def test_ConvergentDivergentConnectOptions(self):
"""Convergent/DivergentConnect with non-standard options and
ensure that the option settings are properly restored before
returning."""
nest.ResetKernel()
copts = nest.sli_func(
'GetOptions', '/RandomConvergentConnect', litconv=True)
dopts = nest.sli_func(
'GetOptions', '/RandomDivergentConnect', litconv=True)
ncopts = dict((k, not v)
for k, v in copts.items() if k != 'DefaultOptions')
ndopts = dict((k, not v)
for k, v in dopts.items() if k != 'DefaultOptions')
n = nest.Create('iaf_neuron', 3)
nest.RandomConvergentConnect(n, n, 1, options=ncopts)
nest.RandomDivergentConnect(n, n, 1, options=ndopts)
opts = nest.sli_func(
'GetOptions', '/RandomConvergentConnect', litconv=True)
self.assertEqual(copts, opts)
opts = nest.sli_func(
'GetOptions', '/RandomDivergentConnect', litconv=True)
self.assertEqual(dopts, opts)
def spore_connection_test(self, resolution, interval, delay, exp_len,
synapse_properties, times, values):
nest.ResetKernel()
nest.SetKernelStatus({"resolution": resolution})
nest.sli_func('InitSynapseUpdater', interval, delay)
nest.CopyModel("spore_test_node", "test_tracing_node",
{"test_name": "test_tracing_node"})
nodes = nest.Create("test_tracing_node", 2)
synapse_properties["reward_transmitter"] = nodes[0]
nest.CopyModel("synaptic_sampling_rewardgradient_synapse",
"test_synapse")
nest.SetDefaults("test_synapse", synapse_properties)
nest.Connect([nodes[0]], [nodes[1]], "one_to_one",
{"model": "test_synapse"})
conns = nest.GetConnections([nodes[0]], [nodes[1]], "test_synapse")
nest.SetStatus(conns, {"recorder_interval": 100.0})
nest.Simulate(exp_len)
results = nest.GetStatus(
conns, ["recorder_times", "synaptic_parameter_values"])
self.assertAlmostEqual(
np.sum((np.array(results[0][0]) - np.array(times))**2), 0.0)
self.assertAlmostEqual(
np.sum((np.array(results[0][1]) - np.array(values))**2), 0.0)
def test_ConnectOptions(self):
"""ConnectOptions"""
nest.ResetKernel()
copts = nest.sli_func('GetOptions',
'/RandomConvergentConnect',
litconv=True)
dopts = nest.sli_func('GetOptions',
'/RandomDivergentConnect',
litconv=True)
ncopts = dict([(k, not v) for k, v in copts.iteritems()
if k != 'DefaultOptions'])
ndopts = dict([(k, not v) for k, v in dopts.iteritems()
if k != 'DefaultOptions'])
n = nest.Create('iaf_neuron', 3)
nest.RandomConvergentConnect(n, n, 1, options=ncopts)
nest.RandomDivergentConnect(n, n, 1, options=ndopts)
self.assertEqual(
copts,
nest.sli_func('GetOptions',
'/RandomConvergentConnect',
litconv=True))
self.assertEqual(
dopts,
nest.sli_func('GetOptions',
'/RandomDivergentConnect',
litconv=True))
def test_ConvergentDivergentConnectOptions(self):
"""Convergent/DivergentConnect with non-standard options and
ensure that the option settings are properly restored before
returning."""
nest.ResetKernel()
copts = nest.sli_func('GetOptions',
'/RandomConvergentConnect',
litconv=True)
dopts = nest.sli_func('GetOptions',
'/RandomDivergentConnect',
litconv=True)
ncopts = dict(
(k, not v) for k, v in copts.items() if k != 'DefaultOptions')
ndopts = dict(
(k, not v) for k, v in dopts.items() if k != 'DefaultOptions')
n = nest.Create('iaf_neuron', 3)
nest.RandomConvergentConnect(n, n, 1, options=ncopts)
nest.RandomDivergentConnect(n, n, 1, options=ndopts)
opts = nest.sli_func('GetOptions',
'/RandomConvergentConnect',
litconv=True)
self.assertEqual(copts, opts)
opts = nest.sli_func('GetOptions',
'/RandomDivergentConnect',
litconv=True)
self.assertEqual(dopts, opts)
def spore_connection_test(self, p_sim, synapse_properties, target_values):
nest.ResetKernel()
nest.SetKernelStatus({"resolution": p_sim["resolution"]})
nest.sli_func('InitSynapseUpdater', p_sim["interval"], p_sim["delay"])
nest.CopyModel("spore_test_node", "test_tracing_node", {"test_name": "test_tracing_node"})
nodes = nest.Create("test_tracing_node", p_sim["num_synapses"])
synapse_properties["reward_transmitter"] = nodes[0]
nest.CopyModel("synaptic_sampling_rewardgradient_synapse", "test_synapse")
nest.SetDefaults("test_synapse", synapse_properties)
nest.Connect([nodes[0]], nodes, "all_to_all", {"model": "test_synapse"})
conns = nest.GetConnections([nodes[0]], nodes, "test_synapse")
syn_param = np.arange(0, 1, 1.0 / 100)
nest.SetStatus(conns, [{"recorder_interval": 100.0, "synaptic_parameter": p} for p in syn_param])
frame = p_sim["frame"]
num_syn = np.zeros(int(p_sim["exp_len"] / frame) + 1)
for i, t in enumerate(np.arange(0, p_sim["exp_len"], frame)):
nest.Simulate(frame)
conns = nest.GetConnections([nodes[0]], nodes, "test_synapse")
num_syn[i] = len(conns)
self.assertAlmostEqual(np.sum((num_syn - np.array(target_values))**2), 0.0)
def spore_connection_test(self, resolution, interval, delay, exp_len):
nest.ResetKernel()
nest.SetKernelStatus({"resolution": resolution})
nest.sli_func('InitSynapseUpdater', interval, delay)
nest.CopyModel("spore_test_node", "test_tracing_node",
{"test_name": "test_tracing_node"})
nodes = nest.Create("test_tracing_node", 2)
nest.CopyModel("spore_test_synapse", "test_synapse")
nest.SetDefaults("test_synapse", {"weight_update_time": 100.0})
nest.Connect([nodes[0]], [nodes[1]], "one_to_one",
{"model": "test_synapse"})
nest.Simulate(exp_len)
conns = nest.GetConnections([nodes[0]], [nodes[1]], "test_synapse")
results = nest.GetStatus(conns, ["recorder_times", "recorder_values"])
offset = 1.0 if (resolution <= 1.0) else resolution
self.assertAlmostEqual(
np.mean(
np.abs(results[0][0] -
np.arange(0.0, exp_len - offset, resolution))), 0.0)
self.assertAlmostEqual(
np.mean(
np.abs(results[0][1] -
np.arange(0.0, (exp_len - offset) / resolution))), 0.0)
def test_7_bad_property_error_reward_transmitter(self):
nest.ResetKernel()
nest.SetKernelStatus({"resolution": 1.0})
nest.sli_func('InitSynapseUpdater', 100, 100)
nest.CopyModel("spore_test_node", "test_tracing_node",
{"test_name": "test_tracing_node"})
nodes = nest.Create("test_tracing_node", 2)
nest.CopyModel("synaptic_sampling_rewardgradient_synapse",
"test_synapse")
nest.SetDefaults("test_synapse", {
"temperature": 0.0,
"synaptic_parameter": 1.0
})
nest.Connect([nodes[0]], [nodes[1]], "one_to_one",
{"model": "test_synapse"})
try:
nest.Simulate(1000)
self.fail("Expected exception of type NESTError, but got nothing")
except Exception as e:
self.assertEqual(
type(e).__name__, "NESTError",
"Expected exception of type NESTError, but got: '" +
type(e).__name__ + "'")
def main():
RESOLUTION = s_to_ms(0.001)
SIMULATION_TIME = s_to_ms(0.5)
N_STEPS = int(SIMULATION_TIME / RESOLUTION)
TRACE_LENGTH = N_STEPS + 1
nest.ResetKernel()
nest.SetKernelStatus({"resolution": RESOLUTION})
nest.Install("sporemodule")
nest.sli_func('InitSynapseUpdater', N_STEPS, 0)
proxy = nest.Create("reward_in_proxy")
nest.SetStatus(proxy, {'port_name': 'in', 'delay': 0.0})
nest.Simulate(SIMULATION_TIME)
traces = nest.GetStatus(proxy, ("trace"))[0]
for i, trace in enumerate(traces):
assert len(trace) == TRACE_LENGTH
# NEST-inherent delay of one timestep forces first value to be unchanged
assert trace[0] == 0.0, "Buffer on NEST side is initialized with 0."
assert trace[N_STEPS] == 0.0, "Unused value is zero"
# [0.0, received values ..., 0.0], #elements = N_STEPS + 1
values = np.array(list(map(s_to_ms, trace[1:N_STEPS])))
expect = np.arange(0.0, SIMULATION_TIME, RESOLUTION)[:-1] + s_to_ms(float(i))
assert np.allclose(values, expect), "{}\n{}".format(values, expect)
def suite():
# MPI tests
if HAVE_MPI:
try:
# Get the MPI command
command = nest.sli_func(
"mpirun", 2, "python", "test_sp/mpitest_issue_578_sp.py")
print ("Executing test with command: " + command)
command = command.split()
call(command)
except:
print (sys.exc_info()[0])
print ("Test call with MPI ended in error")
raise
test_suite = unittest.TestSuite()
test_suite.addTest(test_synaptic_elements.suite())
test_suite.addTest(test_conn_builder.suite())
test_suite.addTest(test_growth_curves.suite())
test_suite.addTest(test_sp_manager.suite())
test_suite.addTest(test_synaptic_elements.suite())
test_suite.addTest(test_disconnect.suite())
test_suite.addTest(test_disconnect_multiple.suite())
test_suite.addTest(test_enable_multithread.suite())
test_suite.addTest(test_get_sp_status.suite())
return test_suite
def suite():
if HAVE_MPI:
try:
mpitests = ["mpitest_issue_578_sp.py"]
path = os.path.dirname(__file__)
for test in mpitests:
test = os.path.join(path, test)
command = nest.sli_func("mpirun", 2, "python", test)
print ("Executing test with command: " + command)
command = command.split()
my_env = os.environ.copy()
try:
my_env.pop("DELAY_PYNEST_INIT")
except:
pass
call(command, env=my_env)
except:
print (sys.exc_info()[0])
print ("Test call with MPI ended in error")
raise
test_suite = unittest.TestSuite()
test_suite.addTest(test_synaptic_elements.suite())
test_suite.addTest(test_conn_builder.suite())
test_suite.addTest(test_growth_curves.suite())
test_suite.addTest(test_sp_manager.suite())
test_suite.addTest(test_synaptic_elements.suite())
test_suite.addTest(test_disconnect.suite())
test_suite.addTest(test_disconnect_multiple.suite())
test_suite.addTest(test_enable_multithread.suite())
test_suite.addTest(test_get_sp_status.suite())
return test_suite
def testWithMPI(self):
# Check that we can import mpi4py
try:
from mpi4py import MPI
except ImportError:
raise unittest.SkipTest("mpi4py required")
directory = os.path.dirname(os.path.realpath(__file__))
scripts = ["test_connect_all_to_all.py",
"test_connect_one_to_one.py",
"test_connect_fixed_indegree.py",
"test_connect_fixed_outdegree.py",
"test_connect_fixed_total_number.py",
"test_connect_pairwise_bernoulli.py"
]
failing_tests = []
for script in scripts:
test_script = os.path.join(directory, script)
command = nest.sli_func("mpirun", 2, "nosetests",
test_script)
command = command.split()
process = sp.Popen(command, stdout=sp.PIPE, stderr=sp.PIPE)
stdout, stderr = process.communicate()
retcode = process.returncode
if retcode != 0:
failing_tests.append(script)
self.assertTrue(not failing_tests, 'The following tests failed when ' +
'executing with "mpirun -np 2 nosetests [script]": ' +
", ".join(failing_tests))
def suite():
# MPI tests
if HAVE_MPI:
try:
# Get the MPI command
command = nest.sli_func("mpirun", 2, "python",
"test_sp/mpitest_issue_578_sp.py")
print("Executing test with command: " + command)
command = command.split()
call(command)
except:
print(sys.exc_info()[0])
print("Test call with MPI ended in error")
raise
test_suite = unittest.TestSuite()
test_suite.addTest(test_synaptic_elements.suite())
test_suite.addTest(test_conn_builder.suite())
test_suite.addTest(test_growth_curves.suite())
test_suite.addTest(test_sp_manager.suite())
test_suite.addTest(test_synaptic_elements.suite())
test_suite.addTest(test_disconnect.suite())
test_suite.addTest(test_disconnect_multiple.suite())
test_suite.addTest(test_enable_multithread.suite())
test_suite.addTest(test_get_sp_status.suite())
return test_suite
def memory(self):
"""
Use NEST's memory wrapper function to record used memory.
"""
mem = nest.sli_func('memory_thisjob')
if isinstance(mem, dict):
return mem['heap']
else:
return mem
def connect_csa(cset, pre, post, syn_type):
print "connecting using cset"
if isinstance(pre, Population) and isinstance(post, Population):
# contiguous IDs, so just pass first_id and size
nest.sli_func("Connect_cg_i_i_i_i_D_l",
self.cset,
pre.first_id, pre.size,
post.first_id, post.size,
{'weight': 0, 'delay': 1}, # ignored if arity==0
syn_type)
else: # PopulationViews or Assemblies
# IDs may be non-contiguous, so need to pass entire arrays
nest.sli_func("Connect_cg_a_a_D_l",
self.cset,
pre.all_cells,
post.all_cells,
{'weight': 0, 'delay': 1}, # ignored if arity==0
syn_type)
def test_ConnectOptions(self):
"""ConnectOptions"""
nest.ResetKernel()
copts = nest.sli_func('GetOptions', '/RandomConvergentConnect', litconv=True)
dopts = nest.sli_func('GetOptions', '/RandomDivergentConnect', litconv=True)
ncopts = dict([(k, not v) for k,v in copts.iteritems() if k != 'DefaultOptions'])
ndopts = dict([(k, not v) for k,v in dopts.iteritems() if k != 'DefaultOptions'])
n = nest.Create('iaf_neuron', 3)
nest.RandomConvergentConnect(n, n, 1, options=ncopts)
nest.RandomDivergentConnect (n, n, 1, options=ndopts)
self.assertEqual(copts,
nest.sli_func('GetOptions', '/RandomConvergentConnect', litconv=True))
self.assertEqual(dopts,
nest.sli_func('GetOptions', '/RandomDivergentConnect', litconv=True))
def topology_func(slifunc, *args):
"""
Execute slifunc with args in topology namespace.
See also: nest.sli_func
"""
# Execute function in topology namespace.
# We need to pass the kwarg namespace as **{} instead of the usuual
# way to keep Python 2.5 happy, see http://bugs.python.org/issue3473
return nest.sli_func(slifunc, *args, **{'namespace': 'topology'})
def memory(self):
"""
Use NEST's memory wrapper function to record used memory.
"""
try:
mem = nest.ll_api.sli_func('memory_thisjob')
except AttributeError:
mem = nest.sli_func('memory_thisjob')
if isinstance(mem, dict):
return mem['heap']
else:
return mem
def ComputePSPnorm(tauMem, CMem, tauSyn):
"""Compute the maximum of postsynaptic potential
for a synaptic input current of unit amplitude
(1 pA)"""
a = (tauMem / tauSyn)
b = (1.0 / tauSyn - 1.0 / tauMem)
# time of maximum
t_max = 1.0/b * ( -nest.sli_func('LambertWm1',-exp(-1.0/a)/a) - 1.0/a )
# maximum of PSP for current of unit amplitude
return exp(1.0)/(tauSyn*CMem*b) * ((exp(-t_max/tauMem) - exp(-t_max/tauSyn)) / b - t_max*exp(-t_max/tauSyn))
def psp_height(tau_m, R_m, tau_syn):
"""
Calculate the height of the EPSP for a synaptic current with
peak amplitude 1 nA.
tau_m and tau_syn: ms
R_m: Mohm
"""
a = (tau_m / tau_syn)
b = (1.0 / tau_syn - 1.0 / tau_m)
# time of maximum
t_max = 1.0/b * (-nest.sli_func('LambertWm1', -exp(-1.0/a)/a) - 1.0/a)
# height of PSP for current of amplitude 1 nA
return 1/(tau_syn*tau_m*b/R_m) * ((exp(-t_max/tau_m) - exp(-t_max/tau_syn)) / b - t_max*exp(-t_max/tau_syn))
def psp_height(tau_m, R_m, tau_syn):
"""
Calculate the height of the EPSP for a synaptic current with
peak amplitude 1 nA.
tau_m and tau_syn: ms
R_m: Mohm
"""
a = (tau_m / tau_syn)
b = (1.0 / tau_syn - 1.0 / tau_m)
# time of maximum
t_max = 1.0/b * (-nest.sli_func('LambertWm1', -exp(-1.0/a)/a) - 1.0/a)
# height of PSP for current of amplitude 1 nA
return 1/(tau_syn*tau_m*b/R_m) * ((exp(-t_max/tau_m) - exp(-t_max/tau_syn)) / b - t_max*exp(-t_max/tau_syn))
def ComputePSPnorm(tauMem, CMem, tauSyn):
"""Compute the maximum of postsynaptic potential
for a synaptic input current of unit amplitude
(1 pA)"""
a = (tauMem / tauSyn)
b = (1.0 / tauSyn - 1.0 / tauMem)
# time of maximum
t_max = 1.0 / b * (-nest.sli_func('LambertWm1', -exp(-1.0 / a) / a) -
1.0 / a)
# maximum of PSP for current of unit amplitude
return exp(1.0) / (tauSyn * CMem * b) * (
(exp(-t_max / tauMem) - exp(-t_max / tauSyn)) / b -
t_max * exp(-t_max / tauSyn))
def compute_normalised_psr(cls, tauMem, R, tauSyn):
"""Compute the maximum of postsynaptic potential
for a synaptic input current of unit amplitude
(1 pA)"""
a = float(tauMem / tauSyn)
b = (1.0 / tauSyn - 1.0 / tauMem)
# time of maximum
t_max = 1.0 / b * \
(-nest.sli_func('LambertWm1', -exp(-1.0 / a) / a) - 1.0 / a)
# maximum of PSP for current of unit amplitude
return exp(1) / (tauSyn * tauMem * b / R) * (
(exp(-float(t_max / tauMem)) - exp(-float(t_max / tauSyn))) /
b - t_max * exp(-float(t_max / tauSyn)))
def compute_normalised_psr(cls, tauMem, R, tauSyn):
"""Compute the maximum of postsynaptic potential
for a synaptic input current of unit amplitude
(1 pA)"""
a = float(tauMem / tauSyn)
b = (1.0 / tauSyn - 1.0 / tauMem)
# time of maximum
t_max = 1.0 / b * \
(-nest.sli_func('LambertWm1', -exp(-1.0 / a) / a) - 1.0 / a)
# maximum of PSP for current of unit amplitude
return exp(1) / (tauSyn * tauMem * b / R) * (
(exp(-float(t_max / tauMem)) - exp(-float(t_max / tauSyn))) / b -
t_max * exp(-float(t_max / tauSyn)))
def find_loc_pspmax(tau_s, tau_m):
'''
This function finds the exact location of the maximum of the PSP caused
by a single input spike. The location is obtained by setting the first
derivative of the equation for the PSP (see `make_psp()`) to zero. The
resulting equation can be expressed ain terms of a LambertW function. This
function is implemented in nest as a .sli file. In order to access this
function in pynest we called the function 'nest.sli_func()'.
This function expects:
Tau_s and Tau_m: the synaptic and membrane time constant (in sec)
It returns the location of the maximum (in sec)
'''
var = tau_m / tau_s
lam = nest.sli_func('LambertWm1', -numpy.exp(-1 / var) / var)
t_maxpsp = (-var * lam - 1) / var / (1 / tau_s - 1 / tau_m) * 1e-3
return t_maxpsp
The new implementation binds V_m to be smaller than 0.
Rationale of the test:
- all models should be close to the reference LSODAR when
submitted to the same excitatory current.
- for ``Delta_T = 0.``, ``a = 0.`` and ``b = 0.``, starting from ``w = 0.``,
models should behave as the associated iaf model. This is tested both with
spike input and direct current input.
Details:
The models are compared and we assess that the difference between the
recorded variables and the reference is smaller than a given tolerance.
"""
HAVE_GSL = nest.sli_func("statusdict/have_gsl ::")
path = os.path.abspath(os.path.dirname(__file__))
# --------------------------------------------------------------------------- #
# Tolerances to compare LSODAR and NEST implementations
# -------------------------
#
# higher for the potential because of the divergence at spike times
di_tolerances_lsodar = {
"aeif_cond_alpha": {"V_m": 5e-4, "w": 1e-4},
"aeif_cond_exp": {"V_m": 5e-4, "w": 1e-4},
"aeif_psc_alpha": {"V_m": 5e-4, "w": 1e-4},
"aeif_psc_exp": {"V_m": 5e-4, "w": 1e-4},
"aeif_psc_delta": {"V_m": 5e-4, "w": 1e-4},
"aeif_cond_alpha_multisynapse": {"V_m": 5e-4, "w": 1e-4},
DisplacementDependentProbabilityConnector,
IndexBasedProbabilityConnector,
SmallWorldConnector,
FromListConnector,
FromFileConnector,
CloneConnector,
ArrayConnector,
FixedTotalNumberConnector)
from .random import NativeRNG, NEST_RDEV_TYPES
logger = logging.getLogger("PyNN")
if not nest.sli_func("statusdict/have_libneurosim ::"):
print(("CSAConnector: libneurosim support not available in NEST.\n" +
"Falling back on PyNN's default CSAConnector.\n" +
"Please re-compile NEST using --with-libneurosim=PATH"))
from pyNN.connectors import CSAConnector
else:
class CSAConnector(Connector):
"""
Use the Connection-Set Algebra (Djurfeldt, 2012) to connect
cells. This is an optimized variant of CSAConnector, which
iterates the connection-set on the C++ level in NEST.
def __init__(self):
PyNestNode.__init__(self, total_simulation_time, pre_run_barrier=True)
logger.info("Setting up NEST and installing SPORE")
if nest_n_threads is None:
import multiprocessing
n_threads = int(
1.1 *
(multiprocessing.cpu_count() - MPI.COMM_WORLD.Get_size()))
logger.info("> n_threads = {} (autodetect)".format(n_threads))
else:
n_threads = nest_n_threads
logger.info("> n_threads = {} (fixed configuration)".format(
nest_n_threads))
nest.SetKernelStatus({
"resolution": s_to_ms(network_node_time_step),
"overwrite_files": True,
"local_num_threads": n_threads
})
nest.Install("sporemodule")
logger.info("> Synaptic update interval: {}s".format(
s_to_ms(synapse_update_interval)))
nest.sli_func("InitSynapseUpdater",
int(s_to_ms(synapse_update_interval)),
int(s_to_ms(synapse_update_interval)))
# initialize random values, see NEST documentation
N_vp = nest.GetKernelStatus(["total_num_virtual_procs"])[0]
nest.SetKernelStatus({"grng_seed": random_seed + N_vp})
nest.SetKernelStatus({
"rng_seeds":
list(range(random_seed + N_vp + 1, random_seed + 2 * N_vp + 1))
})
# ------------- NETWORK CONSTRUCTION -------------
logger.info("Constructing network")
nest.CopyModel("poisson_dbl_exp_neuron", "hidden_neuron",
hidden_neuron_properties)
nest.CopyModel("poisson_dbl_exp_neuron", "inhibitory_neuron",
inhibitory_neuron_properties)
nest.CopyModel("static_synapse", "e_to_i_synapse")
nest.CopyModel("static_synapse", "i_to_e_synapse")
nest.CopyModel("synaptic_sampling_rewardgradient_synapse",
"reward_synapse")
logger.info(
"> Opening port and creating proxy for incoming reward signal")
reward_in = nest.Create('reward_in_proxy')
nest.SetStatus(reward_in, music_network_node_reward_in_port)
synapse_properties["reward_transmitter"] = reward_in[0]
nest.SetDefaults("reward_synapse", synapse_properties)
logger.info(
"> Opening port and creating proxy for incoming spike patterns")
input_neurons = self._create_event_in_proxy(
n_input_neurons, **music_network_node_pattern_in_port)
logger.info("> Creating hidden neurons")
hidden_neurons = nest.Create("hidden_neuron", n_hidden_neurons)
up_neurons = nest.Create("hidden_neuron", n_up_neurons)
down_neurons = nest.Create("hidden_neuron", n_down_neurons)
logger.info("> Creating inhibitory neurons")
inhibitory_neurons = nest.Create("inhibitory_neuron",
n_inhibitory_neurons)
logger.info(
"> Establishing synaptic connection between input and hidden neurons"
)
for i in range(n_max_connections_input_hidden):
nest.Connect(input_neurons, hidden_neurons[:n_input_hidden], {
"rule": "pairwise_bernoulli",
"p": p_connection_input_hidden
}, {
"model": "reward_synapse",
"synaptic_parameter": {
"distribution": "normal",
"mu": synapse_init_mean,
"sigma": synapse_init_std
}
})
logger.info(
"> Establishing synaptic connection between hidden and hidden neurons"
)
for i in range(n_max_connections_hidden_hidden):
nrns = hidden_neurons + up_neurons + down_neurons
nest.Connect(
nrns, nrns, {
"rule": "pairwise_bernoulli",
"p": p_connection_hidden_hidden,
"autapses": False
}, {
"model": "reward_synapse",
"synaptic_parameter": {
"distribution": "normal",
"mu": synapse_init_mean,
"sigma": synapse_init_std
}
})
logger.info(
"> Establishing synaptic connection between inhibitory neurons and hidden neurons"
)
# Connect network to hidden neurons
e_to_i_conspec = {"rule": "pairwise_bernoulli", "p": 0.575}
e_to_i_synspec = {
"model": "static_synapse",
"weight": {
"distribution": "normal_clipped",
"mu": weight_mean_e_to_i,
"sigma": weight_std_e_to_i,
"low": 0.0
},
"delay": 1.0
}
i_to_e_conspec = {"rule": "pairwise_bernoulli", "p": 0.600}
i_to_e_synspec = {
"model": "static_synapse",
"weight": {
"distribution": "normal_clipped",
"mu": weight_mean_i_to_e,
"sigma": weight_std_i_to_e,
"high": 0.0
},
"receptor_type": 1,
"delay": 1.0
}
i_to_i_conspec = {"rule": "pairwise_bernoulli", "p": 0.550}
i_to_i_synspec = {
"model": "static_synapse",
"weight": {
"distribution": "normal_clipped",
"mu": weight_mean_i_to_e,
"sigma": weight_std_i_to_e,
"high": 0.0
},
"receptor_type": 1,
"delay": 1.0
}
nest.Connect(hidden_neurons, inhibitory_neurons, e_to_i_conspec,
e_to_i_synspec)
nest.Connect(up_neurons, inhibitory_neurons, e_to_i_conspec,
e_to_i_synspec)
nest.Connect(down_neurons, inhibitory_neurons, e_to_i_conspec,
e_to_i_synspec)
nest.Connect(inhibitory_neurons, hidden_neurons, i_to_e_conspec,
i_to_e_synspec)
nest.Connect(inhibitory_neurons, up_neurons, i_to_e_conspec,
i_to_e_synspec)
nest.Connect(inhibitory_neurons, down_neurons, i_to_e_conspec,
i_to_e_synspec)
nest.Connect(inhibitory_neurons, inhibitory_neurons, i_to_i_conspec,
i_to_i_synspec)
logger.info(
"> Opening ports for outgoing spikes; Establishing connection to hidden neurons"
)
self._create_event_out_proxy(up_neurons + down_neurons,
**music_network_node_activity_out_port)
except ImportError:
haveCSA = False
from pyNN import random
from pyNN.connectors import (
Connector, AllToAllConnector, FixedProbabilityConnector, OneToOneConnector,
FixedNumberPreConnector, FixedNumberPostConnector,
DistanceDependentProbabilityConnector,
DisplacementDependentProbabilityConnector, IndexBasedProbabilityConnector,
SmallWorldConnector, FromListConnector, FromFileConnector, CloneConnector,
ArrayConnector, FixedTotalNumberConnector)
from .random import NativeRNG, NEST_RDEV_TYPES
logger = logging.getLogger("PyNN")
if not nest.sli_func("statusdict/have_libneurosim ::"):
print(("CSAConnector: libneurosim support not available in NEST.\n" +
"Falling back on PyNN's default CSAConnector.\n" +
"Please re-compile NEST using --with-libneurosim=PATH"))
from pyNN.connectors import CSAConnector
else:
class CSAConnector(Connector):
"""
Use the Connection-Set Algebra (Djurfeldt, 2012) to connect
cells. This is an optimized variant of CSAConnector, which
iterates the connection-set on the C++ level in NEST.
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
from scipy.integrate import quad
import math
import numpy
from numpy import testing
import pylab
import unittest
import nest
from nest import raster_plot
import time
HAVE_OPENMP = nest.sli_func("is_threaded")
class SynapticElementIntegrator(object):
"""
Generic class which describes how to compute the number of
Synaptic Element based on Ca value
Each derived class should overwrite the get_se(self, t) method
"""
def __init__(self, tau_ca=10000.0, beta_ca=0.001):
"""
Constructor
:param tau_ca (float): time constant of Ca decay
:param beta_ca (float): each spike increase Ca value by this value
import nest
import unittest
import numpy as np
__author__ = 'naveau'
try:
from mpi4py import MPI
except ImportError:
# Test without MPI
mpi_test = 0
else:
# Test with MPI
mpi_test = 1
mpi_test = nest.sli_func("statusdict/have_mpi ::") & mpi_test
class TestDisconnectSingle(unittest.TestCase):
def setUp(self):
nest.ResetKernel()
nest.set_verbosity('M_ERROR')
self.num_procs = 1
if mpi_test:
self.comm = MPI.COMM_WORLD
self.rank = self.comm.Get_rank()
assert(nest.Rank() == self.rank)
self.num_procs = 2
self.exclude_synapse_model = [
'stdp_dopamine_synapse',
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
import os
import subprocess as sp
import unittest
import nest
HAVE_MPI = nest.sli_func("statusdict/have_mpi ::")
class TestConnectAllPatterns(unittest.TestCase):
@unittest.skipIf(not HAVE_MPI, 'NEST was compiled without MPI')
def testWithMPI(self):
# Check that we can import mpi4py
try:
from mpi4py import MPI
except ImportError:
raise unittest.SkipTest("mpi4py required")
directory = os.path.dirname(os.path.realpath(__file__))
scripts = ["test_connect_all_to_all.py",
"test_connect_one_to_one.py",
"test_connect_fixed_indegree.py",
def _RandomPopulationConnect(self, pre, post, n, param_dict,
synapse_model='static_synapse'):
'''...'''
nest.sli_func('RandomPopulationConnectD', pre, post, n, param_dict,
'/' + synapse_model, litconv=True)
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
# This script tests the siegert_neuron in NEST.
import nest
import unittest
import numpy as np
HAVE_GSL = nest.sli_func("statusdict/have_gsl ::")
@nest.check_stack
@unittest.skipIf(not HAVE_GSL, 'GSL is not available')
class SiegertNeuronTestCase(unittest.TestCase):
"""
Test siegert_neuron
Details
-------
Compares the rate of a Poisson-driven iaf_psc_delta neuron
with the prediction from the siegert neuron.
"""
def setUp(self):
# test parameter to compare analytic solution to simulation
def spore_connection_test(self, resolution, interval, delay, exp_len):
spike_times_in = [10.0, 15.0, 20.0, 25.0, 50.0]
spike_times_out = [40.0, 50.0, 60.0, 70.0]
synapse_properties = {"weight_update_interval": 100.0, "temperature": 0.0,
"synaptic_parameter": 1.0, "reward_transmitter": None,
"learning_rate": 0.0001, "episode_length": 100.0,
"max_param": 100.0, "min_param": -100.0, "max_param_change": 100.0,
"integration_time": 10000.0}
nest.ResetKernel()
nest.SetKernelStatus({"resolution": resolution})
nest.sli_func("InitSynapseUpdater", interval, delay)
nest.CopyModel("spore_test_node", "test_pulse_trace", {"test_name": "test_pulse_trace",
"spike_times": np.array(spike_times_out),
"weight": 1.0,
"offset": -0.2})
# Create spike generators and connect
gin = nest.Create("spike_generator", params={"spike_times": np.array(spike_times_in)})
nout = nest.Create("test_pulse_trace")
nodes = nest.Create("test_pulse_trace", 1)
nest.CopyModel("synaptic_sampling_rewardgradient_synapse", "test_synapse")
synapse_properties["reward_transmitter"] = nodes[0]
nest.SetDefaults("test_synapse", synapse_properties)
nest.Connect([gin[0]], [nout[0]], "one_to_one", {"model": "test_synapse"})
conns = nest.GetConnections([gin[0]], [nout[0]], "test_synapse")
nest.SetStatus(conns, {"recorder_interval": 100.0})
nest.Simulate(exp_len)
results = nest.GetStatus(conns, ["recorder_times", "synaptic_parameter_values"])
times = [0.0, 100.0, 200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0,
1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0, 1900.0,
2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0, 2800.0,
2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0, 3700.0,
3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0, 4600.0,
4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0, 5500.0,
5600.0, 5700.0, 5800.0, 5900.0, 6000.0, 6100.0, 6200.0, 6300.0, 6400.0,
6500.0, 6600.0, 6700.0, 6800.0, 6900.0, 7000.0, 7100.0, 7200.0, 7300.0,
7400.0, 7500.0, 7600.0, 7700.0, 7800.0, 7900.0, 8000.0, 8100.0, 8200.0,
8300.0, 8400.0, 8500.0, 8600.0, 8700.0, 8800.0, 8900.0, 9000.0, 9100.0,
9200.0, 9300.0, 9400.0, 9500.0, 9600.0, 9700.0, 9800.0, 9900.0]
values = [1.0, 1.1941197605580636, 1.5774259143750948, 2.0259103145644266, 2.4921365591257456,
2.9587360738461674, 3.4194121087993565, 3.871939771677826, 4.315590899450609, 4.750186736885722,
5.175749451568903, 5.592373948376652, 6.000180727410171, 6.399298555975304, 6.789858108361998,
7.171989640278706, 7.545822147382392, 7.911483069607812, 8.269098196118081, 8.618791643894623,
8.960685863254373, 9.29490165311173, 9.621558179666037, 9.940772996191743, 10.252662063078569,
10.557339767809804, 10.854918944765805, 11.14551089481294, 11.429225404665099, 11.706170766014766,
11.976453794434303, 12.240179848049316, 12.497452845986553, 12.748375286598817, 12.993048265469508,
13.231571493199345, 13.464043312977822, 13.690560717941914, 13.91121936832454, 14.126113608395242,
14.33533648319552, 14.53897975507124, 14.737133920004482, 14.929888223747206, 15.11733067775905,
15.299548074951568, 15.476626005241176, 15.64864887091307, 15.815699901798324, 15.97786117026638,
16.13521360603509, 16.287837010800473, 16.43581007268829, 16.579210380529563, 16.71811443796208,
16.852597677359963, 16.982734473593325, 17.10859815762, 17.230261029911343, 17.34779437371405,
17.461268468149946, 17.570752601155647, 17.67631508226397, 17.778023255228995, 17.875943510496594,
17.97014129752227, 18.06068113693813, 18.147626632570702, 18.23104048331149, 18.31098449484185,
18.38751959121408, 18.46070582629026, 18.530602395040663, 18.597267644703344, 18.66075908580651,
18.721133403055404, 18.778446466085196, 18.832753340081556, 18.884108296270426, 18.93256482227858,
18.978175632366465, 19.0209926775349, 19.061067155507057, 19.098449520587284, 19.133189493398152,
19.165336070497265, 19.194937533875176, 19.222041460335888, 19.24669473076129, 19.268943539260974,
19.288833402208706, 19.306409167166986, 19.321715021701, 19.33479450208325, 19.345690501890225,
19.35444528049237, 19.361100471438604, 19.365697090736695, 19.3682755450307, 19.368875639676705]
self.assertAlmostEqual(np.sum((np.array(results[0][0]) - np.array(times))**2), 0.0)
self.assertAlmostEqual(np.sum((np.array(results[0][1]) - np.array(values))**2), 0.0)
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
from scipy.integrate import quad
import math
import numpy
from numpy import testing
import pylab
import unittest
import nest
from nest import raster_plot
import time
HAVE_OPENMP = nest.sli_func("is_threaded")
class SynapticElementIntegrator(object):
"""
Generic class which describes how to compute the number of
Synaptic Element based on Ca value
Each derived class should overwrite the get_se(self, t) method
"""
def __init__(self, tau_ca=10000.0, beta_ca=0.001):
"""
Constructor
:param tau_ca (float): time constant of Ca decay
:param beta_ca (float): each spike increase Ca value by this value
"""
import nest
import unittest
import numpy as np
__author__ = 'naveau'
try:
from mpi4py import MPI
except ImportError:
# Test without MPI
mpi_test = 0
else:
# Test with MPI
mpi_test = 1
mpi_test = nest.sli_func("statusdict/have_mpi ::") & mpi_test
class TestDisconnectSingle(unittest.TestCase):
def setUp(self):
nest.ResetKernel()
nest.set_verbosity('M_ERROR')
self.num_procs = 1
if mpi_test:
self.comm = MPI.COMM_WORLD
self.rank = self.comm.Get_rank()
assert (nest.Rank() == self.rank)
self.num_procs = 2
self.exclude_synapse_model = [
'stdp_dopamine_synapse',
'stdp_dopamine_synapse_lbl',
