def simulate_IF(**kw):
I_vec_in = kw.get('if_I_vec')
tStim = 700 + 1300
my_nest.ResetKernel({'local_num_threads': 1})
sd = {
'active': True,
'params': {
'to_memory': True,
'to_file': False,
'start': 500.0
}
}
# mm={'active':True,
# 'params':{'interval':0.1,'to_memory':True,'to_file':False}}
p = kw.get('if_params')
if 'type_id' in p.keys(): del p['type_id']
mnn = MyNetworkNode('dummy', model=kw.get('model'), n=1, params=p, sd=sd)
I_e0 = my_nest.GetStatus(mnn[:])[0]['I_e']
my_nest.SetStatus(mnn[:], params={'I_e': I_e0 + kw.get('I_E')}) # Set I_e
I_vec_out, fIsi, mIsi, lIsi = mnn.run_IF(I_vec_in, tStim=tStim)
d = {
'x': I_vec_out,
'first': 1000. / fIsi,
'mean': 1000. / mIsi,
'last': 1000. / lIsi,
}
return {'IF': Data_IF_curve(**d)}
def simulate_ahp(**kw):
n = len(kw.get('ahp_curr'))
I_vec = numpy.array(kw.get('ahp_curr'))
simTime = 3000. # ms
my_nest.ResetKernel({'local_num_threads': 1})
sd = {'active': True, 'params': {'to_memory': True, 'to_file': False}}
mm = {
'active': True,
'params': {
'interval': 0.1,
'to_memory': True,
'to_file': False
}
}
p = kw.get('rs_params')
if 'type_id' in p.keys(): del p['type_id']
mnn = MyNetworkNode('dummy',
model=kw.get('model'),
n=n,
params=p,
mm=mm,
sd=sd)
my_nest.SetStatus(mnn[:], params={'I_e': kw.get('ahp_I_e')}) # Set I_e
scg = my_nest.Create('step_current_generator', n=n)
rec = my_nest.GetStatus(mnn[:])[0]['receptor_types']
for source, target, I in zip(scg, mnn[:], I_vec):
my_nest.SetStatus([source], {
'amplitude_times': [500., 1000.],
'amplitude_values': [float(I), 0.]
})
my_nest.Connect([source], [target],
params={'receptor_type': rec['CURR']})
my_nest.MySimulate(simTime)
signal = mnn.spike_signal.time_slice(700, 3000)
delays = []
for i in range(n):
# print signal.spiketrains[i+1.0].spike_times
v = numpy.diff(signal.spiketrains[i + 1.0].spike_times)
v = numpy.append(v, [0])
delays.append(max(v))
dg = Data_generic(**{
'x': I_vec,
'y': delays,
'xlabel': 'Time (ms)',
'ylabel': 'Voltage (mV)'
})
return {'ahp': dg}
def set_local_lockup(self, tr_id):
post = self.get_post()
post_ids = list(numpy.unique(post))
status = my_nest.GetStatus(list(numpy.unique(tr_id[post])), 'local')
lockup = dict(zip(post_ids, status))
l = [1 if lockup[p] else 0 for p in post]
self.local_lockup = sparse.coo_matrix(l)
def simulate_rebound_spike(**kw):
n=len(kw.get('rs_curr'))
simTime = 3000. # ms
my_nest.ResetKernel({'local_num_threads':1})
sd={'active':True, 'params':{'to_memory':True,'to_file':False}}
mm={'active':True,
'params':{'interval':0.1,'to_memory':True,'to_file':False}}
p=kw.get('rs_params')
if 'type_id' in p.keys(): del p['type_id']
mnn=MyNetworkNode('dummy',model=kw.get('model'), n=n, params=p, mm=mm, sd=sd)
my_nest.SetStatus(mnn[:], params={'I_e':.5}) # Set I_e
I_e = my_nest.GetStatus(mnn.ids,'I_e')[0]
scg = my_nest.Create( 'step_current_generator',n=n )
rec=my_nest.GetStatus(mnn[:])[0]['receptor_types']
i=0
for t, c in zip(kw.get('rs_time'), kw.get('rs_curr')):
my_nest.SetStatus([scg[i]], {'amplitude_times':[500.,t+500.],
'amplitude_values':[float(c),0.]})
my_nest.Connect( [scg[i]], [mnn[i]], params = { 'receptor_type' : rec['CURR'] } )
i+=1
my_nest.MySimulate(simTime)
mnn.voltage_signal.my_set_spike_peak( 21, spkSignal= mnn.spike_signal )
d={}
for i in range(n):
voltage=mnn.voltage_signal.analog_signals[i+1].signal
x=numpy.linspace(0,simTime, len(voltage))
dg=Data_generic(**{'x':x, 'y':voltage, 'xlabel':'Time (ms)', 'ylabel':'Voltage (mV)'})
misc.dict_update(d, {'rs_voltage_{0}'.format(i):dg})
rd=mnn.spike_signal.raw_data()
dg=Data_scatter(**{'x':rd[:,0], 'y':rd[:,1], 'xlabel':'Time (ms)', 'ylabel':'Voltage (mV)'})
misc.dict_update(d, {'rs_scatter':dg})
return d
def simulate_IV(**kw):
I_vec = kw.get('iv_I_vec')
my_nest.ResetKernel({'local_num_threads': 1})
sd = {
'active': True,
'params': {
'to_memory': True,
'to_file': False,
'start': 500.0
}
}
mm = {
'active': True,
'params': {
'interval': 0.1,
'to_memory': True,
'to_file': False
}
}
p = kw.get('iv_params')
if 'type_id' in p.keys(): del p['type_id']
mnn = MyNetworkNode('dummy',
model=kw.get('model'),
n=1,
params=p,
mm=mm,
sd=sd)
I_e0 = my_nest.GetStatus(mnn[:])[0]['I_e']
my_nest.SetStatus(mnn[:], params={'I_e': I_e0 + kw.get('I_E')}) # Set I_e
x, y = mnn.run_IV_I_clamp(I_vec)
print x, y
dg = Data_generic(**{
'x': x,
'y': y,
'xlabel': 'Current (pA)',
'ylabel': 'Voltage (mV)'
})
return {'IV': dg}
'''
import numpy
from core import my_nest
my_nest.SetKernelStatus({"total_num_virtual_procs": 4})
pg = my_nest.Create("poisson_generator", 20, params={"rate": 50000.0})
n = my_nest.Create('aeif_cond_exp', 20)
# n = my_nest.Create('iaf_neuron', 20)
w = [10. for _ in n]
d = [1. for _ in n]
sd = my_nest.Create("spike_detector",
params={
"to_file": False,
"to_memory": True
})
post = n
post_ids = list(numpy.unique(post))
status = my_nest.GetStatus(list(numpy.unique(post)), 'local')
lockup = dict(zip(post_ids, status))
l = [1 if lockup[p] else 0 for p in post]
print l
pre = [p for p, b in zip(pg, l) if b]
post = [p for p, b in zip(n, l) if b]
my_nest.Connect_speed(pre, post, w, d)
my_nest.ConvergentConnect(n, sd)
my_nest.Simulate(1000.0)
print len(my_nest.GetStatus(sd)[0]['events']['senders'])
my_nest.Connect(pn[0:2], n[0:2])
my_nest.SetDefaults('bcpnn_dopamine_synapse', params={'vt': vt[0]})
my_nest.Connect([n[0]], [n[1]], model='bcpnn_dopamine_synapse')
p_sd = {"withgid": True, 'to_file': False, 'to_memory': True}
sd = my_nest.Create('spike_detector', 5, params=p_sd)
pd = my_nest.Create('poisson_generator', 5, params={'rate': 100.})
my_nest.Connect(pd, sd)
d = {'n': []}
T = []
for t in range(200):
my_nest.Simulate(1)
d['n'].append(my_nest.GetConnProp([n[0]], [n[1]], 'n'))
_t = my_nest.GetStatus(sd)[0]['events']['times']
if list(_t[(_t >= t - 1) * (_t < t)]):
T.append(_t[(_t >= t - 1) * (_t < t)])
T = reduce(lambda x, y: list(x) + list(y), T)
print _t - T
print my_nest.PrintNetwork()
t = [[tt, tt, tt] for tt in t]
y = [[0, 0.01, 0] for tt in t]
t = reduce(lambda x, y: x + y, t)
y = reduce(lambda x, y: x + y, y)
binned = numpy.zeros(len(d['n']))
pylab.plot(t, y)
def simulate_irregular_firing(**kw):
n = len(kw.get('irf_curr'))
I_vec = numpy.array(kw.get('irf_curr'))
simTime = 2000. # ms
my_nest.ResetKernel({'local_num_threads': 1})
sd = {'active': True, 'params': {'to_memory': True, 'to_file': False}}
mm = {
'active': True,
'params': {
'interval': 0.1,
'to_memory': True,
'to_file': False
}
}
p = kw.get('rs_params')
if 'type_id' in p.keys(): del p['type_id']
mnn = MyNetworkNode('dummy',
model=kw.get('model'),
n=n,
params=p,
mm=mm,
sd=sd)
I_e0 = my_nest.GetStatus(mnn.ids, 'I_e')[0]
for i, I_e in enumerate(I_vec):
my_nest.SetStatus([mnn[i]], params={'I_e': I_e + I_e0})
scg = my_nest.Create('step_current_generator', n=n)
noise = my_nest.Create('noise_generator', params={'mean': 0., 'std': 10.})
rec = my_nest.GetStatus(mnn[:])[0]['receptor_types']
for source, target, I in zip(scg, mnn[:], I_vec):
my_nest.SetStatus([source], {
'amplitude_times': [1., simTime],
'amplitude_values': [-5., float(I)]
})
my_nest.Connect([source], [target],
params={'receptor_type': rec['CURR']})
my_nest.Connect(noise, [target], params={'receptor_type': rec['CURR']})
my_nest.MySimulate(simTime)
mnn.voltage_signal.my_set_spike_peak(21, spkSignal=mnn.spike_signal)
d = {}
for i in range(n):
voltage = mnn.voltage_signal.analog_signals[i + 1].signal
x = numpy.linspace(0, simTime, len(voltage))
dg = Data_generic(**{
'x': x,
'y': voltage,
'xlabel': 'Time (ms)',
'ylabel': 'Voltage (mV)'
})
misc.dict_update(d, {'irf_voltage_{0}'.format(i): dg})
# my_nest.MySimulate(simTime)
# mnn.get_signal( 'v','V_m', stop=simTime ) # retrieve signal
# mnn.get_signal( 's') # retrieve signal
# mnn.signals['V_m'].my_set_spike_peak( 15, spkSignal= mnn.signals['spikes'] )
return d
