RealNeuron(
id=1,
connections=(0),
spikeThreshold=-20,
V0=-65,
deviceFile=os.environ['COMEDI_DEVICE'],
inputSubdevice=os.environ['AI_SUBDEVICE'],
outputSubdevice=os.environ['AO_SUBDEVICE'],
readChannel=options['ai'],
writeChannel=options['ao'],
inputConversionFactor=os.environ['AI_CONVERSION_FACTOR_CC'],
outputConversionFactor=os.environ['AO_CONVERSION_FACTOR_CC'],
inputRange=os.environ['RANGE'],
reference=os.environ['GROUND_REFERENCE'],
kernelFile='kernel.dat'))
config.add_entity(
lcg.entities.Waveform(id=2,
connections=(0, 3),
filename=stim_files['gampa'],
units='nS'))
config.add_entity(
lcg.entities.ConductanceStimulus(id=3, connections=(1), E=0.))
config.add_entity(
lcg.entities.Waveform(id=4,
connections=(0, 5),
filename=stim_files['ggaba'],
units='nS'))
config.add_entity(
lcg.entities.ConductanceStimulus(id=5, connections=(1), E=-80.))
config.add_entity(
lcg.entities.Waveform(id=6,
connections=(0, 7),
filename=stim_files['gampa_common'],
units='nS'))
config.add_entity(
lcg.entities.ConductanceStimulus(id=7, connections=(1), E=0.))
ID = 8
if not options['only_ampa_corr']:
config.add_entity(
lcg.entities.Waveform(id=ID,
connections=(0, ID + 1),
filename=stim_files['ggaba_common'],
units='nS'))
ID += 1
config.add_entity(
lcg.entities.ConductanceStimulus(id=ID, connections=(1), E=-80.))
ID += 1
if options['with_nmda']:
config.add_entity(
lcg.entities.Waveform(id=ID,
connections=(0, ID + 1),
filename=stim_files['gnmda'],
units='nS'))
ID += 1
config.add_entity(
lcg.entities.NMDAConductanceStimulus(id=ID,
connections=(1),
E=0.,
K1=options['nmda_K'][0],
K2=options['nmda_K'][1]))
ID += 1
config.add_entity(
lcg.entities.Waveform(id=ID,
connections=(0, ID + 1),
filename=stim_files['gnmda_common'],
units='nS'))
ID += 1
config.add_entity(
lcg.entities.NMDAConductanceStimulus(id=ID,
connections=(1),
E=0.,
K1=options['nmda_K'][0],
K2=options['nmda_K'][1]))
config.write(config_file)
sys.exit(1)
try:
opts, args = getopt.getopt(sys.argv[2:], switches, long_switches)
except getopt.GetoptError, err:
print(err)
print(usage)
sys.exit(1)
opts = parse_prc_options(mode, opts)
configName = 'prc.xml'
waveformName = 'prc.stim'
lastValueFilename = 'prc.lastValue'
hold_time = 3
duration = 0
config = lcg.XMLConfigurationFile(opts['srate'], duration)
config.add_entity(H5Recorder(id=0, connections=[], compress=True))
if opts['model']:
insert_model(config, opts['holding'], lastValueFilename)
else:
insertRealNeuron(config, opts, [0], lastValueFilename)
# Perturbation waveform
if not mode == 'noise':
config.add_entity(
Waveform(id=2,
connections=[0, 1],
filename=waveformName,
triggered=True,
units='pA'))
def writeIPlusBgGConfig(I, Gexc, Ginh, ai, ao, duration, sampling_rate,
outfile):
config = lcg.XMLConfigurationFile(sampling_rate, duration + 3.61)
config.add_entity(
lcg.entities.H5Recorder(id=0, connections=(), compress=True))
config.add_entity(
lcg.entities.RealNeuron(
id=1,
connections=(0),
spikeThreshold=-20,
V0=-65,
deviceFile=os.environ['COMEDI_DEVICE'],
inputSubdevice=os.environ['AI_SUBDEVICE'],
outputSubdevice=os.environ['AO_SUBDEVICE'],
readChannel=ai,
writeChannel=ao,
inputConversionFactor=os.environ['AI_CONVERSION_FACTOR_CC'],
outputConversionFactor=os.environ['AO_CONVERSION_FACTOR_CC'],
inputRange=os.environ['RANGE'],
reference=os.environ['GROUND_REFERENCE'],
kernelFile='kernel.dat'))
config.add_entity(
lcg.entities.Waveform(id=2,
connections=(0, 1),
filename='current.stim',
units='pA'))
config.add_entity(
lcg.entities.Waveform(id=3,
connections=(0, 5),
filename='gexc.stim',
units='nS'))
config.add_entity(
lcg.entities.Waveform(id=4,
connections=(0, 6),
filename='ginh.stim',
units='nS'))
config.add_entity(
lcg.entities.ConductanceStimulus(id=5, connections=(1), E=0.))
config.add_entity(
lcg.entities.ConductanceStimulus(id=6, connections=(1), E=-80.))
config.write(outfile)
current = [[0.5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0.01, 1, -300, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0.5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0.6, 1, -100, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[duration, 1, I, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
conductance = [[2.61, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[duration, 2, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
lcg.writeStimFile('current.stim', current, None)
conductance[1][2] = Gexc['m']
conductance[1][3] = Gexc['s']
conductance[1][4] = Gexc['tau']
if 'seed' in Gexc:
conductance[1][8] = Gexc['seed']
else:
conductance[1][8] = np.random.poisson(10000)
lcg.writeStimFile('gexc.stim', conductance, None)
conductance[1][2] = Ginh['m']
conductance[1][3] = Ginh['s']
conductance[1][4] = Ginh['tau']
if 'seed' in Ginh:
conductance[1][8] = Ginh['seed']
else:
conductance[1][8] = np.random.poisson(10000)
lcg.writeStimFile('ginh.stim', conductance, None)
def writeFiles(options):
stim_file = 'frequency.stim'
config = lcg.XMLConfigurationFile(options['sampling_rate'],
options['duration'])
config.add_entity(
lcg.entities.H5Recorder(id=0, connections=(), compress=True))
config.add_entity(
lcg.entities.RealNeuron(
id=1,
connections=(0, 4),
spikeThreshold=-20,
V0=-65,
deviceFile=os.environ['COMEDI_DEVICE'],
inputSubdevice=os.environ['AI_SUBDEVICE'],
outputSubdevice=os.environ['AO_SUBDEVICE'],
readChannel=options['ai'],
writeChannel=options['ao'],
inputConversionFactor=os.environ['AI_CONVERSION_FACTOR_CC'],
outputConversionFactor=os.environ['AO_CONVERSION_FACTOR_CC'],
inputRange=os.environ['RANGE'],
reference=os.environ['GROUND_REFERENCE'],
kernelFile='kernel.dat'))
config.add_entity(
lcg.entities.PID(id=2,
connections=(0, 1),
baseline=options['I0'],
gp=options['gp'],
gi=options['gi'],
gd=options['gd']))
config.add_entity(
lcg.entities.Waveform(id=3,
connections=(0, 2),
filename=stim_file,
units='Hz'))
config.add_entity(
lcg.entities.FrequencyEstimator(id=4,
connections=(0, 2),
tau=options['tau'],
initial_frequency=options['target'] /
2))
if 'Gm_exc' in options and 'Gm_inh' in options and 'Gs_exc' in options and 'Gs_inh' in options:
gexc_stim_file = 'gexc.stim'
ginh_stim_file = 'ginh.stim'
config.add_entity(
lcg.entities.Waveform(id=5,
connections=(0, 7),
filename=gexc_stim_file,
units='nS'))
config.add_entity(
lcg.entities.Waveform(id=6,
connections=(0, 8),
filename=ginh_stim_file,
units='nS'))
config.add_entity(
lcg.entities.ConductanceStimulus(id=7, connections=(1), E=0))
config.add_entity(
lcg.entities.ConductanceStimulus(id=8, connections=(1), E=-80))
lcg.writeStimFile(gexc_stim_file, [
options['duration'], 2, options['Gm_exc'], options['Gs_exc'], 5, 0,
0, 1,
int(np.random.uniform(high=10000)), 0, 0, 1
], False)
lcg.writeStimFile(ginh_stim_file, [
options['duration'], 2, options['Gm_inh'], options['Gs_inh'], 10,
0, 0, 1,
int(np.random.uniform(high=10000)), 0, 0, 1
], False)
config.write(config_file)
lcg.writeStimFile(
stim_file,
[options['duration'], 1, options['target'], 0, 0, 0, 0, 0, 0, 0, 0, 1],
False)
if with_preamble:
stimulus = [[duration,1,holding,0,0,0,0,0,0,0,0,1],
[tail,1,holding,0,0,0,0,0,0,0,0,1]]
total_dur = preamble_duration + duration + tail
conductance[0][0] = preamble_duration
row = 0
else:
stimulus = [[tail,1,holding,0,0,0,0,0,0,0,0,1],
[duration,1,holding,0,0,0,0,0,0,0,0,1],
[tail,1,holding,0,0,0,0,0,0,0,0,1]]
total_dur = 2*tail + duration
row = 1
# write the configuration file
config = lcg.XMLConfigurationFile(sampling_rate,total_dur)
config.add_entity(lcg.entities.H5Recorder(id=0, connections=(), compress=True))
config.add_entity(lcg.entities.RealNeuron(id=1, connections=(0), spikeThreshold=-20, V0=-65, deviceFile=os.environ['COMEDI_DEVICE'],
inputSubdevice=os.environ['AI_SUBDEVICE'],
outputSubdevice=os.environ['AO_SUBDEVICE'],
readChannel=ai, writeChannel=ao,
inputConversionFactor=os.environ['AI_CONVERSION_FACTOR_CC'],
outputConversionFactor=os.environ['AO_CONVERSION_FACTOR_CC'],
inputRange=os.environ['RANGE'], reference=os.environ['GROUND_REFERENCE'],
kernelFile='kernel.dat'))
config.add_entity(lcg.entities.Waveform(id=2, connections=(0,3), filename=gexc_stim_file, units='nS'))
config.add_entity(lcg.entities.ConductanceStimulus(id=3, connections=(1), E=E_exc))
config.add_entity(lcg.entities.Waveform(id=4, connections=(0,5), filename=ginh_stim_file, units='nS'))
config.add_entity(lcg.entities.ConductanceStimulus(id=5, connections=(1), E=E_inh))
config.add_entity(lcg.entities.Waveform(id=6, connections=(0,1), filename=stim_file, units='pA'))
config.write(config_file)
def writeIOExternalTriggerConfigurationFile(config_file,
sampling_rate,
duration,
channels,
output_filename=None,
trigger={
'device': None,
'subdevice': None,
'channel': None,
'stopChannel': None
},
digitalChannels={
'device': None,
'subdevice': None,
'channels': None
}):
config = lcg.XMLConfigurationFile(sampling_rate,
duration,
output_filename,
trigger=trigger)
ID = 0
config.add_entity(
lcg.entities.H5Recorder(id=ID,
filename=output_filename,
connections=()))
ID += 1
for chan in channels:
try:
chan = completeWithDefaultValues(chan)
except KeyError:
print('Each channel must contain a "type" key.')
return False
if chan['type'] == 'input':
config.add_entity(
lcg.entities.AnalogInput(id=ID,
connections=(0),
deviceFile=chan['device'],
inputSubdevice=chan['subdevice'],
readChannel=chan['channel'],
inputConversionFactor=chan['factor'],
range=chan['range'],
aref=chan['reference'],
units=chan['units']))
else:
config.add_entity(
lcg.entities.AnalogOutput(
id=ID,
connections=(),
deviceFile=chan['device'],
outputSubdevice=chan['subdevice'],
writeChannel=chan['channel'],
outputConversionFactor=chan['factor'],
aref=chan['reference'],
units=chan['units'],
resetOutput=chan['resetOutput']))
config.add_entity(
lcg.entities.Waveform(id=ID + 1,
connections=(0, ID),
filename=chan['stimfile'],
units=chan['units']))
ID += 1
ID += 1
if not digitalChannels['channels'] is None:
for channel in digitalChannels['channels']:
config.add_entity(
lcg.entities.DigitalInput(
id=ID,
connections=(0),
deviceFile=digitalChannels['device'],
inputSubdevice=digitalChannels['subdevice'],
readChannel=channel,
units='Boolean',
eventToSend=None))
ID += 1
if not trigger['stopChannel'] is None:
config.add_entity(
lcg.entities.DigitalInput(id=ID,
connections=(0),
deviceFile=trigger['device'],
inputSubdevice=trigger['subdevice'],
readChannel=trigger['stopChannel'],
units='Boolean',
eventToSend='STOPRUN'))
config.write(config_file)
return True
def writeConductanceStimulusConfigurationFile(config_file,
sampling_rate,
duration,
channels,
reversal,
model=None):
config = lcg.XMLConfigurationFile(sampling_rate, duration)
ID = 0
realtime = True # Conductance stimulus requires a realtime engine for the moment.
if realtime:
config.add_entity(lcg.entities.H5Recorder(id=ID, connections=()))
ID += 1
input_channels = []
output_channels = []
used_outputs = []
used_outputs_ids = []
for chan in channels:
try:
chan = completeWithDefaultValues(chan)
except KeyError:
print('Each channel must contain a "type" key.')
return False
if chan['type'] == 'input':
input_channels.append(chan)
else:
output_channels.append(chan)
for ii in range(len(reversal)):
chan = input_channels[ii]
outchan = output_channels[ii]
if not outchan[
'channel'] in used_outputs: # To allow multiple conductances to the same neuron
config.add_entity(
lcg.entities.RealNeuron(
id=ID,
connections=(0),
spikeThreshold=-10,
V0=-65,
deviceFile=chan['device'],
inputSubdevice=chan['subdevice'],
outputSubdevice=outchan['subdevice'],
readChannel=chan['channel'],
writeChannel=outchan['channel'],
inputConversionFactor=chan['factor'],
outputConversionFactor=outchan['factor'],
inputRange=chan['range'],
reference=chan['reference'],
kernelFile='kernel.dat'))
config.add_entity(
lcg.entities.Waveform(id=ID + 1,
connections=(0, ID + 2),
filename=outchan['stimfile'],
units='nS'))
config.add_entity(
lcg.entities.ConductanceStimulus(id=ID + 2,
connections=(0, ID),
E=reversal[ii]))
used_outputs.append(outchan['channel'])
used_outputs_ids.append(ID)
ID += 3
else:
# Channel already used
neuron_id = used_outputs_ids[np.nonzero(
np.array(used_outputs) == outchan['channel'])[0]]
config.add_entity(
lcg.entities.Waveform(id=ID + 1,
connections=(0, ID + 2),
filename=outchan['stimfile'],
units='nS'))
config.add_entity(
lcg.entities.ConductanceStimulus(id=ID + 2,
connections=(0, neuron_id),
E=reversal[ii]))
ID += 2
for ii in range(len(input_channels)):
if ii > len(reversal):
chan = input_channels[ii]
config.add_entity(
lcg.entities.AnalogInput(id=ID,
connections=(0),
deviceFile=chan['device'],
inputSubdevice=chan['subdevice'],
readChannel=chan['channel'],
inputConversionFactor=chan['factor'],
range=chan['range'],
aref=chan['reference'],
units=chan['units']))
ID += 1
for ii in range(len(output_channels)):
if ii > len(reversal):
chan = output_channels[ii]
config.add_entity(
lcg.entities.AnalogOutput(id=ID,
connections=(),
deviceFile=chan['device'],
aref=chan['reference'],
units=chan['units'],
resetOutput=chan['resetOutput']))
ID += 1
config.write(config_file)
return True
def writeIOConfigurationFile(config_file,
sampling_rate,
duration,
channels,
realtime=True,
output_filename=None,
model=None):
config = lcg.XMLConfigurationFile(sampling_rate, duration, output_filename)
ID = 0
if realtime:
config.add_entity(
lcg.entities.H5Recorder(id=ID,
filename=output_filename,
connections=()))
ID += 1
for chan in channels:
try:
chan = completeWithDefaultValues(chan)
except KeyError:
print('Each channel must contain a "type" key.')
return False
if model is None:
if realtime:
if chan['type'] == 'input':
config.add_entity(
lcg.entities.AnalogInput(
id=ID,
connections=(0),
deviceFile=chan['device'],
inputSubdevice=chan['subdevice'],
readChannel=chan['channel'],
inputConversionFactor=chan['factor'],
range=chan['range'],
aref=chan['reference'],
units=chan['units']))
else:
config.add_entity(
lcg.entities.AnalogOutput(
id=ID,
connections=(),
deviceFile=chan['device'],
outputSubdevice=chan['subdevice'],
writeChannel=chan['channel'],
outputConversionFactor=chan['factor'],
aref=chan['reference'],
units=chan['units'],
resetOutput=chan['resetOutput']))
config.add_entity(
lcg.entities.Waveform(id=ID + 1,
connections=(0, ID),
filename=chan['stimfile'],
units=chan['units']))
ID += 1
else:
if chan['type'] == 'input':
config.add_stream(
lcg.streams.InputChannel(
id=ID,
connections=(),
device=chan['device'],
subdevice=chan['subdevice'],
channel=chan['channel'],
conversionFactor=chan['factor'],
range=chan['range'],
reference=chan['reference'],
units=chan['units'],
samplingRate=sampling_rate))
else:
config.add_stream(
lcg.streams.OutputChannel(
id=ID,
connections=(),
device=chan['device'],
subdevice=chan['subdevice'],
channel=chan['channel'],
conversionFactor=chan['factor'],
reference=chan['reference'],
units=chan['units'],
stimulusFile=chan['stimfile'],
samplingRate=sampling_rate,
offset=chan['offset'],
resetOutput=chan['resetOutput']))
elif model == 'LIF':
if chan['type'] == 'input':
config.add_entity(
lcg.entities.LIFNeuron(ID, (0),
C=0.08,
tau=0.0075,
tarp=0.0014,
Er=-65.2,
E0=-70,
Vth=-50,
Iext=0,
holdLastValue=False))
elif chan['type'] == 'output':
config.add_entity(
lcg.entities.Waveform(id=ID,
connections=(0, ID - 1),
filename=chan['stimfile'],
units=chan['units']))
ID += 1
config.write(config_file)
return True