def __init__(self, sheet, parameters):
DirectStimulator.__init__(self, sheet,parameters)
exc_syn = self.sheet.sim.StaticSynapse(weight=self.parameters.exc_weight)
inh_syn = self.sheet.sim.StaticSynapse(weight=self.parameters.inh_weight)
if not self.sheet.parameters.mpi_safe:
from pyNN.nest import native_cell_type
if (self.parameters.exc_firing_rate != 0 or self.parameters.exc_weight != 0):
self.np_exc = self.sheet.sim.Population(1, native_cell_type("poisson_generator"),{'rate': 0})
self.sheet.sim.Projection(self.np_exc, self.sheet.pop,self.sheet.sim.AllToAllConnector(),synapse_type=exc_syn,receptor_type='excitatory')
if (self.parameters.inh_firing_rate != 0 or self.parameters.inh_weight != 0):
self.np_inh = self.sheet.sim.Population(1, native_cell_type("poisson_generator"),{'rate': 0})
self.sheet.sim.Projection(self.np_inh, self.sheet.pop,self.sheet.sim.AllToAllConnector(),synapse_type=inh_syn,receptor_type='inhibitory')
else:
if (self.parameters.exc_firing_rate != 0 or self.parameters.exc_weight != 0):
self.ssae = self.sheet.sim.Population(self.sheet.pop.size,self.sheet.sim.SpikeSourceArray())
seeds=mozaik.get_seeds((self.sheet.pop.size,))
self.stgene = [stgen.StGen(rng=numpy.random.RandomState(seed=seeds[i])) for i in numpy.nonzero(self.sheet.pop._mask_local)[0]]
self.sheet.sim.Projection(self.ssae, self.sheet.pop,self.sheet.sim.OneToOneConnector(),synapse_type=exc_syn,receptor_type='excitatory')
if (self.parameters.inh_firing_rate != 0 or self.parameters.inh_weight != 0):
self.ssai = self.sheet.sim.Population(self.sheet.pop.size,self.sheet.sim.SpikeSourceArray())
seeds=mozaik.get_seeds((self.sheet.pop.size,))
self.stgeni = [stgen.StGen(rng=numpy.random.RandomState(seed=seeds[i])) for i in numpy.nonzero(self.sheet.pop._mask_local)[0]]
self.sheet.sim.Projection(self.ssai, self.sheet.pop,self.sheet.sim.OneToOneConnector(),synapse_type=inh_syn,receptor_type='inhibitory')
def __init__(self, sheet, parameters):
DirectStimulator.__init__(self, sheet,parameters)
exc_syn = self.sheet.sim.StaticSynapse(weight=self.parameters.exc_weight,delay=self.sheet.model.parameters.min_delay)
inh_syn = self.sheet.sim.StaticSynapse(weight=self.parameters.inh_weight,delay=self.sheet.model.parameters.min_delay)
if not self.sheet.parameters.mpi_safe:
from pyNN.nest import native_cell_type
if (self.parameters.exc_firing_rate != 0 or self.parameters.exc_weight != 0):
self.np_exc = self.sheet.sim.Population(1, native_cell_type("poisson_generator"),{'rate': 0})
self.sheet.sim.Projection(self.np_exc, self.sheet.pop,self.sheet.sim.AllToAllConnector(),synapse_type=exc_syn,receptor_type='excitatory')
if (self.parameters.inh_firing_rate != 0 or self.parameters.inh_weight != 0):
self.np_inh = self.sheet.sim.Population(1, native_cell_type("poisson_generator"),{'rate': 0})
self.sheet.sim.Projection(self.np_inh, self.sheet.pop,self.sheet.sim.AllToAllConnector(),synapse_type=inh_syn,receptor_type='inhibitory')
else:
if (self.parameters.exc_firing_rate != 0 or self.parameters.exc_weight != 0):
self.ssae = self.sheet.sim.Population(self.sheet.pop.size,self.sheet.sim.SpikeSourceArray())
seeds=mozaik.get_seeds((self.sheet.pop.size,))
self.stgene = [StGen(rng=numpy.random.RandomState(seed=seeds[i])) for i in numpy.nonzero(self.sheet.pop._mask_local)[0]]
self.sheet.sim.Projection(self.ssae, self.sheet.pop,self.sheet.sim.OneToOneConnector(),synapse_type=exc_syn,receptor_type='excitatory')
if (self.parameters.inh_firing_rate != 0 or self.parameters.inh_weight != 0):
self.ssai = self.sheet.sim.Population(self.sheet.pop.size,self.sheet.sim.SpikeSourceArray())
seeds=mozaik.get_seeds((self.sheet.pop.size,))
self.stgeni = [StGen(rng=numpy.random.RandomState(seed=seeds[i])) for i in numpy.nonzero(self.sheet.pop._mask_local)[0]]
self.sheet.sim.Projection(self.ssai, self.sheet.pop,self.sheet.sim.OneToOneConnector(),synapse_type=inh_syn,receptor_type='inhibitory')
def __init__(self, sheet, parameters):
DirectStimulator.__init__(self, sheet, parameters)
population_selector = load_component(
self.parameters.population_selector.component)
self.ids = population_selector(
sheet, self.parameters.population_selector.params
).generate_idd_list_of_neurons()
d = dict((j, i) for i, j in enumerate(self.sheet.pop.all_cells))
self.local_and_to_stimulate_indexes = [
d[i] for i in set(self.ids) & set(self.sheet.pop.local_cells)
]
exc_syn = self.sheet.sim.StaticSynapse(
weight=self.parameters.exc_weight)
if (self.parameters.exc_firing_rate != 0
or self.parameters.exc_weight != 0):
self.ssae = self.sheet.sim.Population(
self.sheet.pop.size, self.sheet.sim.SpikeSourceArray())
seeds = mozaik.get_seeds((self.sheet.pop.size, ))
self.stgene = [
stgen.StGen(rng=numpy.random.RandomState(seed=seeds[i]))
for i in self.local_and_to_stimulate_indexes
]
self.sheet.sim.Projection(self.ssae,
self.sheet.pop,
self.sheet.sim.OneToOneConnector(),
synapse_type=exc_syn,
receptor_type='excitatory')
def __init__(self, model, parameters):
SensoryInputComponent.__init__(self, model, parameters)
self.shape = (self.parameters.density,self.parameters.density)
self.sheets = {}
self._built = False
self.rf_types = ('X_ON', 'X_OFF')
sim = self.model.sim
self.pops = {}
self.scs = {}
self.ncs = {}
self.ncs_rng = {}
for rf_type in self.rf_types:
p = RetinalUniformSheet(model,
ParameterSet({'sx': self.parameters.size[0],
'sy': self.parameters.size[1],
'density': self.parameters.density,
'cell': self.parameters.cell,
'name': rf_type,
'artificial_stimulators' : {},
'recorders' : self.parameters.recorders,
'mpi_safe': False}))
self.sheets[rf_type] = p
for rf_type in self.rf_types:
self.scs[rf_type] = []
self.ncs[rf_type] = []
self.ncs_rng[rf_type] = []
seeds=mozaik.get_seeds((self.sheets[rf_type].pop.size,))
for i, lgn_cell in enumerate(self.sheets[rf_type].pop.all_cells):
scs = sim.StepCurrentSource(times=[0.0], amplitudes=[0.0])
if not self.parameters.mpi_reproducible_noise:
ncs = sim.NoisyCurrentSource(**self.parameters.noise)
else:
ncs = sim.StepCurrentSource(times=[0.0], amplitudes=[0.0])
if self.sheets[rf_type].pop._mask_local[i]:
self.ncs_rng[rf_type].append(numpy.random.RandomState(seed=seeds[i]))
self.scs[rf_type].append(scs)
self.ncs[rf_type].append(ncs)
lgn_cell.inject(scs)
lgn_cell.inject(ncs)
P_rf = self.parameters.receptive_field
rf_function = eval(P_rf.func)
rf_ON = SpatioTemporalReceptiveField(rf_function,
P_rf.func_params,
P_rf.width, P_rf.height,
P_rf.duration)
rf_OFF = SpatioTemporalReceptiveField(lambda x, y, t, p: -1.0 * rf_function(x, y, t, p),
P_rf.func_params,
P_rf.width, P_rf.height,
P_rf.duration)
dx = dy = P_rf.spatial_resolution
dt = P_rf.temporal_resolution
for rf in rf_ON, rf_OFF:
rf.quantize(dx, dy, dt)
self.rf = {'X_ON': rf_ON, 'X_OFF': rf_OFF}
def _connect(self):
# This is due to native synapses models (which we currently use as the short term synaptic plasticity model)
# do not apply the 1000 factor scaler as the pyNN synaptic models
wf = self.parameters.weight_factor * self.weight_scaler
seeds = mozaik.get_seeds(self.target.pop.size)
weights = self.connection_matrix
delays = self.delay_matrix
cl = []
for i in range(0, self.target.pop.size):
co = Counter(
sample_from_bin_distribution(weights[:, i].flatten(),
int(self.parameters.num_samples),
seeds[i]))
cl.extend([(int(k), int(i),
wf * co[k] / self.parameters.num_samples, delays[k][i])
for k in co.keys()])
method = self.sim.FromListConnector(cl)
self.proj = self.sim.Projection(
self.source.pop,
self.target.pop,
method,
synapse_type=self.init_synaptic_mechanisms(),
label=self.name,
receptor_type=self.parameters.target_synapses)
def __init__(self, model, parameters):
SensoryInputComponent.__init__(self, model, parameters)
self.shape = (self.parameters.density,self.parameters.density)
self.sheets = {}
self._built = False
self.rf_types = ('X_ON', 'X_OFF')
sim = self.model.sim
self.on_pop = None
self.off_pop = None
self.pops = {}
self.scs = {}
self.ncs = {}
self.ncs_rng = {}
self.init_sheets(model)
for rf_type in self.rf_types:
self.scs[rf_type] = []
self.ncs[rf_type] = []
self.ncs_rng[rf_type] = []
seeds=mozaik.get_seeds((self.pops[rf_type].size,))
for i, lgn_cell in enumerate(self.pops[rf_type].all_cells):
scs = sim.StepCurrentSource(times=[0.0], amplitudes=[0.0])
if not self.parameters.mpi_reproducible_noise:
ncs = sim.NoisyCurrentSource(**self.parameters.noise[rf_type])
else:
ncs = sim.StepCurrentSource(times=[0.0], amplitudes=[0.0])
if self.pops[rf_type]._mask_local[i]:
self.ncs_rng[rf_type].append(numpy.random.RandomState(seed=seeds[i]))
self.scs[rf_type].append(scs)
self.ncs[rf_type].append(ncs)
lgn_cell.inject(scs)
lgn_cell.inject(ncs)
P_rf = self.parameters.receptive_field
rf_function = eval(P_rf.func)
rf_ON = SpatioTemporalReceptiveField(rf_function,
P_rf.func_params,
P_rf.width, P_rf.height,
P_rf.duration)
rf_OFF = SpatioTemporalReceptiveField(lambda x, y, t, p: -1.0 * rf_function(x, y, t, p),
P_rf.func_params,
P_rf.width, P_rf.height,
P_rf.duration)
dx = dy = P_rf.spatial_resolution
dt = P_rf.temporal_resolution
for rf in rf_ON, rf_OFF:
rf.quantize(dx, dy, dt)
self.rf = {'X_ON': rf_ON, 'X_OFF': rf_OFF}
def __init__(self, sheet, parameters):
DirectStimulator.__init__(self, sheet,parameters)
population_selector = load_component(self.parameters.population_selector.component)
self.ids = population_selector(sheet,self.parameters.population_selector.params).generate_idd_list_of_neurons()
d = dict((j,i) for i,j in enumerate(self.sheet.pop.all_cells))
self.local_and_to_stimulate_indexes = [d[i] for i in set(self.ids) & set(self.sheet.pop.local_cells)]
exc_syn = self.sheet.sim.StaticSynapse(weight=self.parameters.exc_weight)
if (self.parameters.exc_firing_rate != 0 or self.parameters.exc_weight != 0):
self.ssae = self.sheet.sim.Population(self.sheet.pop.size,self.sheet.sim.SpikeSourceArray())
seeds=mozaik.get_seeds((self.sheet.pop.size,))
self.stgene = [stgen.StGen(rng=numpy.random.RandomState(seed=seeds[i])) for i in self.local_and_to_stimulate_indexes]
self.sheet.sim.Projection(self.ssae, self.sheet.pop,self.sheet.sim.OneToOneConnector(),synapse_type=exc_syn,receptor_type='excitatory')
def __init__(self, sheet, parameters):
DirectStimulator.__init__(self, sheet, parameters)
population_selector = load_component(
self.parameters.population_selector.component)
self.ids = population_selector(
sheet, self.parameters.population_selector.params
).generate_idd_list_of_neurons()
# print(self.ids[0].id) # 1901
# d = dict((j, i) for i, j in enumerate(self.sheet.pop.all_cells)) # nest
d = dict((str(j), i)
for i, j in enumerate(self.sheet.pop.all_cells)) # spinnaker
# d = dict((i, j) for i, j in enumerate(numpy.asarray(self.sheet.pop.all_cells))) # spinnaker abbott
# d = dict((j, i) for i, j in enumerate(self.sheet.pop.all_cells)) # spinnaker abbott
# print("self.ids ", self.ids)
# print("d ", d)
# self.to_stimulate_indexes = [d[i.id] for i in self.ids]
self.to_stimulate_indexes = [d[str(i)] for i in self.ids
] # str because there is an object
# workaround
# self.to_stimulate_indexes = [k.item() for k in self.ids] # spinnaker abbott
exc_syn = self.sheet.sim.StaticSynapse(
weight=self.parameters.exc_weight,
delay=self.sheet.model.parameters.min_delay)
if (self.parameters.exc_firing_rate != 0
or self.parameters.exc_weight != 0):
self.ssae = self.sheet.sim.Population(
self.sheet.pop.size, self.sheet.sim.SpikeSourceArray())
seeds = mozaik.get_seeds((self.sheet.pop.size, ))
self.stgene = [
StGen(rng=numpy.random.RandomState(seed=seeds[i]))
for i in self.to_stimulate_indexes
]
print("sheet Kick projection ", self.ssae)
print("sheet Kick projection ", self.sheet.pop)
self.p = self.sheet.sim.Projection(
self.ssae,
self.sheet.pop,
self.sheet.sim.OneToOneConnector(),
synapse_type=exc_syn,
receptor_type='excitatory')
def __init__(self, model, parameters):
SensoryInputComponent.__init__(self, model, parameters)
self.shape = (self.parameters.density, self.parameters.density)
self.sheets = OrderedDict()
self._built = False
self.rf_types = ('X_ON', 'X_OFF')
sim = self.model.sim
self.pops = OrderedDict()
self.scs = OrderedDict()
self.ncs = OrderedDict()
self.ncs_rng = OrderedDict()
self.internal_stimulus_cache = OrderedDict()
for rf_type in self.rf_types:
p = RetinalUniformSheet(
model,
ParameterSet({
'sx': self.parameters.size[0],
'sy': self.parameters.size[1],
'density': self.parameters.density,
'cell': self.parameters.cell,
'name': rf_type,
'artificial_stimulators': OrderedDict(),
'recorders': self.parameters.recorders,
'recording_interval': self.parameters.recording_interval,
'mpi_safe': False
}))
self.sheets[rf_type] = p
for rf_type in self.rf_types:
self.scs[rf_type] = []
self.ncs[rf_type] = []
self.ncs_rng[rf_type] = []
seeds = mozaik.get_seeds((self.sheets[rf_type].pop.size, ))
for i, lgn_cell in enumerate(self.sheets[rf_type].pop.all_cells):
scs = sim.StepCurrentSource(times=[0.0], amplitudes=[0.0])
if not self.parameters.mpi_reproducible_noise:
ncs = sim.NoisyCurrentSource(**self.parameters.noise)
else:
ncs = sim.StepCurrentSource(times=[0.0], amplitudes=[0.0])
if self.sheets[rf_type].pop._mask_local[i]:
self.ncs_rng[rf_type].append(
numpy.random.RandomState(seed=seeds[i]))
self.scs[rf_type].append(scs)
self.ncs[rf_type].append(ncs)
lgn_cell.inject(scs)
lgn_cell.inject(ncs)
P_rf = self.parameters.receptive_field
rf_function = eval(P_rf.func)
rf_ON = SpatioTemporalReceptiveField(rf_function, P_rf.func_params,
P_rf.width, P_rf.height,
P_rf.duration)
rf_OFF = SpatioTemporalReceptiveField(
lambda x, y, t, p: -1.0 * rf_function(x, y, t, p),
P_rf.func_params, P_rf.width, P_rf.height, P_rf.duration)
dx = dy = P_rf.spatial_resolution
dt = P_rf.temporal_resolution
for rf in rf_ON, rf_OFF:
rf.quantize(dx, dy, dt)
self.rf = {'X_ON': rf_ON, 'X_OFF': rf_OFF}
def _connect(self):
# Check if the delay function is incompatible with multiprocessing
cl = []
v = 0
# Generates a splitted and of cells indices to be passed to each subprocesses
seeds = mozaik.get_seeds(
len(numpy.nonzero(self.target.pop._mask_local)[0]))
splitted_seeds = numpy.array_split(seeds, int(self.model.num_threads))
splitted_cell_indices = numpy.array_split(
numpy.nonzero(self.target.pop._mask_local)[0],
int(self.model.num_threads))
import multiprocessing
# This code will be ran by each subprocess
def build_connector(indices, seeds, queue_cl, queue_v):
cli = []
vi = 0
for i in range(len(indices)):
samples = self.target.get_neuron_annotation(
indices[i], self.parameters.annotation_reference_name)
weights = self._obtain_weights(indices[i], seeds[i])
delays = self._obtain_delays(indices[i], seeds[i])
if self.parameters.num_samples == 0:
co = Counter(
sample_from_bin_distribution(weights, int(samples),
seeds[i]))
else:
assert self.parameters.num_samples > 2 * int(
samples), "%s: %d %d" % (
self.name,
self.parameters.num_samples,
2 * int(samples),
)
co = Counter(
sample_from_bin_distribution(
weights,
int(self.parameters.num_samples -
2 * int(samples)),
seeds[i],
))
vi = vi + numpy.sum(list(co.values()))
k = list(co.keys())
a = numpy.array([
k,
numpy.zeros(len(k)) + indices[i],
self.weight_scaler * numpy.multiply(
self.parameters.base_weight.copy(seeds[i]).next(
len(k)), list(co.values())),
numpy.array(delays)[k],
])
cli.append(a)
# Add the output to the two queues to communicate it with the main process
queue_cl.put(cli)
queue_v.put(vi)
# Generate a list of subprocesses and of queues
processes = []
list_queue_cl = []
list_queue_v = []
for i in range(len(splitted_cell_indices)):
# Each queue will be used by the subprocesses to communicate with the main process
list_queue_cl.append(multiprocessing.Queue())
list_queue_v.append(multiprocessing.Queue())
proc = multiprocessing.Process(
target=build_connector,
args=(
splitted_cell_indices[i],
splitted_seeds[i],
list_queue_cl[i],
list_queue_v[i],
),
)
proc.start()
processes.append(proc)
# Gather the output of each subprocesses
cl = []
v = 0
for i, p in enumerate(processes):
cli = list_queue_cl[i].get()
for clii in cli:
cl.append(clii)
v += list_queue_v[i].get()
p.join()
cl = numpy.hstack(cl).T
method = self.sim.FromListConnector(cl)
logger.warning(
"%s(%s): %g connections were created, %g per target neuron [%g]" %
(self.name, self.__class__.__name__, len(cl),
len(cl) / len(numpy.nonzero(self.target.pop._mask_local)[0]),
v / len(numpy.nonzero(self.target.pop._mask_local)[0])))
if len(cl) > 0:
self.proj = self.sim.Projection(
self.source.pop,
self.target.pop,
method,
synapse_type=self.init_synaptic_mechanisms(),
label=self.name,
receptor_type=self.parameters.target_synapses)
else:
logger.warning(
"%s(%s): empty projection - pyNN projection not created." %
(self.name, self.__class__.__name__))
