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_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
'''
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([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)
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']))