def InstallBCPNN(on_milner=False, on_beskow=False):
'''
This function installs the BCPNN Synapse by Phil Tully.
The optional parameter passed, indicate whether this is on Supercomputer Milner (True) or Beskow
or my local machine (False)
'''
if ('milner' in os.getcwd()):
on_milner = True
if ('klemming' in os.getcwd()):
on_beskow = True
if (not 'bcpnn_synapse' in nest.Models('synapses')):
if on_beskow:
nest.sr(
'(/cfs/klemming/nobackup/f/fiebig/170501_Beskow_BCPNN/share/nest/sli) addpath'
)
nest.Install(
'/cfs/klemming/nobackup/f/fiebig/170501_Beskow_BCPNN/lib/nest/pt_module'
)
elif on_milner:
nest.sr(
'(/cfs/milner/scratch/f/fiebig/140408_Milner_BCPNN/share/nest/sli) addpath'
)
nest.Install(
'/cfs/milner/scratch/f/fiebig/140408_Milner_BCPNN/lib/nest/pt_module'
)
else:
try:
nest.Install('pt_module')
except:
nest.Install('pt_module')
def run_model(model='aeif_cond_alpha', dt=0.1,reps=1):
nest.ResetKernel()
nest.sr("30 setverbosity")
nest.SetKernelStatus({"overwrite_files": True})
nest.SetStatus([0],[{"resolution": dt}])
nest.SetDefaults('aeif_cond_alpha_RK5',{'HMIN':0.001})
nest.SetDefaults('aeif_cond_alpha_RK5',{'MAXERR':1e-10})
neuron = nest.Create(model,2)
nest.SetStatus(neuron,[{"V_peak": 0.0, "a": 4.0, "b":80.5}])
dc=nest.Create("dc_generator")
nest.SetStatus(dc,[{"amplitude":700.0,
"start":700.0,
"stop":2700.0}])
nest.Connect(dc,[neuron[0]])
sd = nest.Create('spike_detector')
nest.Connect([neuron[0]],sd)
meter0 = nest.Create('multimeter', params={'record_from': ['V_m', 'g_ex','g_in','w'], 'interval' :0.1})
nest.Connect(meter0,[neuron[0]])
nest.SetStatus(meter0,[{"to_file": False, "withtime": True}])
t = timeit.Timer("nest.Simulate(3000)","import nest")
runtime = t.timeit(number=reps)/reps
sptimes = nest.GetStatus(sd,"events")[0]['times']
voltage_trace = nest.GetStatus(meter0,"events")[0]['V_m']
return (runtime,sptimes,voltage_trace)
def run_model(model='aeif_cond_alpha', dt=0.1, reps=1):
nest.ResetKernel()
nest.sr("30 setverbosity")
nest.SetKernelStatus({"overwrite_files": True})
nest.SetStatus([0], [{"resolution": dt}])
nest.SetDefaults('aeif_cond_alpha_RK5', {'HMIN': 0.001})
nest.SetDefaults('aeif_cond_alpha_RK5', {'MAXERR': 1e-10})
neuron = nest.Create(model, 2)
nest.SetStatus(neuron, [{"V_peak": 0.0, "a": 4.0, "b": 80.5}])
dc = nest.Create("dc_generator")
nest.SetStatus(dc, [{"amplitude": 700.0, "start": 700.0, "stop": 2700.0}])
nest.Connect(dc, [neuron[0]])
sd = nest.Create('spike_detector')
nest.Connect([neuron[0]], sd)
meter0 = nest.Create('multimeter',
params={
'record_from': ['V_m', 'g_ex', 'g_in', 'w'],
'interval': 0.1
})
nest.Connect(meter0, [neuron[0]])
nest.SetStatus(meter0, [{"to_file": False, "withtime": True}])
t = timeit.Timer("nest.Simulate(3000)", "import nest")
runtime = t.timeit(number=reps) / reps
sptimes = nest.GetStatus(sd, "events")[0]['times']
voltage_trace = nest.GetStatus(meter0, "events")[0]['V_m']
return (runtime, sptimes, voltage_trace)
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 setup(timestep=0.1, min_delay=0.1, max_delay=10.0, **extra_params):
"""
Should be called at the very beginning of a script.
extra_params contains any keyword arguments that are required by a given
simulator but not by others.
"""
global tempdir
common.setup(timestep, min_delay, max_delay, **extra_params)
if 'verbosity' in extra_params:
nest_verbosity = extra_params['verbosity'].upper()
else:
nest_verbosity = "WARNING"
nest.sli_run("M_%s setverbosity" % nest_verbosity)
# clear the sli stack, if this is not done --> memory leak cause the stack increases
nest.sr('clear')
# reset the simulation kernel
nest.ResetKernel()
# set tempdir
tempdir = tempfile.mkdtemp()
tempdirs.append(tempdir) # append tempdir to tempdirs list
nest.SetKernelStatus({
'data_path': tempdir,
})
# set kernel RNG seeds
num_threads = extra_params.get('threads') or 1
if 'rng_seeds' in extra_params:
rng_seeds = extra_params['rng_seeds']
else:
rng_seeds_seed = extra_params.get('rng_seeds_seed') or 42
rng = NumpyRNG(rng_seeds_seed)
rng_seeds = (rng.rng.uniform(size=num_threads * num_processes()) *
100000).astype('int').tolist()
logger.debug("rng_seeds = %s" % rng_seeds)
nest.SetKernelStatus({
'local_num_threads': num_threads,
'rng_seeds': rng_seeds
})
# set resolution
nest.SetKernelStatus({'resolution': timestep})
# Set min_delay and max_delay for all synapse models
for synapse_model in NEST_SYNAPSE_TYPES:
nest.SetDefaults(synapse_model, {
'delay': min_delay,
'min_delay': min_delay,
'max_delay': max_delay
})
simulator.reset()
return rank()
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 setUp(self):
nest.ResetKernel()
nest.sr("statusdict/threading :: (no) eq not")
if not nest.spp():
# no multi-threading
nest.SetKernelStatus({'grng_seed': 120,
'rng_seeds': [576]})
else:
# multi-threading
nest.SetKernelStatus({'local_num_threads': 2,
'grng_seed': 120,
'rng_seeds': [576, 886]})
pass
def setUp(self):
nest.ResetKernel()
nest.sr("statusdict/threading :: (no) eq not")
if not nest.spp():
# no multi-threading
nest.SetKernelStatus({'grng_seed': 120,
'rng_seeds': [576]})
else:
# multi-threading
nest.SetKernelStatus({'local_num_threads': 2,
'grng_seed': 120,
'rng_seeds': [576, 886]})
pass
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_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 clear(self):
self.populations = []
self.recording_devices = []
self.recorders = set()
# clear the sli stack, if this is not done --> memory leak cause the stack increases
nest.sr('clear')
# reset the simulation kernel
nest.ResetKernel()
# set tempdir
tempdir = tempfile.mkdtemp()
self.tempdirs.append(tempdir) # append tempdir to tempdirs list
nest.SetKernelStatus({'data_path': tempdir, })
self.segment_counter = -1
self.reset()
def install_module(path, sli_path, model_to_exist='my_aeif_cond_exp'):
if not model_to_exist in nest.Models():
if sli_path != '':
nest.sr('(' + sli_path + ') addpath')
#nest.Install(path)
# Solves weird problem that I need to load it twice
# only on my wheezy debian
try:
nest.Install(path) #always fails in Nest 2.4.X
except:
nest.Install(path) #running twice fixes Nest 2.4.X
print '...successful'
def clear(self):
self.populations = []
self.recording_devices = []
self.recorders = set()
# clear the sli stack, if this is not done --> memory leak cause the stack increases
nest.sr('clear')
# reset the simulation kernel
nest.ResetKernel()
# but this reverts some of the PyNN settings, so we have to repeat them (see NEST #716)
self.spike_precision = self._spike_precision
# set tempdir
tempdir = tempfile.mkdtemp()
self.tempdirs.append(tempdir) # append tempdir to tempdirs list
nest.SetKernelStatus({'data_path': tempdir, })
self.segment_counter = -1
self.reset()
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 install_module(path, sli_path, model_to_exist='my_aeif_cond_exp'):
if not model_to_exist in nest.Models():
if sli_path!='':
nest.sr('('+sli_path+') addpath')
#nest.Install(path)
# Solves weird problem that I need to load it twice
# only on my wheezy debian
try:
nest.Install(path)#always fails in Nest 2.4.X
except:
nest.Install(path)#running twice fixes Nest 2.4.X
print '...successful'
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 clear(self):
self.populations = []
self.recording_devices = []
self.recorders = set()
# clear the sli stack, if this is not done --> memory leak cause the stack increases
nest.sr('clear')
# reset the simulation kernel
nest.ResetKernel()
# but this reverts some of the PyNN settings, so we have to repeat them (see NEST #716)
self.spike_precision = self._spike_precision
# set tempdir
tempdir = tempfile.mkdtemp()
self.tempdirs.append(tempdir) # append tempdir to tempdirs list
nest.SetKernelStatus({
'data_path': tempdir,
})
self.segment_counter = -1
self.reset()
def clear(self, **kwargs):
# Set initial values
self.populations = []
self.recording_devices = []
self.recorders = set()
# clear the sli stack, if this is not done --> memory leak cause the
# stack increases
nest.sr('clear')
# set tempdir
tempdir = tempfile.mkdtemp()
self.tempdirs.append(tempdir) # append tempdir to tempdirs list
nest.SetKernelStatus({'data_path': tempdir})
self.segment_counter = 0
# Get values before they are reset
dt = kwargs.get('dt', self.dt)
num_processes = self.num_processes
threads = self.threads
# Reset network and kernel
nest.ResetKernel()
nest.ResetNetwork()
nest.SetKernelStatus({'overwrite_files': True, 'resolution': dt})
if 'dt' in kwargs:
self.dt = kwargs['dt']
# set kernel RNG seeds
self.num_threads = kwargs.get('threads', 1)
if 'grng_seed' in kwargs:
self.grng_seed = kwargs['grng_seed']
if 'rng_seeds' in kwargs:
self.rng_seeds = kwargs['rng_seeds']
else:
rng = numpy.random.RandomState(kwargs.get('rng_seed',
int(time.time())))
n = num_processes * threads
self.rng_seeds = list(
numpy.asarray(rng.uniform(low=0, high=100000, size=n),
dtype=int))
self.reset()
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)
def memory_thisjob():
'''Wrapper to obtain current memory usage'''
nest.sr('memory_thisjob')
return nest.spp()
def nest_multithreaded(self):
"""Return True, if we have a thread-enabled NEST, False otherwise"""
nest.sr("statusdict/threading :: (no) eq not")
return nest.spp()
def lambertwm1(x):
'''Wrapper for LambertWm1 function'''
nest.sr('{} LambertWm1'.format(x))
return nest.spp()
def setup(timestep=0.1, min_delay=0.1, max_delay=10.0, **extra_params):
"""
Should be called at the very beginning of a script.
extra_params contains any keyword arguments that are required by a given
simulator but not by others.
"""
global tempdir
common.setup(timestep, min_delay, max_delay, **extra_params)
# clear the sli stack, if this is not done --> memory leak cause the stack increases
nest.sr('clear')
# reset the simulation kernel
nest.ResetKernel()
if 'verbosity' in extra_params:
nest_verbosity = extra_params['verbosity'].upper()
else:
nest_verbosity = "WARNING"
nest.sli_run("M_%s setverbosity" % nest_verbosity)
if "spike_precision" in extra_params:
simulator.state.spike_precision = extra_params["spike_precision"]
if extra_params["spike_precision"] == 'off_grid':
simulator.state.default_recording_precision = 15
nest.SetKernelStatus(
{'off_grid_spiking': simulator.state.spike_precision == 'off_grid'})
if "recording_precision" in extra_params:
simulator.state.default_recording_precision = extra_params[
"recording_precision"]
# all NEST to erase previously written files (defaut with all the other simulators)
nest.SetKernelStatus({'overwrite_files': True})
# set tempdir
tempdir = tempfile.mkdtemp()
tempdirs.append(tempdir) # append tempdir to tempdirs list
nest.SetKernelStatus({
'data_path': tempdir,
})
# set kernel RNG seeds
num_threads = extra_params.get('threads') or 1
if 'rng_seeds' in extra_params:
rng_seeds = extra_params['rng_seeds']
else:
rng_seeds_seed = extra_params.get('rng_seeds_seed') or 42
rng = NumpyRNG(rng_seeds_seed)
rng_seeds = (rng.rng.uniform(size=num_threads * num_processes()) *
100000).astype('int').tolist()
logger.debug("rng_seeds = %s" % rng_seeds)
nest.SetKernelStatus({
'local_num_threads': num_threads,
'rng_seeds': rng_seeds
})
# set resolution
nest.SetKernelStatus({'resolution': timestep})
if 'allow_offgrid_spikes' in nest.GetDefaults('spike_generator'):
nest.SetDefaults('spike_generator', {'allow_offgrid_spikes': True})
# Set min_delay and max_delay for all synapse models
NEST_SYNAPSE_TYPES = nest.Models(
mtype='synapses') # need to rebuild after ResetKernel
for synapse_model in NEST_SYNAPSE_TYPES:
nest.SetDefaults(synapse_model, {
'delay': min_delay,
'min_delay': min_delay,
'max_delay': max_delay
})
simulator.connection_managers = []
simulator.populations = []
simulator.reset()
return rank()
def lambertwm1(x):
'''Wrapper for LambertWm1 function'''
nest.sr('{} LambertWm1'.format(x))
return nest.spp()
import tempfile
import mynest
import mynest_light
import pprint as pp
os.environ['MPLCONFIGDIR'] = tempfile.mkdtemp()
import matplotlib
matplotlib.use('Agg')
import pylab as pl
print 'INIT'
# Load BCPNN synapse and bias-iaf neuron module
if (not 'bcpnn_dopamine_synapse' in nest.Models()):
nest.sr('(/cfs/milner/scratch/b/berthet/modules/bcpnndopa_module/share/ml_module/sli) addpath') #t/tully/sequences/share/nest/sli
nest.Install('/cfs/milner/scratch/b/berthet/modules/bcpnndopa_module/lib/nest/ml_module') #t/tully/sequences/lib/nest/pt_module
# nest.Install('ml_module')
try:
from mpi4py import MPI
USE_MPI = True
comm = MPI.COMM_WORLD
pc_id, n_proc = comm.rank, comm.size
print "USE_MPI:", USE_MPI, 'pc_id, n_proc:', pc_id, n_proc
except:
USE_MPI = False
pc_id, n_proc, comm = 0, 1, None
print "MPI not used"
nest.ResetKernel()
nest.ResetKernel()
import nest
print nest.Models() # 'bcpnn_synapse' should NOT show up
# ON LINDGREN: instead of nest.Install('pt_module'), do:
#if (not 'bcpnn_synapse' in nest.Models('synapses')):
# nest.sr('(/home/bernhard/Downloads/nest/nest-2.2.1-build/share/nest/sli) addpath')
# nest.Install('/home/bernhard/workspace/BCPNN-module/build-module-100725/pt_module')
on_milner = True
if (not 'bcpnn_synapse' in nest.Models('synapses')):
if on_milner:
nest.sr(
'(/cfs/milner/scratch/b/bkaplan/BCPNN-Module/share/nest/sli) addpath'
)
nest.Install(
'/cfs/milner/scratch/b/bkaplan/BCPNN-Module/lib/nest/pt_module')
else:
nest.Install('pt_module')
nest.Models() # now, 'bcpnn_synapse' is available
nest.GetDefaults('bcpnn_synapse')
import pprint
pp=pprint.pprint
from os.path import expanduser
from core.my_population import MySpikeList
s='nest-2.2.2'
HOME = expanduser("~")
MODULE_PATH= (HOME+'/opt/NEST/module/'
+'install-module-130701-'+s+'/lib/nest/ml_module')
MODULE_SLI_PATH= (HOME+'/opt/NEST/module/'
+'install-module-130701-'+s+'/share/ml_module/sli')
nest.sr('('+MODULE_SLI_PATH+') addpath')
nest.Install(MODULE_PATH)
pp(nest.Models())
# nest.GetDefaults(dn['type_id'])
nest.CopyModel('poisson_generator_dynamic', 'new')
pp(nest.GetDefaults('poisson_generator_dynamic'))
n=nest.Create('poisson_generator_dynamic', params={'rates':[500.,0.,100.],
'timings':[0.,3000., 4000.]})
pn=nest.Create('parrot_neuron',2)
sd=nest.Create('spike_detector',2)
nest.Connect(n+n,pn)
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 nest
import nest.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
def memory_thisjob():
'''Wrapper to obtain current memory usage'''
nest.sr('memory_thisjob')
return nest.spp()
import json
import simulation_parameters
import utils
import numpy as np
import time
import tempfile
import mynest
os.environ['MPLCONFIGDIR'] = tempfile.mkdtemp()
import matplotlib
matplotlib.use('Agg')
import pylab as pl
# Load BCPNN synapse and bias-iaf neuron module
if (not 'bcpnn_dopamine_synapse' in nest.Models()):
nest.sr('(/cfs/klemming/nobackup/b/berthet/code/modules/bcpnndopa_module/share/ml_module/sli) addpath') #t/tully/sequences/share/nest/sli
nest.Install('/cfs/klemming/nobackup/b/berthet/code/modules/bcpnndopa_module/lib/nest/ml_module') #t/tully/sequences/lib/nest/pt_module
nest.ResetKernel()
nest.SetKernelStatus({"overwrite_files": True})
GP = simulation_parameters.global_parameters()
GP.write_parameters_to_file() # write_parameters_to_file MUST be called before every simulation
params = GP.params
t0 = time.time()
# ########################
# SIMULATION
import nest
print nest.Models() # 'bcpnn_synapse' should NOT show up
on_beskow = True
if (not 'bcpnn_synapse' in nest.Models('synapses')):
if on_beskow:
nest.sr(
'(/cfs/klemming/nobackup/f/fiebig/170501_Beskow_BCPNN/share/nest/sli) addpath'
)
nest.Install(
'/cfs/klemming/nobackup/f/fiebig/170501_Beskow_BCPNN/lib/nest/pt_module'
)
else:
try:
nest.Install('pt_module')
except:
nest.Install('pt_module')
status = ('bcpnn_synapse' in nest.Models('synapses'))
print 'bcpnn now available', status
nest.GetDefaults('bcpnn_synapse')
servo_ids["shin.L"] = setPositionServo(reference_object_name = "obj_shin.L", attached_object_name = "obj_thigh.L", P = PP)
servo_ids["shin.R"] = setPositionServo(reference_object_name = "obj_shin.R", attached_object_name = "obj_thigh.R", P = PP)
servo_ids["thigh.L"] = setPositionServo(reference_object_name = "obj_thigh.L", attached_object_name = "obj_hips", P = PP)
servo_ids["thigh.R"] = setPositionServo(reference_object_name = "obj_thigh.R", attached_object_name = "obj_hips", P = PP)
# =================================================================================================================================================
# Network creation
## Nest Kernel Initialization
T=8
nest.ResetKernel()
nest.SetKernelStatus({"overwrite_files": True, "print_time": True})
nest.SetKernelStatus({"local_num_threads": T})
aa=np.random.randint(1,100)
nest.SetKernelStatus({'rng_seeds' : range(aa,aa+T)})
nest.sr("M_ERROR setverbosity")
## Network Parameters
neuronModel='aeif_cond_exp'
numSC=100
dx=10
dy=5
dz=2
dim=3
inpDim=6
connProb=.1
synType='tsodyks2_synapse'
numInp=(numSC*1.)
numOut=12
Weight=10.
def nest_multithreaded(self):
"""Return True, if we have a thread-enabled NEST, False otherwise"""
nest.sr("statusdict/threading :: (no) eq not")
return nest.spp()
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 nest
import nest.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]})
def setup(timestep=0.1, min_delay=0.1, max_delay=10.0, **extra_params):
"""
Should be called at the very beginning of a script.
extra_params contains any keyword arguments that are required by a given
simulator but not by others.
"""
global tempdir
common.setup(timestep, min_delay, max_delay, **extra_params)
if 'verbosity' in extra_params:
nest_verbosity = extra_params['verbosity'].upper()
else:
nest_verbosity = "WARNING"
nest.sli_run("M_%s setverbosity" % nest_verbosity)
if "spike_precision" in extra_params:
simulator.state.spike_precision = extra_params["spike_precision"]
if extra_params["spike_precision"] == 'off_grid':
simulator.state.default_recording_precision = 15
nest.SetKernelStatus({'off_grid_spiking': simulator.state.spike_precision=='off_grid'})
if "recording_precision" in extra_params:
simulator.state.default_recording_precision = extra_params["recording_precision"]
# clear the sli stack, if this is not done --> memory leak cause the stack increases
nest.sr('clear')
# reset the simulation kernel
nest.ResetKernel()
# all NEST to erase previously written files (defaut with all the other simulators)
nest.SetKernelStatus({'overwrite_files' : True})
# set tempdir
tempdir = tempfile.mkdtemp()
tempdirs.append(tempdir) # append tempdir to tempdirs list
nest.SetKernelStatus({'data_path': tempdir,})
# set kernel RNG seeds
num_threads = extra_params.get('threads') or 1
if 'rng_seeds' in extra_params:
rng_seeds = extra_params['rng_seeds']
else:
rng_seeds_seed = extra_params.get('rng_seeds_seed') or 42
rng = NumpyRNG(rng_seeds_seed)
rng_seeds = (rng.rng.uniform(size=num_threads*num_processes())*100000).astype('int').tolist()
logger.debug("rng_seeds = %s" % rng_seeds)
nest.SetKernelStatus({'local_num_threads': num_threads,
'rng_seeds' : rng_seeds})
# set resolution
nest.SetKernelStatus({'resolution': float(timestep)})
# Set min_delay and max_delay for all synapse models
for synapse_model in NEST_SYNAPSE_TYPES:
nest.SetDefaults(synapse_model, {'delay' : float(min_delay),
'min_delay': float(min_delay),
'max_delay': float(max_delay)})
simulator.reset()
return rank()
