def compute_transfer(self, i_mean=(0.0,100.0, 10.0), i_std=(0.0,100.0, 10.0)):
self.i_range=numpy.arange(*i_mean)
self.std_range=numpy.arange(*i_std)
self.rate=numpy.zeros((self.i_range.size,self.std_range.size))
nest.sr('M_WARNING setverbosity')
for n,i in enumerate(self.i_range):
print "I= %s"%i
for m,std in enumerate(self.std_range):
self.rate[n,m]=self.output_rate(i,std)
def test_StackUnderFlow(self):
"""Stack underflow"""
nest.ResetKernel()
try:
nest.sr('clear ;')
self.fail('an error should have risen!') # should not be reached
except nest.NESTError:
info = sys.exc_info()[1]
if not "StackUnderflow" in info.__str__():
self.fail('wrong error message')
# another error has been thrown, this is wrong
except:
self.fail('wrong error has been thrown')
def test_StackUnderFlow(self):
"""Stack underflow"""
nest.ResetKernel()
try:
nest.sr('clear ;')
self.fail('an error should have risen!') # should not be reached
except nest.NESTError:
info = sys.exc_info()[1]
if not "StackUnderflow" in info.__str__():
self.fail('wrong error message')
# another error has been thrown, this is wrong
except:
self.fail('wrong error has been thrown')
def test_DivisionByZero(self):
"""Division by zero"""
nest.ResetKernel()
try:
nest.sr('1 0 div')
self.fail('an error should have risen!') # should not be reached
except nest.NESTError:
info = sys.exc_info()[1]
if not "DivisionByZero" in info.__str__():
self.fail('wrong error message')
# another error has been thrown, this is wrong
except:
self.fail('wrong error has been thrown')
def test_DivisionByZero(self):
"""Division by zero"""
nest.ResetKernel()
try:
nest.sr('1 0 div')
self.fail('an error should have risen!') # should not be reached
except nest.NESTError:
info = sys.exc_info()[1]
if not "DivisionByZero" in info.__str__():
self.fail('wrong error message')
# another error has been thrown, this is wrong
except:
self.fail('wrong error has been thrown')
def test_Threads(self):
"""Multiple threads"""
# Test if we have a thread-enabled NEST
nest.sr("statusdict /have_pthreads get")
if not nest.spp(): return
nest.ResetKernel()
self.assertEqual(nest.GetKernelStatus()['local_num_threads'],1)
nest.SetKernelStatus({'local_num_threads':8})
n=nest.Create('iaf_neuron',8)
st = nest.GetStatus(n,'vp')
st.sort()
self.assertEqual(st,[0, 1, 2, 3, 4, 5, 6, 7])
def test_Threads(self):
"""Multiple threads"""
# Test if we have a thread-enabled NEST
nest.sr("statusdict /have_pthreads get")
if not nest.spp(): return
nest.ResetKernel()
self.assertEqual(nest.GetKernelStatus()['local_num_threads'], 1)
nest.SetKernelStatus({'local_num_threads': 8})
n = nest.Create('iaf_neuron', 8)
st = nest.GetStatus(n, 'vp')
st.sort()
self.assertEqual(st, [0, 1, 2, 3, 4, 5, 6, 7])
def test_EventsVoltage(self):
"""Voltage Events"""
nest.ResetKernel()
nest.sr('20 setverbosity')
n = nest.Create('iaf_neuron')
vm = nest.Create('voltmeter',1,{'withtime':True, 'interval':1.})
nest.Connect(vm,n)
nest.SetKernelStatus({'print_time':False})
nest.Simulate(10)
d = nest.GetStatus(vm,'events')[0]
self.assertEqual(len(d['V_m']), 9)
def test_EventsVoltage(self):
"""Voltage Events"""
nest.ResetKernel()
nest.sr('20 setverbosity')
n = nest.Create('iaf_neuron')
vm = nest.Create('voltmeter', 1, {'withtime': True, 'interval': 1.})
nest.Connect(vm, n)
nest.SetKernelStatus({'print_time': False})
nest.Simulate(10)
d = nest.GetStatus(vm, 'events')[0]
self.assertEqual(len(d['V_m']), 9)
def test_EventsSpikes(self):
"""Spike Events"""
nest.ResetKernel()
nest.sr('20 setverbosity')
n = nest.Create('iaf_neuron',1,{'I_e':1000.})
sd = nest.Create('spike_detector',1,{'withtime':True})
nest.Connect(n,sd)
nest.SetKernelStatus({'print_time':False})
nest.Simulate(1000)
d = nest.GetStatus(sd,'events')[0]
self.assert_(len(d['times'])>0)
def test_EventsSpikes(self):
"""Spike Events"""
nest.ResetKernel()
nest.sr('20 setverbosity')
n = nest.Create('iaf_neuron', 1, {'I_e': 1000.})
sd = nest.Create('spike_detector', 1, {'withtime': True})
nest.Connect(n, sd)
nest.SetKernelStatus({'print_time': False})
nest.Simulate(1000)
d = nest.GetStatus(sd, 'events')[0]
self.assert_(len(d['times']) > 0)
def test_ThreadsFindConnections(self):
"""FindConnections with threads"""
# Test if we have a thread-enabled NEST
nest.sr("statusdict /have_pthreads get")
if not nest.spp(): return
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads':8})
pre = nest.Create("iaf_neuron")
post = nest.Create("iaf_neuron", 6)
nest.DivergentConnect(pre, post)
conn = nest.FindConnections(pre)
targets = nest.GetStatus(conn, "target")
self.assertEqual(targets, post)
def test_ThreadsFindConnections(self):
"""FindConnections with threads"""
# Test if we have a thread-enabled NEST
nest.sr("statusdict /have_pthreads get")
if not nest.spp(): return
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads': 8})
pre = nest.Create("iaf_neuron")
post = nest.Create("iaf_neuron", 6)
nest.DivergentConnect(pre, post)
conn = nest.FindConnections(pre)
targets = nest.GetStatus(conn, "target")
self.assertEqual(targets, post)
def test_ThreadsGetEvents(self):
""" Gathering events across threads """
# Test if we have a thread-enabled NEST
nest.sr("statusdict /have_pthreads get")
if not nest.spp(): return
threads = [1, 2, 4, 8]
n_events_sd = []
n_events_vm = []
N = 128
Simtime = 1000.
for t in threads:
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads': t})
n = nest.Create('iaf_psc_alpha', N,
{'I_e': 2000.}) # force a lot of spike events
sd = nest.Create('spike_detector')
vm = nest.Create('voltmeter')
nest.ConvergentConnect(n, sd)
nest.DivergentConnect(vm, n)
nest.Simulate(Simtime)
n_events_sd.append(nest.GetStatus(sd, 'n_events')[0])
n_events_vm.append(nest.GetStatus(vm, 'n_events')[0])
ref_vm = N * (Simtime - 1)
ref_sd = n_events_sd[0]
# could be done more elegantly with any(), ravel(),
# but we dont want to be dependent on numpy et al
[self.assertEqual(x, ref_vm) for x in n_events_vm]
[self.assertEqual(x, ref_sd) for x in n_events_sd]
def test_ThreadsGetEvents(self):
""" Gathering events across threads """
# Test if we have a thread-enabled NEST
nest.sr("statusdict /have_pthreads get")
if not nest.spp(): return
threads = [1,2,4,8]
n_events_sd = []
n_events_vm = []
N = 128
Simtime = 1000.
for t in threads:
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads': t})
n = nest.Create('iaf_psc_alpha', N, {'I_e':2000.}) # force a lot of spike events
sd = nest.Create('spike_detector')
vm = nest.Create('voltmeter')
nest.ConvergentConnect(n,sd)
nest.DivergentConnect(vm,n)
nest.Simulate(Simtime)
n_events_sd.append(nest.GetStatus(sd, 'n_events')[0])
n_events_vm.append(nest.GetStatus(vm, 'n_events')[0])
ref_vm = N*(Simtime-1)
ref_sd = n_events_sd[0]
# could be done more elegantly with any(), ravel(),
# but we dont want to be dependent on numpy et al
[ self.assertEqual(x,ref_vm) for x in n_events_vm]
[ self.assertEqual(x,ref_sd) for x in n_events_sd]
def test_Count(self):
"""Object count"""
nest.ResetKernel()
nest.sr('clear')
for i in range(100):
nest.sps(i)
nest.sr('count')
self.assertEqual(nest.spp(), 100)
for i in range(100):
self.assertEqual(nest.spp(), (99-i))
nest.sr('count')
self.assertEqual(nest.spp(), 0)
def test_Count(self):
"""Object count"""
nest.ResetKernel()
nest.sr('clear')
for i in range(100):
nest.sps(i)
nest.sr('count')
self.assertEqual(nest.spp(), 100)
for i in range(100):
self.assertEqual(nest.spp(), (99 - i))
nest.sr('count')
self.assertEqual(nest.spp(), 0)
This example is also shown in the article Eppler et al. (2009) PyNEST:
A convenient interface to the {NEST} simulator, Front. Neuroinform.
doi:10.3389/neuro.11.012.2008
'''
# First, we import all necessary modules for simulation, analysis and
# plotting. Additionally, we set the verbosity to suppress info
# messages
from scipy.optimize import bisect
import cynest as nest
import cynest.voltage_trace
nest.sr("M_WARNING setverbosity")
nest.ResetKernel()
# Second, the simulation parameters are assigned to variables.
t_sim = 25000.0 # how long we simulate
n_ex = 16000 # size of the excitatory population
n_in = 4000 # size of the inhibitory population
r_ex = 5.0 # mean rate of the excitatory population
r_in = 20.5 # initial rate of the inhibitory population
epsc = 45.0 # peak amplitude of excitatory synaptic currents
ipsc = -45.0 # peak amplitude of inhibitory synaptic currents
d = 1.0 # synaptic delay
lower = 15.0 # lower bound of the search interval
upper = 25.0 # upper bound of the search interval
prec = 0.01 # how close need the excitatory rates be
ll = (V0*mV - mu) / (sigma)/sqrt(2)
interval = (ul-ll)/num_iterations
tmpsum = 0.0
for cu in range(0,num_iterations+1):
u = ll + cu * interval
f = exp(u**2)*(1+erf(u))
tmpsum += interval * sqrt(pi) * f
r = 1. / (tref*ms + tau_m*ms * tmpsum)
########################################################################################
# Simulation section
nest.ResetKernel()
nest.sr('20 setverbosity')
neurondict = {'V_th':Vth, 'tau_m':tau_m, 'tau_syn_ex':tau_syn_ex,'tau_syn_in':tau_syn_in, 'C_m':Cm, 'E_L':V0, 't_ref':tref, 'V_m': V0, 'V_reset': V0}
if (mu*1000) < Vth:
neurondict['V_m'] = mu*1000.
nest.SetDefaults('iaf_psc_alpha', neurondict)
n = nest.Create('iaf_psc_alpha', n_neurons)
n_free = nest.Create('iaf_psc_alpha', 1 ,[{'V_th':999999.}]) # high threshold as we want free membrane potential
pg = nest.Create('poisson_generator', len(rates), [ {'rate':float(rate_i)} for rate_i in rates] )
vm = nest.Create('voltmeter', 1, [{'record_to':['memory'], 'withtime':True, 'withgid':True, 'interval':.1}])
sd = nest.Create('spike_detector',1, [{'record_to':['memory'], 'withtime':True, 'withgid':True}])
for i, currentpg in enumerate(pg):
nest.DivergentConnect([currentpg],n,weight=float(J[i]), delay=0.1)
nest.Connect([currentpg],n_free, {'weight':J[i]})
This example is also shown in the article Eppler et al. (2009) PyNEST:
A convenient interface to the {NEST} simulator, Front. Neuroinform.
doi:10.3389/neuro.11.012.2008
'''
# First, we import all necessary modules for simulation, analysis and
# plotting. Additionally, we set the verbosity to suppress info
# messages
from scipy.optimize import bisect
import cynest as nest
import cynest.voltage_trace
nest.sr("M_WARNING setverbosity")
nest.ResetKernel()
# Second, the simulation parameters are assigned to variables.
t_sim = 25000.0 # how long we simulate
n_ex = 16000 # size of the excitatory population
n_in = 4000 # size of the inhibitory population
r_ex = 5.0 # mean rate of the excitatory population
r_in = 20.5 # initial rate of the inhibitory population
epsc = 45.0 # peak amplitude of excitatory synaptic currents
ipsc = -45.0 # peak amplitude of inhibitory synaptic currents
d = 1.0 # synaptic delay
lower = 15.0 # lower bound of the search interval
upper = 25.0 # upper bound of the search interval
prec = 0.01 # how close need the excitatory rates be
