def simulate_network(**kw):
my_nest.ResetKernel()
my_nest.SetKernelStatus({
'local_num_threads': 1,
# 'print_time':True
})
p_gi = par.dic['nest']['GI']
p_st = par.dic['nest']['ST']
for d in [p_gi, p_st]:
if 'type_id' in d: del d['type_id']
mm = {'active': True, 'params': {'to_memory': True, 'to_file': False}}
st = MyNetworkNode('st', model='my_aeif_cond_exp', n=1, params=p_st, mm=mm)
for key in ['GI_ST_gaba', 'CS_ST_ampa']:
d = par.dic['nest'][key]
if 'type_id' in d: del d['type_id']
my_nest.CopyModel('static_synapse', key, d)
d = {
'rate_first': 30**2. * (1 + kw.get('gi_amp', 0)),
'rate_second': 30**2.**(1 - kw.get('gi_amp', 0)),
'period_first': 25.0,
'period_second': 25.
}
inp_st_gaba = my_nest.Create('poisson_generator_periodic', n=1, params=d)
d = {
'rate_first': 400. * (1 + kw.get('st_amp', 0)),
'rate_second': 400.**(1 - kw.get('st_amp', 0)),
'period_first': 25.0,
'period_second': 25.
}
inp_st_ampa = my_nest.Create('poisson_generator_periodic', n=1, params=d)
my_nest.Connect(inp_st_gaba, st.ids, 0.08, 1., model='GI_ST_gaba')
my_nest.Connect(inp_st_ampa, st.ids, 0.25, 1., model='CS_ST_ampa')
my_nest.Simulate(kw.get('sim_time'), chunksize=1000.0)
d = {}
from core.network.data_processing import Data_unit_vm
st_dus = Data_unit_vm('st', st.get_voltage_signal())
d['st'] = {'vm_signal': st_dus}
return 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 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_network(**kw):
my_nest.ResetKernel()
my_nest.SetKernelStatus({
'local_num_threads': 4,
# 'print_time':True
})
p_gi = par.dic['nest']['GI']
p_st = par.dic['nest']['ST']
p_st.update(kw.get('p_st', {}))
for d in [p_gi, p_st]:
if 'type_id' in d: del d['type_id']
sd = {
'active': True,
'params': {
'to_memory': True,
'to_file': False,
'start': 500.0
}
}
gi = MyNetworkNode('gi',
model='my_aeif_cond_exp',
n=kw.get('gi_n'),
params=p_gi,
sd=sd)
# st=MyNetworkNode('gi',model='my_aeif_cond_exp', n=kw.get('st_n'), params=p_gi, sd=sd)
st = MyNetworkNode('st',
model='my_aeif_cond_exp',
n=kw.get('st_n'),
params=p_st,
sd=sd)
for key in [
'GI_ST_gaba', 'GI_GI_gaba', 'ST_GI_ampa', 'EI_GI_ampa',
'CS_ST_ampa'
]:
d = par.dic['nest'][key]
if 'type_id' in d: del d['type_id']
my_nest.CopyModel('static_synapse', key, d)
df = my_nest.GetDefaults('my_aeif_cond_exp')['receptor_types']
for post in st:
idx = random.sample(range(kw.get('gi_n')), 30)
# params={'receptor_type':df['GABAA_1']}
delay = kw['gi_st_delay']
model = 'GI_ST_gaba'
my_nest.Connect([pre for i, pre in enumerate(gi) if i in idx],
[post] * 30, [0.08] * 30, [delay] * 30, model)
for pre in gi:
idx = random.sample(range(kw.get('gi_n')), 30)
# params={'receptor_type':df['GABAA_1']}
delay = kw['gi_gi_delay']
model = 'GI_GI_gaba'
my_nest.Connect([pre] * 30,
[post for i, post in enumerate(gi) if i in idx],
[1.3] * 30, [delay] * 30, model)
for pre in gi:
idx = random.sample(range(kw.get('st_n')), 30)
# params={'receptor_type':df['AMPA_1']}
delay = kw['st_gi_delay']
model = 'ST_GI_ampa'
my_nest.Connect([pre for i, pre in enumerate(st) if i in idx],
[post] * 30, [0.35] * 30, [delay] * 30, model)
d = {
'rate_first': 1400. * (1 + kw.get('gi_amp', 0)),
'rate_second': 1400.**(1 - kw.get('gi_amp', 0)),
'period_first': 25.0,
'period_second': 25.
}
inp_gi = my_nest.Create('poisson_generator_periodic',
n=kw.get('gi_n'),
params=d)
d = {
'rate_first': 400. * (1 + kw.get('st_amp', 0)),
'rate_second': 400.**(1 - kw.get('st_amp', 0)),
'period_first': 25.0,
'period_second': 25.
}
inp_st = my_nest.Create('poisson_generator_periodic',
n=kw.get('st_n'),
params=d)
my_nest.Connect(inp_gi, gi.ids, 0.25, 1., model='EI_GI_ampa')
my_nest.Connect(inp_st, st.ids, 0.25, 1., model='CS_ST_ampa')
my_nest.Simulate(kw.get('sim_time'), chunksize=1000.0)
d = {}
from core.network.data_processing import Data_unit_spk, Data_units_relation
gi_dus = Data_unit_spk('gi', gi.get_spike_signal())
d['gi'] = {'spike_signal': gi_dus}
st_dus = Data_unit_spk('st', st.get_spike_signal())
d['st'] = {'spike_signal': st_dus}
d['gi_st'] = {'spike_signal': Data_units_relation('gi_st', gi_dus, st_dus)}
return d
'''
Created on Oct 20, 2014
@author: mikael
'''
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)
import numpy
import pylab
from os.path import expanduser
from core import my_nest
import mpi4py
HOME = expanduser("~")
MODULE_PATH = (HOME + '/opt/NEST/dist/install-nest-2.2.2/lib/nest/ml_module')
my_nest.Install(MODULE_PATH)
#ip=my_nest.Create('spike_generator',params={'spike_times':[10.,20.]})
ip = my_nest.Create('poisson_generator', 3, params={'rate': 3500.})
pn = my_nest.Create('parrot_neuron', 3)
sd = my_nest.Create('spike_detector')
n = my_nest.Create('iaf_cond_exp', 3, params={'C_m': 200.0, 'V_th': -50.})
vt = my_nest.Create('volume_transmitter')
my_nest.Connect(ip, pn)
my_nest.Connect([pn[2]], [n[2]], params={'weight': 10.})
my_nest.Connect([n[2]], sd)
my_nest.Connect([n[2]], vt)
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)
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