def writeSinusoidsConfig(bg_current, modulating_current, G0_exc, sigma_exc, G0_inh, sigma_inh,
ai=0, ao=0, duration=30, outfile='sinusoids.xml', infile=''):
import shutil
writeIPlusBgGConfig(0, {'m': G0_exc, 's': sigma_exc, 'tau': 5, 'seed': 5061983},
{'m': G0_inh, 's': sigma_inh, 'tau': 10, 'seed': 7051983},
ai, ao, duration, outfile, infile)
if isinstance(bg_current, float) or isinstance(bg_current,int):
if bg_current == 0:
# we have only the sinusoidal modulating current
current = [[duration,3,modulating_current,'F',0,0,0,0,0,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
else:
# sinusoid on top of a DC current
current = [[duration,-2,bg_current,0,0,0,0,0,0,1,0,1],
[0,-2,modulating_current,'F',0,0,0,0,0,3,1,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
else:
if bg_current['mean'] == 0 and bg_current['std'] == 0:
# we have only the sinusoidal modulating current
current = [[duration,3,modulating_current,'F',0,0,0,0,0,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
else:
# sinusoid on top of a noisy (OU) current
current = [[duration,-2,bg_current['mean'],bg_current['std'],bg_current['tau'],0,0,1,5061983,2,0,1],
[0,-2,modulating_current,'F',0,0,0,0,0,3,1,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
os.remove('current.stim')
shutil.move('gexc.stim','gexc_template.stim')
shutil.move('ginh.stim','ginh_template.stim')
lcg.writeStimFile('current_template.stim',current,True)
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 run_batch(repetitions, interval, stim_dur, stim_amp, hyperpolarizing_pulse, pre, post,
ai, ao, pairing=False, offset=None):
try:
template_config_file = os.environ['CONFIGURATIONS_PATH'] + '/' + config_file
except:
template_config_file = os.environ['HOME'] + '/configurations/' + config_file
lcg.substituteStrings(template_config_file, config_file, {'TEND': pre+post,
'AI': ai,
'AO_INTRA': ao['intra'],
'AO_EXTRA': ao['extra']})
extra_stim = [[pre,1,0,0,0,0,0,0,0,0,0,1],
[stim_dur['extra'],1,stim_amp['extra'],0,0,0,0,0,0,0,0,1],
[post-stim_dur['extra'],1,0,0,0,0,0,0,0,0,0,1]]
lcg.writeStimFile(extracellular_stim_file, extra_stim, False)
if pairing:
intra_stim = [[pre+offset,1,0,0,0,0,0,0,0,0,0,1],
[stim_dur['intra'],1,stim_amp['intra'],0,0,0,0,0,0,0,0,1],
[post-offset-stim_dur['intra']-hyperpolarizing_pulse['dur']-0.1,1,0,0,0,0,0,0,0,0,0,1],
[hyperpolarizing_pulse['dur'],1,hyperpolarizing_pulse['amp'],0,0,0,0,0,0,0,0,1],
[0.1,1,0,0,0,0,0,0,0,0,0,1]]
else:
intra_stim = [[pre+post-hyperpolarizing_pulse['dur']-0.1,1,0,0,0,0,0,0,0,0,0,1],
[hyperpolarizing_pulse['dur'],1,hyperpolarizing_pulse['amp'],0,0,0,0,0,0,0,0,1],
[0.1,1,0,0,0,0,0,0,0,0,0,1]]
lcg.writeStimFile(intracellular_stim_file, intra_stim, False)
sub.call('lcg kernel -I ' + str(ai) + ' -O ' + str(ao['intra']), shell=True)
sub.call(lcg.common.prog_name + ' -c '+ config_file + ' -n ' + str(repetitions) +
' -i ' + str(interval-(pre+post)), shell=True)
def writeGainModulationConfig(G0_exc,
sigma_exc,
G0_inh,
sigma_inh,
ai=0,
ao=0,
duration=5,
outfile='gain_modulation.xml',
infile=''):
writeIPlusBgGConfig(0, {
'm': G0_exc,
's': sigma_exc,
'tau': 5
}, {
'm': G0_inh,
's': sigma_inh,
'tau': 10
}, ai, ao, duration + 3, outfile, infile)
os.remove('current.stim')
current = [[0.5, 1, 0, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[0.01, 1, -300, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[0.5, 1, 0, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[0.6, 1, -100, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[4, 1, 0, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[duration, 1, 'I', 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[1, 1, 0, 0, 0, 0, 0, 0, 3532765, 0, 0, 1]]
lcg.writeStimFile('template.stim', current, None)
def writeSinusoidsConfig(bg_current,
modulating_current,
G0_exc,
sigma_exc,
G0_inh,
sigma_inh,
ai=0,
ao=0,
duration=30,
outfile='sinusoids.xml',
infile=''):
import shutil
writeIPlusBgGConfig(0, {
'm': G0_exc,
's': sigma_exc,
'tau': 5,
'seed': 5061983
}, {
'm': G0_inh,
's': sigma_inh,
'tau': 10,
'seed': 7051983
}, ai, ao, duration, outfile, infile)
if isinstance(bg_current, float) or isinstance(bg_current, int):
if bg_current == 0:
# we have only the sinusoidal modulating current
current = [[
duration, 3, modulating_current, 'F', 0, 0, 0, 0, 0, 0, 0, 1
], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
else:
# sinusoid on top of a DC current
current = [[duration, -2, bg_current, 0, 0, 0, 0, 0, 0, 1, 0, 1],
[
0, -2, modulating_current, 'F', 0, 0, 0, 0, 0, 3, 1,
1
], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
else:
if bg_current['mean'] == 0 and bg_current['std'] == 0:
# we have only the sinusoidal modulating current
current = [[
duration, 3, modulating_current, 'F', 0, 0, 0, 0, 0, 0, 0, 1
], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
else:
# sinusoid on top of a noisy (OU) current
current = [[
duration, -2, bg_current['mean'], bg_current['std'],
bg_current['tau'], 0, 0, 1, 5061983, 2, 0, 1
], [0, -2, modulating_current, 'F', 0, 0, 0, 0, 0, 3, 1, 1],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
os.remove('current.stim')
shutil.move('gexc.stim', 'gexc_template.stim')
shutil.move('ginh.stim', 'ginh_template.stim')
lcg.writeStimFile('current_template.stim', current, True)
def writefIStim(Imin, Imax, Istep, noisy=False):
A = [[0.5, 1, 0, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[0.01, 1, -300, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[0.5, 1, 0, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[0.6, 1, -100, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[1, 1, 0, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[5, 1, 500, 0, 0, 0, 0, 0, 3532765, 0, 0, 1],
[0.1, 1, 0, 0, 0, 0, 0, 0, 3532765, 0, 0, 1]]
if noisy:
A[4, 0] = 4
for k, i in np.arange(Imin, Imax + Istep, Istep):
A[5, 2] = i
lcg.writeStimFile('fi_%02d.stim' % (k + 1), A, None)
def writeGainModulationConfig(G0_exc, sigma_exc, G0_inh, sigma_inh, ai=0, ao=0, duration=5, outfile='gain_modulation.xml', infile=''):
writeIPlusBgGConfig(0, {'m': G0_exc, 's': sigma_exc, 'tau': 5},
{'m': G0_inh, 's': sigma_inh, 'tau': 10},
ai, ao, duration+3, outfile, infile)
os.remove('current.stim')
current = [[0.5,1,0,0,0,0,0,0,3532765,0,0,1],
[0.01,1,-300,0,0,0,0,0,3532765,0,0,1],
[0.5,1,0,0,0,0,0,0,3532765,0,0,1],
[0.6,1,-100,0,0,0,0,0,3532765,0,0,1],
[4,1,0,0,0,0,0,0,3532765,0,0,1],
[duration,1,'I',0,0,0,0,0,3532765,0,0,1],
[1,1,0,0,0,0,0,0,3532765,0,0,1]]
lcg.writeStimFile('template.stim',current,None)
def writefIStim(Imin,Imax,Istep,noisy=False):
A = [[0.5,1,0,0,0,0,0,0,3532765,0,0,1],
[0.01,1,-300,0,0,0,0,0,3532765,0,0,1],
[0.5,1,0,0,0,0,0,0,3532765,0,0,1],
[0.6,1,-100,0,0,0,0,0,3532765,0,0,1],
[1,1,0,0,0,0,0,0,3532765,0,0,1],
[5,1,500,0,0,0,0,0,3532765,0,0,1],
[0.1,1,0,0,0,0,0,0,3532765,0,0,1]]
if noisy:
A[4,0] = 4
for k,i in np.arange(Imin,Imax+Istep,Istep):
A[5,2] = i
lcg.writeStimFile('fi_%02d.stim' % (k+1), A, None)
def writeGStimFiles(Gexc, Ginh, duration, before, after, outfiles = ['gexc.stim','ginh.stim']):
G = [[[before,1,0,0,0,0,0,0,0,0,0,1],
[duration,2,Gexc['m'],Gexc['s'],Gexc['tau'],0,0,1,np.random.poisson(10000),0,0,1],
[after,1,0,0,0,0,0,0,0,0,0,1]],
[[before,1,0,0,0,0,0,0,0,0,0,1],
[duration,2,Ginh['m'],Ginh['s'],Ginh['tau'],0,0,1,np.random.poisson(10000),0,0,1],
[after,1,0,0,0,0,0,0,0,0,0,1]]]
if 'seed' in Gexc:
G[0][1][8] = Gexc['seed']
if 'seed' in Ginh:
G[1][1][8] = Ginh['seed']
for i,filename in enumerate(outfiles):
lcg.writeStimFile(filename,G[i],None)
def main():
opts = parseArgs()
writeConfigurationFile(opts)
ratio = lcg.computeRatesRatio(Vm=opts['balanced_voltage'],
Rin=opts['input_resistance'])
Gm_exc, Gm_inh, Gs_exc, Gs_inh = lcg.computeSynapticBackgroundCoefficients(
ratio, R_exc=opts['R_exc'], Rin=opts['input_resistance'])
gexc = [[opts['pre'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
ginh = [[opts['pre'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
for dr in opts['dR']:
if opts['exc']:
gm = Gm_exc * (1 + dr)
gs = Gs_exc * np.sqrt(1 + dr)
gexc.append([
opts['step_duration'], 2, gm, gs, 5, Gm_exc, 0, 0,
int(np.random.uniform(high=10000)), 0, 0, 1
])
ginh.append([
opts['step_duration'], 2, Gm_inh, Gs_inh, 10, Gm_inh, 0, 0,
int(np.random.uniform(high=10000)), 0, 0, 1
])
else:
gm = Gm_inh * (1 + dr)
gs = Gs_inh * np.sqrt(1 + dr)
gexc.append([
opts['step_duration'], 2, Gm_exc, Gs_exc, 5, 0, 0, 0,
int(np.random.uniform(high=10000)), 0, 0, 1
])
ginh.append([
opts['step_duration'], 2, gm, gs, 10, Gm_inh, 0, 0,
int(np.random.uniform(high=10000)), 0, 0, 1
])
gexc.append([opts['post'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
ginh.append([opts['post'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
lcg.writeStimFile(gexc_file, gexc, False)
lcg.writeStimFile(ginh_file, ginh, False)
while opts['reps'] > 0:
reps = min(opts['reps'], opts['kernel_frequency'])
sub.call('lcg kernel -I ' + str(opts['ai']) + ' -O ' + str(opts['ao']),
shell=True)
sub.call(lcg.common.prog_name + ' -c ' + config_file + ' -n ' +
str(reps) + ' -i ' + str(opts['interval']),
shell=True)
opts['reps'] -= reps
if opts['reps'] > 0:
pass
sub.call(['sleep', str(opts['interval'])])
def writePulsesStimFile(f, dur, amp, N=10, delay=1, pulsesInBurst=1, withRecovery=True, filename='pulses.stim'):
"""
Writes a stimulation file containing a series of pulses, with an optional "recovery" pulse.
Parameters:
f - frequency of the stimulation
dur - duration of the stimulation (!!! in ms !!!)
amp - amplitude of the stimulation
N - number of repetitions
delay - initial and final delay
pulsesInBurst - number of pulses in each group
withRecovery - whether or not to include a recovery pulse
filename - file to write to
Returns:
The duration of the stimulation.
"""
stimulus = [[delay,1,0,0,0,0,0,0,5061983,0,0,1]]
if type(f) != list:
f = [f]
if len(f) == 1:
f = f*2
stimulus.append([N/f[1],-2,amp,-f[0],dur,0,0,0,5061983,8,0,1])
stimulus.append([0,-2,1,-f[1],pulsesInBurst*(1000./f[0]),0,0,0,5061983,8,2,1])
if withRecovery:
stimulus.append([0.5,1,0,0,0,0,0,0,5061983,0,0,1])
stimulus.append([dur,1,amp,0,0,0,0,0,5061983,0,0,1])
stimulus.append([delay,1,0,0,0,0,0,0,5061983,0,0,1])
dur = lcg.writeStimFile(filename, stimulus, None)
return dur
def main():
if len(sys.argv) < 2 or sys.argv[1] in ('-h', '--help', 'help'):
usage()
sys.exit(0)
opts = parseArgs()
ncells = len(opts['ao'])
stim_file = []
for ii in range(ncells):
stim_file.append('ou_prot%d_%d.stim' %
(opts['ai'][ii], opts['ao'][ii]))
stimulus = create_ou_stimulus(opts['mean'], opts['std'], opts['tau'],
opts['duration'], opts['tail'],
opts['preamble'])
lcg.writeStimFile(stim_file[ii], stimulus, opts['preamble'])
if len(opts['holding']) < 2:
opts['holding'] = opts['holding'] * 2
for ai, ao, stim in zip(opts['ai'], opts['ao'], stim_file):
if opts['kernel']:
sub.call('lcg output ' + str(opts['holding'][0]), shell=True)
if not opts['holding'][0] in [0]:
sleep(2)
if ncells > 1:
sub.call('lcg kernel -F ' + str(opts['srate']) + ' -H ' +
str(opts['holding'][0]) + ' -a',
shell=True)
else:
sub.call('lcg kernel -F ' + str(opts['srate']) + ' -H ' +
str(opts['holding'][0]),
shell=True)
if not opts['holding'][0] == opts['holding'][1]:
sleep(2)
sub.call('lcg-stimulus -s ' + stim + ' -i ' + str(opts['interval']) +
' -F ' + str(opts['srate']) + ' -H ' +
str(opts['holding'][1]),
shell=True)
if opts['report']:
isim, cv, Vm, Vsd = analyse_last_file()
print('Report from the last file')
print('Mean firing rate: {0}'.format(1. / isim))
print('Coefficient of variation: {0}'.format(cv))
print('Mean membrane voltage (Vm): {0}'.format(Vm))
print('Standard deviation of Vm: {0}'.format(Vsd))
def main():
if len(sys.argv) < 1:
print(help.format(os.path.basename(sys.argv[0])))
sys.exit(0)
opts = parseArgs()
if not (len(opts['amp']) == len(opts['pw']) == len(opts['freq'])):
print(
'amplitude, pulsewidth and frequency must have the same number of arguments.'
)
print(help.format(os.path.basename(sys.argv[0])))
sys.exit(1)
N = len(opts['amp'])
ntrials = np.ceil(opts['nreps'] / N)
fnames = ['intra.stim', 'digi.stim']
filename = 'simultaneous.cfg'
if not len(opts['ao']) == len(fnames):
print('Must have 2 and only 2 AO.')
sys.exit(1)
cfgFile = createCfg(opts['ai'], opts['ao'], fnames)
with open(filename, "w") as fd:
fd.write(cfgFile)
if opts['kernel']:
sub.call('lcg kernel -I ' + str(opts['ai'][0]) + ' -O ' +
str(opts['ao'][0]) + ' -F ' + str(opts['srate']) +
' -d 25 -s 150',
shell=True)
for ii in np.arange(ntrials):
print('Trial {} of {}.'.format(int(ii), int(ntrials)))
for nn in np.arange(N):
lcg.writeStimFile(fnames[0],
createIntraStim(opts['dur'], opts['amp'][nn],
opts['freq'][nn], opts['pw'][nn],
opts['r_in']),
addDefaultPreamble=False)
lcg.writeStimFile(fnames[1],
createDigiStim(opts['dur'], opts['r_in']),
addDefaultPreamble=False)
sub.call('lcg-non-rt -c ' + filename + ' -F ' + str(opts['srate']),
shell=True)
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)
def writeGStimFiles(Gexc,
Ginh,
duration,
before,
after,
outfiles=['gexc.stim', 'ginh.stim']):
G = [[[before, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[
duration, 2, Gexc['m'], Gexc['s'], Gexc['tau'], 0, 0, 1,
np.random.poisson(10000), 0, 0, 1
], [after, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]],
[[before, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[
duration, 2, Ginh['m'], Ginh['s'], Ginh['tau'], 0, 0, 1,
np.random.poisson(10000), 0, 0, 1
], [after, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]]
if 'seed' in Gexc:
G[0][1][8] = Gexc['seed']
if 'seed' in Ginh:
G[1][1][8] = Ginh['seed']
for i, filename in enumerate(outfiles):
lcg.writeStimFile(filename, G[i], None)
def main():
if len(sys.argv) < 2 or sys.argv[1] in ('-h','--help','help'):
usage()
sys.exit(0)
opts = parseArgs()
ncells = len(opts['ao'])
stim_file = []
for ii in range(ncells):
stim_file.append('ou_prot%d_%d.stim'%(opts['ai'][ii],opts['ao'][ii]))
stimulus = create_ou_stimulus(opts['mean'],opts['std'],
opts['tau'],opts['duration'],
opts['tail'],opts['preamble'])
lcg.writeStimFile(stim_file[ii], stimulus, opts['preamble'])
if len(opts['holding']) < 2:
opts['holding'] = opts['holding']*2
for ai,ao,stim in zip(opts['ai'],opts['ao'],stim_file):
if opts['kernel']:
sub.call('lcg output ' + str(opts['holding'][0]), shell=True)
if not opts['holding'][0] in [0]:
sleep(2)
if ncells>1:
sub.call('lcg kernel -F '+ str(opts['srate']) + ' -H ' + str(opts['holding'][0]) + ' -a', shell=True)
else:
sub.call('lcg kernel -F '+ str(opts['srate']) + ' -H ' + str(opts['holding'][0]), shell=True)
if not opts['holding'][0] == opts['holding'][1]:
sleep(2)
sub.call('lcg-stimulus -s ' + stim + ' -i ' + str(opts['interval']) +
' -F ' + str(opts['srate']) + ' -H ' + str(opts['holding'][1]), shell=True)
if opts['report']:
isim,cv,Vm,Vsd = analyse_last_file()
print('Report from the last file')
print('Mean firing rate: {0}'.format(1./isim))
print('Coefficient of variation: {0}'.format(cv))
print('Mean membrane voltage (Vm): {0}'.format(Vm))
print('Standard deviation of Vm: {0}'.format(Vsd))
def main():
if len(sys.argv) < 1:
print(help.format(os.path.basename(sys.argv[0])))
sys.exit(0)
opts = parseArgs()
if not (len(opts['amp']) == len(opts['pw']) == len(opts['freq'])):
print('amplitude, pulsewidth and frequency must have the same number of arguments.')
print(help.format(os.path.basename(sys.argv[0])))
sys.exit(1)
N = len(opts['amp'])
ntrials = np.ceil(opts['nreps']/N)
fnames = ['intra.stim','digi.stim']
filename = 'simultaneous.cfg'
if not len(opts['ao']) == len(fnames):
print('Must have 2 and only 2 AO.')
sys.exit(1)
cfgFile = createCfg(opts['ai'],opts['ao'],fnames)
with open(filename, "w") as fd:
fd.write(cfgFile)
if opts['kernel']:
sub.call('lcg kernel -I ' + str(opts['ai'][0]) + ' -O ' + str(opts['ao'][0]) +
' -F '+ str(opts['srate']) + ' -d 25 -s 150', shell=True)
for ii in np.arange(ntrials):
print('Trial {} of {}.'.format(int(ii),int(ntrials)))
for nn in np.arange(N):
lcg.writeStimFile(fnames[0],createIntraStim(opts['dur'],
opts['amp'][nn],
opts['freq'][nn],
opts['pw'][nn],
opts['r_in']),
addDefaultPreamble=False)
lcg.writeStimFile(fnames[1],createDigiStim(opts['dur'],
opts['r_in']),
addDefaultPreamble=False)
sub.call('lcg-non-rt -c ' + filename + ' -F '+ str(opts['srate']), shell=True)
def writePulsesStimFile(f,
dur,
amp,
N=10,
delay=1,
pulsesInBurst=1,
withRecovery=True,
filename='pulses.stim'):
"""
Writes a stimulation file containing a series of pulses, with an optional "recovery" pulse.
Parameters:
f - frequency of the stimulation
dur - duration of the stimulation (!!! in ms !!!)
amp - amplitude of the stimulation
N - number of repetitions
delay - initial and final delay
pulsesInBurst - number of pulses in each group
withRecovery - whether or not to include a recovery pulse
filename - file to write to
Returns:
The duration of the stimulation.
"""
stimulus = [[delay, 1, 0, 0, 0, 0, 0, 0, 5061983, 0, 0, 1]]
if type(f) != list:
f = [f]
if len(f) == 1:
f = f * 2
stimulus.append([N / f[1], -2, amp, -f[0], dur, 0, 0, 0, 5061983, 8, 0, 1])
stimulus.append([
0, -2, 1, -f[1], pulsesInBurst * (1000. / f[0]), 0, 0, 0, 5061983, 8,
2, 1
])
if withRecovery:
stimulus.append([0.5, 1, 0, 0, 0, 0, 0, 0, 5061983, 0, 0, 1])
stimulus.append([dur, 1, amp, 0, 0, 0, 0, 0, 5061983, 0, 0, 1])
stimulus.append([delay, 1, 0, 0, 0, 0, 0, 0, 5061983, 0, 0, 1])
dur = lcg.writeStimFile(filename, stimulus, None)
return dur
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)
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)
Gm_exc,Gm_inh,Gs_exc,Gs_inh = lcg.computeSynapticBackgroundCoefficients(R_exc/R_inh,R_exc,Rin=input_resistance)
conductance[1][2] = Gm_exc
conductance[1][3] = Gs_exc
conductance[1][4] = tau_exc
lcg.writeStimFile(gexc_stim_file, conductance, False)
conductance[1][2] = Gm_inh
conductance[1][3] = Gs_inh
conductance[1][4] = tau_inh
lcg.writeStimFile(ginh_stim_file, conductance, False)
for _ in range(nreps):
if kernel:
sub.call('lcg kernel -I %d -O %d -F %g -H %g --rt yes' % (ai,ao,sampling_rate,holding), shell=True)
for i,amp in enumerate(amplitudes):
stimulus[row][2] = amp + holding
lcg.writeStimFile(stim_file, stimulus, with_preamble, preamble_holding=holding)
sub.call(lcg.common.prog_name + ' -V 4 -c ' + config_file, shell=True)
if i < nreps*len(amplitudes)-1:
sub.call(['sleep', str(interval)])
print e
sys.exit(1)
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]]
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]]
row = 1
for i,amp in enumerate(amplitudes):
stimulus[row][2] = amp + holding
lcg.writeStimFile('%s/step_%02d.stim' % (stimuli_directory,i+1), stimulus,
with_preamble, preamble_holding=holding)
if kernel:
sub.call('lcg kernel -I %d -O %d -F %g -H %g --rt %s' % (ai,ao,samplf,holding,realtime), shell=True)
sub.call('lcg stimulus -d %s -i %g -I %d -O %d -n %d -F %g --rt %s %s' %
(stimuli_directory,
interval,ai,ao,
nreps,samplf,
realtime,
model), shell=True)
if __name__ == '__main__':
main()
sys.exit(1)
if withRecovery and pause <= 0:
print('The interval between pulse train and recovery pulse must positive.')
sys.exit(1)
if computeKernel:
sub.call('lcg kernel -F ' + str(samplingRate) + ' -I ' + str(analogInput[0]) + ' -O ' + str(analogOutput) + \
' --input-factor ' + str(inputGains[0]) + ' --output-factor ' + str(outputGain) + \
' --input-units ' + inputUnits[0] + ' --output-units ' + outputUnits + ' --non-rt', shell=True)
stim = [[pre,1,0,0,0,0,0,0,0,0,0,1],
[nPulses/stimulusFrequency,8,stimulusAmplitude,-stimulusFrequency,stimulusDuration,0,0,0,0,0,0,1]]
if withRecovery:
stim.append([pause-1./stimulusFrequency,1,0,0,0,0,0,0,0,0,0,1])
stim.append([stimulusDuration*1e-3,1,stimulusAmplitude,0,0,0,0,0,0,0,0,1])
stim.append([post,1,0,0,0,0,0,0,0,0,0,1])
totalDuration = lcg.writeStimFile(stimFile, stim, False)
channels = [{'type':'input', 'channel':analogInput[0], 'factor':inputGains[0], 'units':inputUnits[0]},
{'type':'input', 'channel':analogInput[1], 'factor':inputGains[1], 'units':inputUnits[1]},
{'type':'output', 'channel':analogOutput, 'factor':outputGain, 'units':outputUnits, 'stimfile':stimFile}]
lcg.writeIOConfigurationFile(configFile,samplingRate,totalDuration,channels)
for i in range(nTrials):
sub.call(lcg.common.prog_name + ' -c ' + configFile, shell=True)
if i < nTrials-1:
sub.call('sleep ' + str(interTrialInterval), shell=True)
if __name__ == '__main__':
main()
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]]
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],
]
row = 1
for i, amp in enumerate(amplitudes):
stimulus[row][2] = amp + holding
lcg.writeStimFile(
"%s/step_%02d.stim" % (stimuli_directory, i + 1), stimulus, with_preamble, preamble_holding=holding
)
if kernel:
sub.call("lcg kernel -I %d -O %d -F %g -H %g --rt %s" % (ai, ao, samplf, holding, realtime), shell=True)
sub.call(
"lcg stimulus -d %s -i %g -I %d -O %d -n %d -F %g --rt %s %s"
% (stimuli_directory, interval, ai, ao, nreps, samplf, realtime, model),
shell=True,
)
if __name__ == "__main__":
main()
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)
def main():
mode = None
if len(sys.argv) < 2 or sys.argv[1] in ('-h','--help','help'):
usage()
sys.exit(0)
mode = sys.argv[1]
if mode != 'current' and mode != 'conductance':
print('Unknown working mode: [%s].' % sys.argv[1])
sys.exit(1)
opts = parseGlobalArgs()
if mode == 'current':
opts = dict(parseCurrentModeArgs(), **opts)
if opts['separate'] and (type(opts['ao']) == int or type(opts['ai']) == int or \
len(opts['ao']) == 1 or len(opts['ai']) == 1):
print('When using the --separate options, two channels must be specified (use the -I and -O options).')
sys.exit(1)
else:
opts = dict(parseConductanceModeArgs(), **opts)
if (mode == 'current' and opts['with_bg']) or mode == 'conductance':
ratio = lcg.computeRatesRatio(Vm=opts['balanced_voltage'], Rin=opts['input_resistance'])
Gm_exc,Gm_inh,Gs_exc,Gs_inh = lcg.computeSynapticBackgroundCoefficients(ratio, R_exc=opts['R_exc'], Rin=opts['input_resistance'])
opts['Gm_exc'] = Gm_exc
opts['Gm_inh'] = Gm_inh
else:
# no background conductances, it will be a current-clamp experiment
gexc = None
ginh = None
if mode == 'current':
if opts['with_bg']:
# background conductances only
gexc = [[2.61,1,0,0,0,0,0,0,0,0,0,1],
[opts['duration'],2,Gm_exc,Gs_exc,5,0,0,1,random_seed,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
ginh = [[2.61,1,0,0,0,0,0,0,0,0,0,1],
[opts['duration'],2,Gm_inh,Gs_inh,10,0,0,1,random_seed,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
if opts['mean'] == 0 and opts['std'] == 0 and opts['tau'] == 0:
# sinusoidal modulating current only
if opts['separate']:
current = [[opts['duration'],1,0,0,0,0,0,1,random_seed,0,0,1], # OU current
[1,1,0,0,0,0,0,0,0,0,0,1]]
modulation = [[opts['duration'],3,opts['I_modul'],frequency_value,0,0,0,0,0,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
else:
current = [[opts['duration'],3,opts['I_modul'],frequency_value,0,0,0,0,0,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
elif opts['tau'] == 0:
# sinusoid on top of a DC current
if opts['separate']:
current = [[opts['duration'],1,opts['mean'],0,0,0,0,0,random_seed,0,0,1], # OU current
[1,1,0,0,0,0,0,0,0,0,0,1]]
modulation = [[opts['duration'],3,opts['I_modul'],frequency_value,0,0,0,0,0,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
else:
current = [[opts['duration'],-2,opts['mean'],0,0,0,0,0,0,1,0,1],
[0,-2,opts['I_modul'],frequency_value,0,0,0,0,0,3,1,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
else:
# sinusoid on top of a noisy (OU) current
if opts['separate']:
current = [[opts['duration'],2,opts['mean'],opts['std'],opts['tau'],0,0,1,random_seed,0,0,1], # OU current
[1,1,0,0,0,0,0,0,0,0,0,1]]
modulation = [[opts['duration'],3,opts['I_modul'],frequency_value,0,0,0,0,0,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
else:
current = [[opts['duration'],-2,opts['mean'],opts['std'],opts['tau'],0,0,1,random_seed,2,0,1], # OU current
[0,-2,opts['I_modul'],frequency_value,0,0,0,0,0,3,1,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
else:
# current just for the preamble
current = [[opts['duration']+1,1,0,0,0,0,0,0,0,0,0,1]]
# conductances
opts['R_inh'] = opts['R_exc']/ratio
if opts['exc']:
gexc = createSinusoidallyModOU(frequency_value, 'exc', random_seed, opts)
else:
gexc = [{'matrix': [[2.61,1,0,0,0,0,0,0,0,0,0,1],
[opts['duration'],2,Gm_exc,Gs_exc,5,0,0,1,random_seed,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]], 'filename': 'gexc.stim'}]
if opts['inh']:
ginh = createSinusoidallyModOU(frequency_value, 'inh', random_seed, opts)
else:
ginh = [{'matrix': [[2.61,1,0,0,0,0,0,0,0,0,0,1],
[opts['duration'],2,Gm_inh,Gs_inh,10,0,0,1,random_seed,0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]], 'filename': 'ginh.stim'}]
if gexc and ginh:
writeConfigurationFile(opts)
cnt = 0
tot = opts['reps']*len(opts['frequencies'])
for i in range(opts['reps']):
np.random.shuffle(opts['frequencies'])
for f in opts['frequencies']:
if opts['compute_kernel'] and cnt%opts['kernel_frequency'] == 0:
if 'separate' in opts and opts['separate']:
# compute a kernel for each channel
sub.call('lcg kernel -I ' + str(opts['ai'][0]) + ' -O ' + str(opts['ao'][0]) +
' -F '+ str(opts['sampling_rate']) + ' --non-rt --append', shell=True)
sub.call('lcg kernel -I ' + str(opts['ai'][1]) + ' -O ' + str(opts['ao'][1]) +
' -F '+ str(opts['sampling_rate']) + ' --non-rt --append', shell=True)
pass
else:
sub.call('lcg kernel -I ' + str(opts['ai']) + ' -O ' + str(opts['ao']) +
' -F '+ str(opts['sampling_rate']), shell=True)
cnt = cnt+1
print('[%02d/%02d] F = %g Hz.' % (cnt,tot,f))
I = copy.deepcopy(current)
replaceValue(I, random_seed)
replaceValue(I, frequency_value, f)
lcg.writeStimFile(current_file, I, preamble=True)
if 'separate' in opts and opts['separate']:
I = copy.deepcopy(modulation)
replaceValue(I, frequency_value, f)
lcg.writeStimFile(modulation_file, I, preamble=[0,0])
if gexc and ginh:
for stimulus in gexc:
G = copy.deepcopy(stimulus['matrix'])
replaceValue(G, random_seed)
replaceValue(G, frequency_value, f)
lcg.writeStimFile(stimulus['filename'], G)
for stimulus in ginh:
G = copy.deepcopy(stimulus['matrix'])
replaceValue(G, random_seed)
replaceValue(G, frequency_value, f)
lcg.writeStimFile(stimulus['filename'], G)
sub.call(lcg.common.prog_name + ' -V 3 -c ' + config_file, shell=True)
else:
if 'separate' in opts and opts['separate']:
channels = [{'type':'input', 'channel':opts['ai'][0]},{'type':'input', 'channel':opts['ai'][1]},
{'type':'output', 'channel':opts['ao'][0], 'stimfile':current_file},
{'type':'output', 'channel':opts['ao'][1], 'stimfile':modulation_file}]
lcg.writeIOConfigurationFile(config_file,opts['sampling_rate'],opts['duration']+3.61,channels)
sub.call(lcg.common.prog_name + ' -c ' + config_file, shell=True)
else:
sub.call('lcg stimulus -s ' + current_file + ' -F '+ str(opts['sampling_rate']), shell=True)
if cnt != tot:
sub.call(['sleep', str(opts['interval'])])
' --input-units ' + inputUnits[0] + ' --output-units ' + outputUnits + ' --non-rt', shell=True)
stim = [[pre, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[
nPulses / stimulusFrequency, 8, stimulusAmplitude,
-stimulusFrequency, stimulusDuration, 0, 0, 0, 0, 0, 0, 1
]]
if withRecovery:
stim.append(
[pause - 1. / stimulusFrequency, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
stim.append([
stimulusDuration * 1e-3, 1, stimulusAmplitude, 0, 0, 0, 0, 0, 0, 0,
0, 1
])
stim.append([post, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
totalDuration = lcg.writeStimFile(stimFile, stim, False)
channels = [{
'type': 'input',
'channel': analogInput[0],
'factor': inputGains[0],
'units': inputUnits[0]
}, {
'type': 'input',
'channel': analogInput[1],
'factor': inputGains[1],
'units': inputUnits[1]
}, {
'type': 'output',
'channel': analogOutput,
'factor': outputGain,
'units': outputUnits,
def main():
mode = None
if len(sys.argv) < 2 or sys.argv[1] in ('-h','--help','help'):
usage()
sys.exit(0)
mode = sys.argv[1]
if mode != 'current' and mode != 'conductance':
print('Unknown working mode: [%s].' % sys.argv[1])
sys.exit(1)
opts = parseGlobalArgs()
if mode == 'current':
opts = dict(parseCurrentModeArgs(), **opts)
tot = len(opts['correlation_coefficients']) * opts['reps']
stim = [[opts['before'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[opts['duration'], -2, opts['current_mean'], 0, opts['current_tau'], 0, 0, 1, 0, 2, 0, 1], # indipendent part
[0, -2, 0, 0, opts['current_tau'], 0, 0, 1, 0, 2, 1, 1], # common part
[opts['after'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
I = {'m': 0, 's': 0, 'mc': 0, 'sc': 0}
np.random.seed(5061983)
I_seeds = map(int, np.random.uniform(low=0, high=10000, size=opts['reps']))
else:
opts = dict(parseConductanceModeArgs(), **opts)
writeConfigFile(opts)
tot = len(opts['balanced_voltages']) * len(opts['correlation_coefficients']) * opts['reps']
stim = [[opts['before'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[opts['duration'], 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],
[opts['after'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
gampa = {'m': 0, 's': 0, 'mc': 0, 'sc': 0}
ggaba = {'m': 0, 's': 0, 'mc': 0, 'sc': 0}
np.random.seed(5061983)
ampa_seeds = map(int, np.random.uniform(low=0, high=10000, size=opts['reps']))
np.random.seed(7051983)
gaba_seeds = map(int, np.random.uniform(low=0, high=10000, size=opts['reps']))
if opts['with_nmda']:
np.random.seed(723587)
nmda_seeds = map(int, np.random.uniform(low=0, high=10000, size=opts['reps']))
np.random.seed(int(time.time()))
np.random.shuffle(opts['correlation_coefficients'])
cnt = 0
for c in opts['correlation_coefficients']:
if mode == 'current':
stim[1][3] = np.sqrt(1-c)*opts['current_std']
stim[2][3] = np.sqrt(c)*opts['current_std']
for k in range(opts['reps']):
if cnt%opts['kernel_frequency'] == 0:
sub.call('lcg kernel -I ' + str(opts['ai']) + ' -O ' + str(opts['ao']), shell=True)
stim[1][8] = int(np.random.uniform(low=0, high=100*opts['reps']))
stim[2][8] = I_seeds[k]
lcg.writeStimFile(stim_files['current'], stim, False)
sub.call('lcg vcclamp -V 4 -f ' + stim_files['current'], shell=True)
sub.call(['sleep', str(opts['interval'])])
cnt = cnt+1
if cnt%10 == 0:
print('[%02d/%02d]' % (cnt,tot))
else:
np.random.shuffle(opts['balanced_voltages'])
for v in opts['balanced_voltages']:
ratio = lcg.computeRatesRatio(Vm=v, Rin=opts['input_resistance'])
gampa['m'],ggaba['m'],gampa['s'],ggaba['s'] = lcg.computeSynapticBackgroundCoefficients(
ratio,R_exc=ratio*(1-c)*opts['R_inh'],Rin=opts['input_resistance'])
gampa['mc'],ggaba['mc'],gampa['sc'],ggaba['sc'] = lcg.computeSynapticBackgroundCoefficients(
ratio,R_exc=ratio*c*opts['R_inh'],Rin=opts['input_resistance'])
for k in range(opts['reps']):
current_ampa_seed = int(np.random.uniform(low=0, high=100*opts['reps']))
stim[1][2] = gampa['m']
stim[1][3] = gampa['s']
stim[1][4] = 5
stim[1][8] = current_ampa_seed
lcg.writeStimFile(stim_files['gampa'], stim, False)
stim[1][2] = ggaba['m']
stim[1][3] = ggaba['s']
stim[1][4] = 10
stim[1][8] = int(np.random.uniform(low=0, high=100*opts['reps']))
lcg.writeStimFile(stim_files['ggaba'], stim, False)
stim[1][2] = gampa['mc']
stim[1][3] = gampa['sc']
stim[1][4] = 5
stim[1][8] = ampa_seeds[k]
lcg.writeStimFile(stim_files['gampa_common'], stim, False)
stim[1][2] = ggaba['mc']
stim[1][3] = ggaba['sc']
stim[1][4] = 10
stim[1][8] = gaba_seeds[k]
lcg.writeStimFile(stim_files['ggaba_common'], stim, False)
if opts['with_nmda']:
stim[1][2] = gampa['m']
stim[1][3] = gampa['s']
stim[1][4] = opts['nmda_tau']
stim[1][8] = current_ampa_seed
lcg.writeStimFile(stim_files['gnmda'], stim, False)
stim[1][2] = gampa['m']
stim[1][3] = gampa['s']
stim[1][4] = opts['nmda_tau']
stim[1][8] = ampa_seeds[k]
lcg.writeStimFile(stim_files['gnmda_common'], stim, False)
if cnt%opts['kernel_frequency'] == 0:
sub.call('lcg kernel -I ' + str(opts['ai']) + ' -O ' + str(opts['ao']), shell=True)
cnt = cnt+1
sub.call(lcg.common.prog_name + ' -V 4 -c ' + config_file, shell=True)
sub.call(['sleep', str(opts['interval'])])
if cnt%10 == 0:
print('[%02d/%02d]' % (cnt,tot))
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 main():
mode = None
if len(sys.argv) < 2 or sys.argv[1] in ('-h', '--help', 'help'):
usage()
sys.exit(0)
mode = sys.argv[1]
if mode != 'current' and mode != 'conductance':
print('Unknown working mode: [%s].' % sys.argv[1])
sys.exit(1)
opts = parseGlobalArgs()
if mode == 'current':
opts = dict(parseCurrentModeArgs(), **opts)
tot = len(opts['correlation_coefficients']) * opts['reps']
stim = [
[opts['before'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[
opts['duration'], -2, opts['current_mean'], 0,
opts['current_tau'], 0, 0, 1, 0, 2, 0, 1
], # indipendent part
[0, -2, 0, 0, opts['current_tau'], 0, 0, 1, 0, 2, 1,
1], # common part
[opts['after'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
]
I = {'m': 0, 's': 0, 'mc': 0, 'sc': 0}
np.random.seed(5061983)
I_seeds = map(int,
np.random.uniform(low=0, high=10000, size=opts['reps']))
else:
opts = dict(parseConductanceModeArgs(), **opts)
writeConfigFile(opts)
tot = len(opts['balanced_voltages']) * len(
opts['correlation_coefficients']) * opts['reps']
stim = [[opts['before'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[opts['duration'], 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],
[opts['after'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
gampa = {'m': 0, 's': 0, 'mc': 0, 'sc': 0}
ggaba = {'m': 0, 's': 0, 'mc': 0, 'sc': 0}
np.random.seed(5061983)
ampa_seeds = map(
int, np.random.uniform(low=0, high=10000, size=opts['reps']))
np.random.seed(7051983)
gaba_seeds = map(
int, np.random.uniform(low=0, high=10000, size=opts['reps']))
if opts['with_nmda']:
np.random.seed(723587)
gnmda = {'m': 0, 's': 0, 'mc': 0, 'sc': 0}
nmda_seeds = map(
int, np.random.uniform(low=0, high=10000, size=opts['reps']))
np.random.seed(int(time.time()))
np.random.shuffle(opts['correlation_coefficients'])
cnt = 0
for c in opts['correlation_coefficients']:
if mode == 'current':
stim[1][3] = np.sqrt(1 - c) * opts['current_std']
stim[2][3] = np.sqrt(c) * opts['current_std']
for k in range(opts['reps']):
if cnt % opts['kernel_frequency'] == 0:
sub.call('lcg kernel -I ' + str(opts['ai']) + ' -O ' +
str(opts['ao']),
shell=True)
stim[1][8] = int(
np.random.uniform(low=0, high=100 * opts['reps']))
stim[2][8] = I_seeds[k]
lcg.writeStimFile(stim_files['current'], stim, False)
sub.call('lcg vcclamp -V 4 -f ' + stim_files['current'],
shell=True)
sub.call(['sleep', str(opts['interval'])])
cnt = cnt + 1
if cnt % 10 == 0:
print('[%02d/%02d]' % (cnt, tot))
else:
np.random.shuffle(opts['balanced_voltages'])
if opts['with_nmda']:
gnmda['m'] = (1 - c) * opts['nmda_mean']
gnmda['mc'] = c * opts['nmda_mean']
gnmda['s'] = np.sqrt(1 - c) * opts['nmda_std']
gnmda['sc'] = np.sqrt(c) * opts['nmda_std']
for v in opts['balanced_voltages']:
ratio = lcg.computeRatesRatio(Vm=v,
Rin=opts['input_resistance'])
gampa['m'], ggaba['m'], gampa['s'], ggaba[
's'] = lcg.computeSynapticBackgroundCoefficients(
ratio,
R_exc=ratio * (1 - c) * opts['R_inh'],
Rin=opts['input_resistance'])
gampa['mc'], ggaba['mc'], gampa['sc'], ggaba[
'sc'] = lcg.computeSynapticBackgroundCoefficients(
ratio,
R_exc=ratio * c * opts['R_inh'],
Rin=opts['input_resistance'])
Gm_ampa, Gm_gaba, Gs_ampa, Gs_gaba = lcg.computeSynapticBackgroundCoefficients(
ratio,
R_exc=ratio * opts['R_inh'],
Rin=opts['input_resistance'])
if opts['only_ampa_corr']:
ggaba['m'] = ggaba['m'] + ggaba['mc']
ggaba['s'] = np.sqrt(ggaba['s']**2 + ggaba['sc']**2)
ggaba['mc'] = 0
ggaba['sc'] = 0
for k in range(opts['reps']):
stim[1][2] = gampa['m']
stim[1][3] = gampa['s']
stim[1][4] = 5
stim[1][8] = int(
np.random.uniform(low=0, high=100 * opts['reps']))
lcg.writeStimFile(stim_files['gampa'], stim, False)
stim[1][2] = ggaba['m']
stim[1][3] = ggaba['s']
stim[1][4] = 10
stim[1][8] = int(
np.random.uniform(low=0, high=100 * opts['reps']))
lcg.writeStimFile(stim_files['ggaba'], stim, False)
stim[1][2] = gampa['mc']
stim[1][3] = gampa['sc']
stim[1][4] = 5
stim[1][8] = ampa_seeds[k]
lcg.writeStimFile(stim_files['gampa_common'], stim, False)
if not opts['only_ampa_corr']:
stim[1][2] = ggaba['mc']
stim[1][3] = ggaba['sc']
stim[1][4] = 10
stim[1][8] = gaba_seeds[k]
lcg.writeStimFile(stim_files['ggaba_common'], stim,
False)
if opts['with_nmda']:
stim[1][2] = gnmda['m']
stim[1][3] = gnmda['s']
stim[1][4] = opts['nmda_tau']
stim[1][8] = int(
np.random.uniform(low=0, high=100 * opts['reps']))
lcg.writeStimFile(stim_files['gnmda'], stim, False)
stim[1][2] = gnmda['m']
stim[1][3] = gnmda['s']
stim[1][4] = opts['nmda_tau']
stim[1][8] = nmda_seeds[k]
lcg.writeStimFile(stim_files['gnmda_common'], stim,
False)
if cnt % opts['kernel_frequency'] == 0:
sub.call('lcg kernel -I ' + str(opts['ai']) + ' -O ' +
str(opts['ao']),
shell=True)
cnt = cnt + 1
sub.call(lcg.common.prog_name + ' -V 4 -c ' + config_file,
shell=True)
sub.call(['sleep', str(opts['interval'])])
if cnt % 10 == 0:
print('[%02d/%02d]' % (cnt, tot))
if duration <= 0. or amplitude <= 0.:
print('Duration and amplitude of the stimulation must be positive.')
sys.exit(1)
if ai < 0 or ao < 0:
print('I/O channels must be non-negative.')
sys.exit(1)
if append:
suffix = '-' + str(ai) + '-' + str(ao)
else:
suffix = ''
stim = [[duration,11,0,amplitude,0,0,0,1,int(rnd.uniform(high=10000)),0,0,1],
[1,1,0,0,0,0,0,0,0,0,0,1]]
lcg.writeStimFile(stim_file,stim,True)
channels = [{'type':'input', 'channel':ai, 'factor':input_factor, 'units':input_units},
{'type':'output', 'channel':ao, 'factor':output_factor, 'units':output_units, 'stimfile':stim_file}]
lcg.writeIOConfigurationFile(config_file,sampling_rate,duration+3.61,channels,realtime)
oldfiles = glob.glob('*.h5')
sub.call(lcg.common.prog_name + ' -c ' + config_file, shell=True)
try:
os.system('beep')
except:
pass
files = glob.glob('*.h5')
for ff in files:
if ff not in oldfiles:
data_file = ff
lcg.computeElectrodeKernel(os.path.abspath(data_file))
try:
def run_batch(repetitions,
interval,
stim_dur,
stim_amp,
hyperpolarizing_pulse,
pre,
post,
ai,
ao,
pairing=False,
offset=None):
try:
template_config_file = os.environ[
'CONFIGURATIONS_PATH'] + '/' + config_file
except:
template_config_file = os.environ[
'HOME'] + '/configurations/' + config_file
lcg.substituteStrings(
template_config_file, config_file, {
'TEND': pre + post,
'AI': ai,
'AO_INTRA': ao['intra'],
'AO_EXTRA': ao['extra']
})
extra_stim = [[
pre, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
], [stim_dur['extra'], 1, stim_amp['extra'], 0, 0, 0, 0, 0, 0, 0, 0, 1],
[post - stim_dur['extra'], 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
lcg.writeStimFile(extracellular_stim_file, extra_stim, False)
if pairing:
intra_stim = [[pre + offset, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[
stim_dur['intra'], 1, stim_amp['intra'], 0, 0, 0, 0,
0, 0, 0, 0, 1
],
[
post - offset - stim_dur['intra'] -
hyperpolarizing_pulse['dur'] - 0.1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1
],
[
hyperpolarizing_pulse['dur'], 1,
hyperpolarizing_pulse['amp'], 0, 0, 0, 0, 0, 0, 0, 0,
1
], [0.1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
else:
intra_stim = [[
pre + post - hyperpolarizing_pulse['dur'] - 0.1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1
],
[
hyperpolarizing_pulse['dur'], 1,
hyperpolarizing_pulse['amp'], 0, 0, 0, 0, 0, 0, 0, 0,
1
], [0.1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]
lcg.writeStimFile(intracellular_stim_file, intra_stim, False)
sub.call('lcg kernel -I ' + str(ai) + ' -O ' + str(ao['intra']),
shell=True)
sub.call(lcg.common.prog_name + ' -c ' + config_file + ' -n ' +
str(repetitions) + ' -i ' + str(interval - (pre + post)),
shell=True)
