def testFixedProbability(self):
"""For all connections created with "fixedProbability"..."""
for srcP in [self.source5, self.source22]:
for tgtP in [self.target1, self.target6, self.target33]:
prj1 = neuron.Projection(srcP, tgtP, neuron.FixedProbabilityConnector(0.5), rng=random.NumpyRNG(12345))
prj2 = neuron.Projection(srcP, tgtP, neuron.FixedProbabilityConnector(0.5), rng=random.NativeRNG(12345))
for prj in prj1, prj2:
assert (0 < len(prj) < len(srcP)*len(tgtP)), 'len(prj) = %d, len(srcP)*len(tgtP) = %d' % (len(prj), len(srcP)*len(tgtP))
def testFixedNumberPost(self):
c1 = neuron.FixedNumberPostConnector(10)
c2 = neuron.FixedNumberPostConnector(3)
c3 = neuron.FixedNumberPostConnector(random.RandomDistribution('poisson',[5]))
for srcP in [self.source5, self.source22]:
for tgtP in [self.target6, self.target33]:
for c in c1, c2:
prj1 = neuron.Projection(srcP, tgtP, c)
self.assertEqual(len(prj1.connections), c.n*len(srcP))
prj2 = neuron.Projection(srcP, tgtP, c3) # just a test that no Exceptions are raised
self.assertRaises(Exception, neuron.FixedNumberPostConnector, None)
def build_network(sim, order=1000, epsilon=0.1, delay=1.5, J=0.1, theta=20.0,
tau=20.0, tau_syn=0.1, tau_refrac=2.0, v_reset=10.0,
R=1.5, g=5, eta=2, seed=None):
NE = 4 * order
NI = 1 * order
CE = int(epsilon * NE) # number of excitatory synapses per neuron
CI = int(epsilon * NI) # number of inhibitory synapses per neuron
CMem = tau/R
J_unit = psp_height(tau, R, tau_syn)
J_ex = J / J_unit
J_in = -g * J_ex
nu_th = theta / (J_ex * CE * R * tau_syn)
nu_ex = eta * nu_th
p_rate = 1000.0 * nu_ex * CE
assert seed is not None
rng = NumpyRNG(seed)
neuron_params = {
"nrn_tau": tau,
"nrn_v_threshold": theta,
"nrn_refractory_period": tau_refrac,
"nrn_v_reset": v_reset,
"nrn_R": R,
"syn_tau": tau_syn
}
celltype = Dynamics(name='iaf',
subnodes={'nrn': read("sources/BrunelIaF.xml")['BrunelIaF'],
'syn': read("sources/AlphaPSR.xml")['AlphaPSR']})
celltype.connect_ports('syn.i_synaptic', 'nrn.i_synaptic')
exc = sim.Population(NE, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**neuron_params))
inh = sim.Population(NI, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**neuron_params))
all = exc + inh
all.initialize(v=RandomDistribution('uniform', (0.0, theta), rng=rng))
stim = sim.Population(NE + NI, nineml_cell_type('Poisson', read("sources/Poisson.xml")['Poisson'], {})(rate=p_rate))
print("Connecting network")
exc_synapse = sim.StaticSynapse(weight=J_ex, delay=delay)
inh_synapse = sim.StaticSynapse(weight=J_in, delay=delay)
input_connections = sim.Projection(stim, all, sim.OneToOneConnector(), exc_synapse, receptor_type="syn")
exc_connections = sim.Projection(exc, all, sim.FixedNumberPreConnector(n=CE), exc_synapse, receptor_type="syn") # check is Pre not Post
inh_connections = sim.Projection(inh, all, sim.FixedNumberPreConnector(n=CI), inh_synapse, receptor_type="syn")
return stim, exc, inh
def testAllToAll(self):
"""For all connections created with "allToAll" it should be possible to
obtain the weight using the top-level HocObject"""
for srcP in [self.source5, self.source22, self.target33]:
for tgtP in [self.target6, self.target33]:
#print "gid_counter = ", neuron.simulator.state.gid_counter
if srcP == tgtP:
prj = neuron.Projection(srcP, tgtP, neuron.AllToAllConnector(allow_self_connections=False))
else:
prj = neuron.Projection(srcP, tgtP, neuron.AllToAllConnector())
prj.setWeights(1.234)
weights = []
for c in prj.connections:
weights.append(c.nc.weight[0])
assert weights == [1.234]*len(prj)
def testRandomizeWeights(self):
# The probability of having two consecutive weights vector that are equal should be 0
prj1 = neuron.Projection(self.source, self.target, neuron.AllToAllConnector())
prj2 = neuron.Projection(self.source, self.target, neuron.AllToAllConnector())
prj1.randomizeWeights(self.distrib_Numpy)
prj2.randomizeWeights(self.distrib_Native)
w1 = []; w2 = []; w3 = []; w4 = []
for c in prj1.connections:
w1.append(c.nc.weight[0])
w2.append(c.nc.weight[0])
prj1.randomizeWeights(self.distrib_Numpy)
prj2.randomizeWeights(self.distrib_Native)
for c in prj1.connections:
w3.append(c.nc.weight[0])
w4.append(c.nc.weight[0])
self.assertNotEqual(w1, w3) and self.assertNotEqual(w2, w4)
def testRandomizeDelays(self):
# The probability of having two consecutive delays vector that are equal should be 0
prj1 = neuron.Projection(self.source, self.target, neuron.AllToAllConnector())
prj2 = neuron.Projection(self.source, self.target, neuron.AllToAllConnector())
prj1.randomizeDelays(self.distrib_Numpy)
prj2.randomizeDelays(self.distrib_Native)
d1 = []; d2 = []; d3 = []; d4 = []
for c in prj1.connections:
d1.append(c.nc.weight[0])
d2.append(c.nc.weight[0])
prj1.randomizeWeights(self.distrib_Numpy)
prj2.randomizeWeights(self.distrib_Native)
for c in prj1.connections:
d3.append(c.nc.weight[0])
d4.append(c.nc.weight[0])
self.assertNotEqual(d1, d3) and self.assertNotEqual(d2, d4)
def testSetDelays(self):
prj1 = neuron.Projection(self.source, self.target, neuron.AllToAllConnector())
prj1.setDelays(2.345)
delays = []
for c in prj1.connections:
delays.append(c.nc.delay)
result = 2.345*numpy.ones(len(prj1.connections))
assert (delays == result.tolist())
def testSetWeights(self):
prj1 = neuron.Projection(self.source, self.target, neuron.AllToAllConnector())
prj1.setWeights(2.345)
weights = []
for c in prj1.connections:
weights.append(c.nc.weight[0])
result = 2.345*numpy.ones(len(prj1.connections))
assert (weights == result.tolist())
def model_network(param_dict):
"""
This model network consists of a spike source and a neuron (IF_curr_alpha).
The spike rate of the source and the weight can be specified in the
param_dict. Returns the number of spikes fired during 1000 ms of simulation.
Parameters:
param_dict - dictionary with keys
rate - the rate of the spike source (spikes/second)
weight - weight of the connection source -> neuron
Returns:
dictionary with keys:
source_rate - the rate of the spike source
weight - weight of the connection source -> neuron
neuron_rate - spike rate of the neuron
"""
#set up the network
import pyNN.neuron as sim
sim.setup(dt=0.01,
min_delay=1.,
max_delay=1.,
debug=False,
quit_on_end=False)
weight = param_dict['weight']
import NeuroTools.stgen as stgen
stgen = stgen.StGen()
spiketrain = stgen.poisson_generator(param_dict['rate'], t_stop=1000.)
source = sim.Population(1, sim.SpikeSourceArray,
{'spike_times': spiketrain.spike_times})
neuron = sim.Population(1, sim.IF_cond_alpha)
sim.Projection(source,
neuron,
method=sim.OneToOneConnector(weights=param_dict['weight'],
delays=1.))
#set recorder
neuron.record()
neuron.record_v()
#run the simulation
sim.run(1001.)
sim.end()
# count the number of spikes
spikes = neuron.getSpikes()
numspikes = len(spikes)
# return everything, including the input parameters
return {
'source_rate': param_dict['rate'],
'weight': param_dict['weight'],
'neuron_rate': numspikes
}
def testFromList(self):
c1 = neuron.FromListConnector([
([0,], [0,], 0.1, 0.1),
([3,], [0,], 0.2, 0.11),
([2,], [3,], 0.3, 0.12),
([4,], [2,], 0.4, 0.13),
([0,], [1,], 0.5, 0.14),
])
prj = neuron.Projection(self.source5, self.target6, c1)
self.assertEqual(len(prj.connections), 5)
def testSaveAndLoad(self):
prj1 = neuron.Projection(self.source33, self.target33, neuron.OneToOneConnector())
prj1.setDelays(1)
prj1.setWeights(1.234)
prj1.saveConnections("connections.tmp", gather=False)
if neuron.num_processes() > 1:
distributed = True
else:
distributed = False
prj2 = neuron.Projection(self.source33, self.target33, neuron.FromFileConnector("connections.tmp",
distributed=distributed))
w1 = []; w2 = []; d1 = []; d2 = []
# For a connections scheme saved and reloaded, we test if the connections, their weights and their delays
# are equal.
for c1,c2 in zip(prj1.connections, prj2.connections):
w1.append(c1.nc.weight[0])
w2.append(c2.nc.weight[0])
d1.append(c1.nc.delay)
d2.append(c2.nc.delay)
assert (w1 == w2), 'w1 = %s\nw2 = %s' % (w1, w2)
assert (d1 == d2), 'd1 = %s\nd2 = %s' % (d1, d2)
def testDistanceDependentProbability(self):
"""For all connections created with "distanceDependentProbability"..."""
# Test should be improved..."
for rngclass in (random.NumpyRNG, random.NativeRNG):
for expr in ('exp(-d)', 'd < 0.5'):
#rngclass = random.NumpyRNG
#expr = 'exp(-d)'
prj = neuron.Projection(self.source33, self.target33,
neuron.DistanceDependentProbabilityConnector(d_expression=expr),
rng=rngclass(12345))
assert (0 < len(prj) < len(self.source33)*len(self.target33))
self.assertRaises(ZeroDivisionError, neuron.DistanceDependentProbabilityConnector, d_expression="d/0.0")
return gen()
assert generate_spike_times(0).max() > simtime
spike_source = sim.Population(
n, sim.SpikeSourceArray(spike_times=generate_spike_times))
spike_source.record('spikes')
cells.record('spikes')
cells[0:2].record('m')
syn = sim.StaticSynapse(weight=w, delay=syn_delay)
input_conns = sim.Projection(spike_source,
cells,
sim.FixedProbabilityConnector(0.5),
syn,
receptor_type="default")
# === Run simulation ===========================================================
sim.run(simtime)
filename = normalized_filename("Results", "nrn_artificial_cell", "pkl",
"neuron", sim.num_processes())
cells.write_data(filename, annotations={'script_name': __file__})
print("Mean firing rate: ", cells.mean_spike_count() * 1000.0 / simtime, "Hz")
plot_figure = True
if plot_figure:
def t4():
print 'Loading Forth XML File (iaf-2coba-Model)'
print '----------------------------------------'
component = readers.XMLReader.read_component(Join(tenml_dir,
'iaf_2coba.10ml'),
component_name='iaf')
writers.XMLWriter.write(
component,
'/tmp/nineml_toxml4.xml',
)
model = readers.XMLReader.read_component(Join(tenml_dir, 'iaf_2coba.10ml'))
from nineml.abstraction_layer.flattening import flatten
from nineml.abstraction_layer.dynamics.utils.modifiers import (
DynamicsModifier)
flatcomponent = flatten(model, componentname='iaf_2coba')
DynamicsModifier.close_analog_port(component=flatcomponent,
port_name='iaf_iSyn',
value='0')
writers.XMLWriter.write(flatcomponent, '/tmp/nineml_out_iaf_2coba.9ml')
import pyNN.neuron as sim
from pyNN.utility import init_logging
init_logging(None, debug=True)
sim.setup(timestep=0.1, min_delay=0.1)
print 'Attempting to simulate From Model:'
print '----------------------------------'
celltype_cls = pyNNml.nineml_celltype_from_model(
name="iaf_2coba",
nineml_model=flatcomponent,
synapse_components=[
pyNNml.CoBaSyn(namespace='cobaExcit', weight_connector='q'),
pyNNml.CoBaSyn(namespace='cobaInhib', weight_connector='q'),
])
parameters = {
'iaf.cm': 1.0,
'iaf.gl': 50.0,
'iaf.taurefrac': 5.0,
'iaf.vrest': -65.0,
'iaf.vreset': -65.0,
'iaf.vthresh': -50.0,
'cobaExcit.tau': 2.0,
'cobaInhib.tau': 5.0,
'cobaExcit.vrev': 0.0,
'cobaInhib.vrev': -70.0,
}
parameters = ComponentFlattener.flatten_namespace_dict(parameters)
cells = sim.Population(1, celltype_cls, parameters)
cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99) # neuron not refractory at start
cells.initialize('regime', 1002) # temporary hack
input = sim.Population(2, sim.SpikeSourcePoisson, {'rate': 100})
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
conn = [
sim.Projection(input[0:1], cells, connector, target='cobaExcit'),
sim.Projection(input[1:2], cells, connector, target='cobaInhib')
]
cells._record('iaf_V')
cells._record('cobaExcit_g')
cells._record('cobaInhib_g')
cells._record('cobaExcit_I')
cells._record('cobaInhib_I')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V",
filter=[cells[0]])
cells.recorders['cobaExcit_g'].write("Results/nineml_neuron.g_exc",
filter=[cells[0]])
cells.recorders['cobaInhib_g'].write("Results/nineml_neuron.g_inh",
filter=[cells[0]])
cells.recorders['cobaExcit_I'].write("Results/nineml_neuron.g_exc",
filter=[cells[0]])
cells.recorders['cobaInhib_I'].write("Results/nineml_neuron.g_inh",
filter=[cells[0]])
t = cells.recorders['iaf_V'].get()[:, 1]
v = cells.recorders['iaf_V'].get()[:, 2]
gInh = cells.recorders['cobaInhib_g'].get()[:, 2]
gExc = cells.recorders['cobaExcit_g'].get()[:, 2]
IInh = cells.recorders['cobaInhib_I'].get()[:, 2]
IExc = cells.recorders['cobaExcit_I'].get()[:, 2]
import pylab
pylab.subplot(311)
pylab.ylabel('Voltage')
pylab.plot(t, v)
pylab.subplot(312)
pylab.ylabel('Conductance')
pylab.plot(t, gInh)
pylab.plot(t, gExc)
pylab.subplot(313)
pylab.ylabel('Current')
pylab.plot(t, IInh)
pylab.plot(t, IExc)
pylab.suptitle("From Tree-Model Pathway")
pylab.show()
sim.end()
}
p = sim.Population(1, sim.IF_curr_alpha(**cell_parameters))
p.initialize(v=0.0)
rate = 20
stim = sim.Population(
1,
nineml_cell_type('Poisson',
read("../sources/Poisson.xml")['Poisson'], {})(rate=rate))
stim.initialize(t_next=numpy.random.exponential(1000 / rate))
weight = 0.1
delay = 0.5
prj = sim.Projection(stim,
p,
sim.AllToAllConnector(),
sim.StaticSynapse(weight=weight, delay=delay),
receptor_type='excitatory')
stim.record('spikes')
p.record('v')
sim.run(t_stop)
nrn_data = p.get_data().segments[0]
stim_data = stim.get_data().segments[0]
print("Expected spike count: {}".format(t_stop * rate / 1000))
print("Actual spike count: {}".format(stim.mean_spike_count()))
Figure(
def test_create_with_fast_synapse_dynamics(self):
prj = sim.Projection(self.p1, self.p2, self.all2all,
synapse_type=sim.TsodyksMarkramSynapse())
def test_create_simple(self):
prj = sim.Projection(self.p1, self.p2, self.all2all, self.syn2)
#seeds = numpy.arange(numberOfNodes) + int((time.time()*100)%2**32)
# seeds which are same every time, different for each node
seeds = numpy.arange(numberOfNodes)
# bcast, as we can't be sure each node has the same time, and therefore
# different seeds. This way, all nodes get the list from rank=0.
seeds = MPI.COMM_WORLD.bcast(seeds)
#rng = NumpyRNG(seed=seeds[rank], parallel_safe=False, rank=rank,
# num_processes=numberOfNodes)
#nest.SetKernelStatus({'rng_seeds': list(seeds)})
myconn = sim.OneToOneConnector(weights=globalWeight, delays=dt)
prjE_E = sim.Projection(poissonE_E, popE, method=myconn, target='excitatory')
prjE_I = sim.Projection(poissonE_I, popI, method=myconn, target='excitatory')
prjI_E = sim.Projection(poissonI_E, popE, method=myconn, target='inhibitory')
prjI_I = sim.Projection(poissonI_I, popI, method=myconn, target='inhibitory')
## Record the spikes ##
popE.record(to_file=False)
popI.record(to_file=False)
printTimer("Time for setup part")
###################### RUN PART ###########################
## Run the simulation without inter-connection ##
printMessage("Now running without inter-lattice connections.")
sim.run(int(tinit))
def run(plot_and_show=True):
import sys
from os.path import abspath, realpath, join
import numpy
import nineml
root = abspath(join(realpath(nineml.__path__[0]), "../../.."))
sys.path.append(join(root, "lib9ml/python/examples/AL"))
sys.path.append(join(root, "code_generation/nmodl"))
sys.path.append(join(root, "code_generation/nest2"))
#from nineml.abstraction_layer.example_models import get_hierachical_iaf_3coba
from nineml.abstraction_layer.testing_utils import TestableComponent
from nineml.abstraction_layer.flattening import ComponentFlattener
import pyNN.neuron as sim
import pyNN.neuron.nineml as pyNNml
from pyNN.utility import init_logging
init_logging(None, debug=True)
sim.setup(timestep=0.1, min_delay=0.1)
#test_component = get_hierachical_iaf_3coba()
test_component = TestableComponent('hierachical_iaf_3coba')()
from nineml.abstraction_layer.writers import DotWriter
DotWriter.write(test_component, 'test1.dot')
from nineml.abstraction_layer.writers import XMLWriter
XMLWriter.write(test_component, 'iaf_3coba.xml')
celltype_cls = pyNNml.nineml_celltype_from_model(
name="iaf_3coba",
nineml_model=test_component,
synapse_components=[
pyNNml.CoBaSyn(namespace='AMPA', weight_connector='q'),
pyNNml.CoBaSyn(namespace='GABAa', weight_connector='q'),
pyNNml.CoBaSyn(namespace='GABAb', weight_connector='q'),
])
parameters = {
'iaf.cm': 1.0,
'iaf.gl': 50.0,
'iaf.taurefrac': 5.0,
'iaf.vrest': -65.0,
'iaf.vreset': -65.0,
'iaf.vthresh': -50.0,
'AMPA.tau': 2.0,
'GABAa.tau': 5.0,
'GABAb.tau': 50.0,
'AMPA.vrev': 0.0,
'GABAa.vrev': -70.0,
'GABAb.vrev': -95.0,
}
parameters = ComponentFlattener.flatten_namespace_dict(parameters)
cells = sim.Population(1, celltype_cls, parameters)
cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99) # neuron not refractory at start
cells.initialize('regime', 1002) # temporary hack
input = sim.Population(3, sim.SpikeSourceArray)
numpy.random.seed(12345)
input[0].spike_times = numpy.add.accumulate(
numpy.random.exponential(1000.0 / 100.0, size=1000))
input[1].spike_times = numpy.add.accumulate(
numpy.random.exponential(1000.0 / 20.0, size=1000))
input[2].spike_times = numpy.add.accumulate(
numpy.random.exponential(1000.0 / 50.0, size=1000))
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
conn = [
sim.Projection(input[0:1], cells, connector, target='AMPA'),
sim.Projection(input[1:2], cells, connector, target='GABAa'),
sim.Projection(input[2:3], cells, connector, target='GABAb')
]
cells._record('iaf_V')
cells._record('AMPA_g')
cells._record('GABAa_g')
cells._record('GABAb_g')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V",
filter=[cells[0]])
cells.recorders['AMPA_g'].write("Results/nineml_neuron.g_exc",
filter=[cells[0]])
cells.recorders['GABAa_g'].write("Results/nineml_neuron.g_gabaA",
filter=[cells[0]])
cells.recorders['GABAb_g'].write("Results/nineml_neuron.g_gagaB",
filter=[cells[0]])
t = cells.recorders['iaf_V'].get()[:, 1]
v = cells.recorders['iaf_V'].get()[:, 2]
gInhA = cells.recorders['GABAa_g'].get()[:, 2]
gInhB = cells.recorders['GABAb_g'].get()[:, 2]
gExc = cells.recorders['AMPA_g'].get()[:, 2]
if plot_and_show:
import pylab
pylab.subplot(211)
pylab.plot(t, v)
pylab.ylabel('voltage [mV]')
pylab.suptitle("AMPA, GABA_A, GABA_B")
pylab.subplot(212)
pylab.plot(t, gInhA, label='GABA_A')
pylab.plot(t, gInhB, label='GABA_B')
pylab.plot(t, gExc, label='AMPA')
pylab.ylabel('conductance [nS]')
pylab.xlabel('t [ms]')
pylab.legend()
pylab.show()
sim.end()
'GABAb.vrev': -95.0,
}
parameters = ModelToSingleComponentReducer.flatten_namespace_dict(parameters)
cells = sim.Population(1, celltype_cls, parameters)
cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99) # neuron not refractory at start
cells.initialize('regime', 1002) # temporary hack
input = sim.Population(3, sim.SpikeSourcePoisson, {'rate': 100})
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
conn = [
sim.Projection(input[0:1], cells, connector, target='AMPA'),
sim.Projection(input[1:2], cells, connector, target='GABAa'),
sim.Projection(input[2:3], cells, connector, target='GABAb')
]
cells._record('iaf_V')
cells._record('AMPA_g')
cells._record('GABAa_g')
cells._record('GABAb_g')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
cells.recorders['AMPA_g'].write("Results/nineml_neuron.g_exc",
filter=[cells[0]])
parameters = ComponentFlattener.flatten_namespace_dict(parameters)
cells = sim.Population(1, celltype_cls, parameters)
cells.initialize('iaf_V', parameters['iaf_vrest'])
cells.initialize('tspike', -1e99) # neuron not refractory at start
cells.initialize('regime', 1002) # temporary hack
input = sim.Population(1, sim.SpikeSourcePoisson, {'rate': 100})
connector = sim.OneToOneConnector(weights=1.0, delays=0.5)
conn = [
sim.Projection(input[0:1], cells, connector, target='nmda'),
sim.Projection(input[0:1], cells, connector, target='cobaExcit'),
]
cells._record('iaf_V')
cells._record('nmda_g')
cells._record('cobaExcit_g')
cells.record()
sim.run(100.0)
cells.recorders['iaf_V'].write("Results/nineml_neuron.V", filter=[cells[0]])
cells.recorders['nmda_g'].write("Results/nineml_neuron.g_nmda", filter=[cells[0]])
cells.recorders['cobaExcit_g'].write("Results/nineml_neuron.g_cobaExcit", filter=[cells[0]])
def sim_runner(wgf):
wg = wgf
import pyNN.neuron as sim
nproc = sim.num_processes()
node = sim.rank()
print(nproc)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams.update({'font.size':16})
#import mpi4py
#threads = sim.rank()
threads = 1
rngseed = 98765
parallel_safe = False
#extra = {'threads' : threads}
import os
import pandas as pd
import sys
import numpy as np
from pyNN.neuron import STDPMechanism
import copy
from pyNN.random import RandomDistribution, NumpyRNG
import pyNN.neuron as neuron
from pyNN.neuron import h
from pyNN.neuron import StandardCellType, ParameterSpace
from pyNN.random import RandomDistribution, NumpyRNG
from pyNN.neuron import STDPMechanism, SpikePairRule, AdditiveWeightDependence, FromListConnector, TsodyksMarkramSynapse
from pyNN.neuron import Projection, OneToOneConnector
from numpy import arange
import pyNN
from pyNN.utility import get_simulator, init_logging, normalized_filename
import random
import socket
#from neuronunit.optimization import get_neab
import networkx as nx
sim = pyNN.neuron
# Get some hippocampus connectivity data, based on a conversation with
# academic researchers on GH:
# https://github.com/Hippocampome-Org/GraphTheory/issues?q=is%3Aissue+is%3Aclosed
# scrape hippocamome connectivity data, that I intend to use to program neuromorphic hardware.
# conditionally get files if they don't exist.
path_xl = '_hybrid_connectivity_matrix_20171103_092033.xlsx'
if not os.path.exists(path_xl):
os.system('wget https://github.com/Hippocampome-Org/GraphTheory/files/1657258/_hybrid_connectivity_matrix_20171103_092033.xlsx')
xl = pd.ExcelFile(path_xl)
dfEE = xl.parse()
dfEE.loc[0].keys()
dfm = dfEE.as_matrix()
rcls = dfm[:,:1] # real cell labels.
rcls = rcls[1:]
rcls = { k:v for k,v in enumerate(rcls) } # real cell labels, cast to dictionary
import pickle
with open('cell_names.p','wb') as f:
pickle.dump(rcls,f)
import pandas as pd
pd.DataFrame(rcls).to_csv('cell_names.csv', index=False)
filtered = dfm[:,3:]
filtered = filtered[1:]
rng = NumpyRNG(seed=64754)
delay_distr = RandomDistribution('normal', [2, 1e-1], rng=rng)
weight_distr = RandomDistribution('normal', [45, 1e-1], rng=rng)
sanity_e = []
sanity_i = []
EElist = []
IIlist = []
EIlist = []
IElist = []
for i,j in enumerate(filtered):
for k,xaxis in enumerate(j):
if xaxis == 1 or xaxis == 2:
source = i
sanity_e.append(i)
target = k
if xaxis ==-1 or xaxis == -2:
sanity_i.append(i)
source = i
target = k
index_exc = list(set(sanity_e))
index_inh = list(set(sanity_i))
import pickle
with open('cell_indexs.p','wb') as f:
returned_list = [index_exc, index_inh]
pickle.dump(returned_list,f)
import numpy
a = numpy.asarray(index_exc)
numpy.savetxt('pickles/'+str(k)+'excitatory_nunber_labels.csv', a, delimiter=",")
import numpy
a = numpy.asarray(index_inh)
numpy.savetxt('pickles/'+str(k)+'inhibitory_nunber_labels.csv', a, delimiter=",")
for i,j in enumerate(filtered):
for k,xaxis in enumerate(j):
if xaxis==1 or xaxis == 2:
source = i
sanity_e.append(i)
target = k
delay = delay_distr.next()
weight = 1.0
if target in index_inh:
EIlist.append((source,target,delay,weight))
else:
EElist.append((source,target,delay,weight))
if xaxis==-1 or xaxis == -2:
sanity_i.append(i)
source = i
target = k
delay = delay_distr.next()
weight = 1.0
if target in index_exc:
IElist.append((source,target,delay,weight))
else:
IIlist.append((source,target,delay,weight))
internal_conn_ee = sim.FromListConnector(EElist)
ee = internal_conn_ee.conn_list
ee_srcs = ee[:,0]
ee_tgs = ee[:,1]
internal_conn_ie = sim.FromListConnector(IElist)
ie = internal_conn_ie.conn_list
ie_srcs = set([ int(e[0]) for e in ie ])
ie_tgs = set([ int(e[1]) for e in ie ])
internal_conn_ei = sim.FromListConnector(EIlist)
ei = internal_conn_ei.conn_list
ei_srcs = set([ int(e[0]) for e in ei ])
ei_tgs = set([ int(e[1]) for e in ei ])
internal_conn_ii = sim.FromListConnector(IIlist)
ii = internal_conn_ii.conn_list
ii_srcs = set([ int(e[0]) for e in ii ])
ii_tgs = set([ int(e[1]) for e in ii ])
for e in internal_conn_ee.conn_list:
assert e[0] in ee_srcs
assert e[1] in ee_tgs
for i in internal_conn_ii.conn_list:
assert i[0] in ii_srcs
assert i[1] in ii_tgs
ml = len(filtered[1])+1
pre_exc = []
post_exc = []
pre_inh = []
post_inh = []
rng = NumpyRNG(seed=64754)
delay_distr = RandomDistribution('normal', [2, 1e-1], rng=rng)
plot_EE = np.zeros(shape=(ml,ml), dtype=bool)
plot_II = np.zeros(shape=(ml,ml), dtype=bool)
plot_EI = np.zeros(shape=(ml,ml), dtype=bool)
plot_IE = np.zeros(shape=(ml,ml), dtype=bool)
for i in EElist:
plot_EE[i[0],i[1]] = int(0)
#plot_ss[i[0],i[1]] = int(1)
if i[0]!=i[1]: # exclude self connections
plot_EE[i[0],i[1]] = int(1)
pre_exc.append(i[0])
post_exc.append(i[1])
assert len(pre_exc) == len(post_exc)
for i in IIlist:
plot_II[i[0],i[1]] = int(0)
if i[0]!=i[1]:
plot_II[i[0],i[1]] = int(1)
pre_inh.append(i[0])
post_inh.append(i[1])
for i in IElist:
plot_IE[i[0],i[1]] = int(0)
if i[0]!=i[1]: # exclude self connections
plot_IE[i[0],i[1]] = int(1)
pre_inh.append(i[0])
post_inh.append(i[1])
for i in EIlist:
plot_EI[i[0],i[1]] = int(0)
if i[0]!=i[1]:
plot_EI[i[0],i[1]] = int(1)
pre_exc.append(i[0])
post_exc.append(i[1])
plot_excit = plot_EI + plot_EE
plot_inhib = plot_IE + plot_II
assert len(pre_inh) == len(post_inh)
num_exc = [ i for i,e in enumerate(plot_excit) if sum(e) > 0 ]
num_inh = [ y for y,i in enumerate(plot_inhib) if sum(i) > 0 ]
# the network is dominated by inhibitory neurons, which is unusual for modellers.
assert num_inh > num_exc
assert np.sum(plot_inhib) > np.sum(plot_excit)
assert len(num_exc) < ml
assert len(num_inh) < ml
# # Plot all the Projection pairs as a connection matrix (Excitatory and Inhibitory Connections)
import pickle
with open('graph_inhib.p','wb') as f:
pickle.dump(plot_inhib,f, protocol=2)
import pickle
with open('graph_excit.p','wb') as f:
pickle.dump(plot_excit,f, protocol=2)
#with open('cell_names.p','wb') as f:
# pickle.dump(rcls,f)
import pandas as pd
pd.DataFrame(plot_EE).to_csv('ee.csv', index=False)
import pandas as pd
pd.DataFrame(plot_IE).to_csv('ie.csv', index=False)
import pandas as pd
pd.DataFrame(plot_II).to_csv('ii.csv', index=False)
import pandas as pd
pd.DataFrame(plot_EI).to_csv('ei.csv', index=False)
from scipy.sparse import coo_matrix
m = np.matrix(filtered[1:])
bool_matrix = np.add(plot_excit,plot_inhib)
with open('bool_matrix.p','wb') as f:
pickle.dump(bool_matrix,f, protocol=2)
if not isinstance(m, coo_matrix):
m = coo_matrix(m)
Gexc_ud = nx.Graph(plot_excit)
avg_clustering = nx.average_clustering(Gexc_ud)#, nodes=None, weight=None, count_zeros=True)[source]
rc = nx.rich_club_coefficient(Gexc_ud,normalized=False)
print('This graph structure as rich as: ',rc[0])
gexc = nx.DiGraph(plot_excit)
gexcc = nx.betweenness_centrality(gexc)
top_exc = sorted(([ (v,k) for k, v in dict(gexcc).items() ]), reverse=True)
in_degree = gexc.in_degree()
top_in = sorted(([ (v,k) for k, v in in_degree.items() ]))
in_hub = top_in[-1][1]
out_degree = gexc.out_degree()
top_out = sorted(([ (v,k) for k, v in out_degree.items() ]))
out_hub = top_out[-1][1]
mean_out = np.mean(list(out_degree.values()))
mean_in = np.mean(list(in_degree.values()))
mean_conns = int(mean_in + mean_out/2)
k = 2 # number of neighbouig nodes to wire.
p = 0.25 # probability of instead wiring to a random long range destination.
ne = len(plot_excit)# size of small world network
small_world_ring_excit = nx.watts_strogatz_graph(ne,mean_conns,0.25)
k = 2 # number of neighbouring nodes to wire.
p = 0.25 # probability of instead wiring to a random long range destination.
ni = len(plot_inhib)# size of small world network
small_world_ring_inhib = nx.watts_strogatz_graph(ni,mean_conns,0.25)
nproc = sim.num_processes()
nproc = 8
host_name = socket.gethostname()
node_id = sim.setup(timestep=0.01, min_delay=1.0)#, **extra)
print("Host #%d is on %s" % (node_id + 1, host_name))
rng = NumpyRNG(seed=64754)
#pop_size = len(num_exc)+len(num_inh)
#num_exc = [ i for i,e in enumerate(plot_excit) if sum(e) > 0 ]
#num_inh = [ y for y,i in enumerate(plot_inhib) if sum(i) > 0 ]
#pop_exc = sim.Population(len(num_exc), sim.Izhikevich(a=0.02, b=0.2, c=-65, d=8, i_offset=0))
#pop_inh = sim.Population(len(num_inh), sim.Izhikevich(a=0.02, b=0.25, c=-65, d=2, i_offset=0))
#index_exc = list(set(sanity_e))
#index_inh = list(set(sanity_i))
all_cells = sim.Population(len(index_exc)+len(index_inh), sim.Izhikevich(a=0.02, b=0.2, c=-65, d=8, i_offset=0))
#all_cells = None
#all_cells = pop_exc + pop_inh
pop_exc = sim.PopulationView(all_cells,index_exc)
pop_inh = sim.PopulationView(all_cells,index_inh)
#print(pop_exc)
#print(dir(pop_exc))
for pe in pop_exc:
print(pe)
#import pdb
pe = all_cells[pe]
#pdb.set_trace()
#pe = all_cells[i]
r = random.uniform(0.0, 1.0)
pe.set_parameters(a=0.02, b=0.2, c=-65+15*r, d=8-r**2, i_offset=0)
#pop_exc.append(pe)
#pop_exc = sim.Population(pop_exc)
for pi in index_inh:
pi = all_cells[pi]
#print(pi)
#pi = all_cells[i]
r = random.uniform(0.0, 1.0)
pi.set_parameters(a=0.02+0.08*r, b=0.25-0.05*r, c=-65, d= 2, i_offset=0)
#pop_inh.append(pi)
#pop_inh = sim.Population(pop_inh)
'''
for pe in pop_exc:
r = random.uniform(0.0, 1.0)
pe.set_parameters(a=0.02, b=0.2, c=-65+15*r, d=8-r**2, i_offset=0)
for pi in pop_inh:
r = random.uniform(0.0, 1.0)
pi.set_parameters(a=0.02+0.08*r, b=0.25-0.05*r, c=-65, d= 2, i_offset=0)
'''
NEXC = len(num_exc)
NINH = len(num_inh)
exc_syn = sim.StaticSynapse(weight = wg, delay=delay_distr)
assert np.any(internal_conn_ee.conn_list[:,0]) < ee_srcs.size
prj_exc_exc = sim.Projection(all_cells, all_cells, internal_conn_ee, exc_syn, receptor_type='excitatory')
prj_exc_inh = sim.Projection(all_cells, all_cells, internal_conn_ei, exc_syn, receptor_type='excitatory')
inh_syn = sim.StaticSynapse(weight = wg, delay=delay_distr)
delay_distr = RandomDistribution('normal', [1, 100e-3], rng=rng)
prj_inh_inh = sim.Projection(all_cells, all_cells, internal_conn_ii, inh_syn, receptor_type='inhibitory')
prj_inh_exc = sim.Projection(all_cells, all_cells, internal_conn_ie, inh_syn, receptor_type='inhibitory')
inh_distr = RandomDistribution('normal', [1, 2.1e-3], rng=rng)
def prj_change(prj,wg):
prj.setWeights(wg)
prj_change(prj_exc_exc,wg)
prj_change(prj_exc_inh,wg)
prj_change(prj_inh_exc,wg)
prj_change(prj_inh_inh,wg)
def prj_check(prj):
for w in prj.weightHistogram():
for i in w:
print(i)
prj_check(prj_exc_exc)
prj_check(prj_exc_inh)
prj_check(prj_inh_exc)
prj_check(prj_inh_inh)
#print(rheobase['value'])
#print(float(rheobase['value']),1.25/1000.0)
'''Old values that worked
noise = sim.NoisyCurrentSource(mean=0.85/1000.0, stdev=5.00/1000.0, start=0.0, stop=2000.0, dt=1.0)
pop_exc.inject(noise)
#1000.0 pA
noise = sim.NoisyCurrentSource(mean=1.740/1000.0, stdev=5.00/1000.0, start=0.0, stop=2000.0, dt=1.0)
pop_inh.inject(noise)
#1750.0 pA
'''
noise = sim.NoisyCurrentSource(mean=0.74/1000.0, stdev=4.00/1000.0, start=0.0, stop=2000.0, dt=1.0)
pop_exc.inject(noise)
#1000.0 pA
noise = sim.NoisyCurrentSource(mean=1.440/1000.0, stdev=4.00/1000.0, start=0.0, stop=2000.0, dt=1.0)
pop_inh.inject(noise)
##
# Setup and run a simulation. Note there is no current injection into the neuron.
# All cells in the network are in a quiescent state, so its not a surprise that xthere are no spikes
##
sim = pyNN.neuron
arange = np.arange
import re
all_cells.record(['v','spikes']) # , 'u'])
all_cells.initialize(v=-65.0, u=-14.0)
# === Run the simulation =====================================================
tstop = 2000.0
sim.run(tstop)
data = None
data = all_cells.get_data().segments[0]
#print(len(data.analogsignals[0].times))
with open('pickles/qi'+str(wg)+'.p', 'wb') as f:
pickle.dump(data,f)
# make data none or else it will grow in a loop
all_cells = None
data = None
noise = None
def testOneToOne(self):
"""For all connections created with "OneToOne" ..."""
prj = neuron.Projection(self.source33, self.target33, neuron.OneToOneConnector())
assert len(prj.connections) == len(self.target33.local_cells), prj.connections
"e_e": 0.0,
"e_i": -75.0,
}
sim.setup()
p0 = sim.Population(1, sim.SpikeSourcePoisson, {'rate': 100.0})
p1 = sim.Population(10, TestCell, cell_params)
p2 = sim.Population(10, sim.IF_cond_exp)
p1.record_v(1)
p1.record()
p2.record_v(1)
#curr = sim.DCSource()
#curr.inject_into(p1)
prj01 = sim.Projection(p0, p1, sim.AllToAllConnector())
prj12 = sim.Projection(p1, p2, sim.FixedProbabilityConnector(0.5))
prj01.setWeights(0.1)
prj12.setWeights(0.1)
sim.run(1000.0)
t,v = p1.get_v()[:, 1:3].T
#print p2.get_v()
import pylab
pylab.rcParams['interactive'] = True
pylab.plot(t, v)
stdp = sim.STDPMechanism(weight_dependence=sim.AdditiveWeightDependence(w_min=0.0, w_max=0.1),
timing_dependence=sim.Vogels2011Rule(eta=0.0, rho=1e-3),
weight=0.005, delay=0.5)
fpc = sim.FixedProbabilityConnector(0.02, rng=NumpyRNG(seed=854))
connections = sim.Projection(input, output, fpc,
synapse_type=stdp,
receptor_type='excitatory')
connections.set(eta=0.0003)
output.record(['spikes', 'v'])
sim.run(simTimeFin - simTimeIni)
print("\n\nETA: input to output:")
print connections.get('eta', format='list')
def testSettingDelays(self):
"""Delays should be set correctly when using a Connector object."""
for srcP in [self.source5, self.source22]:
for tgtP in [self.target6, self.target33]:
prj1 = neuron.Projection(srcP, tgtP, neuron.AllToAllConnector(delays=0.321))
assert prj1.connections[0].nc.delay == 0.321, "Delay should be 0.321, actually %g" % prj1.connections[0].nc.delay
import pyNN.neuron as sim # can of course replace `neuron` with `nest`, `brian`, etc.
import matplotlib.pyplot as plt
import numpy as np
sim.setup(timestep=0.01)
p_in = sim.Population(10, sim.SpikeSourcePoisson(rate=10.0), label="input")
p_out = sim.Population(10, sim.EIF_cond_exp_isfa_ista(), label="AdExp neurons")
syn = sim.StaticSynapse(weight=0.05)
random = sim.FixedProbabilityConnector(p_connect=0.5)
connections = sim.Projection(p_in, p_out, random, syn, receptor_type='excitatory')
p_in.record('spikes')
p_out.record('spikes') # record spikes from all neurons
p_out[0:2].record(['v', 'w', 'gsyn_exc']) # record other variables from first two neurons
sim.run(500.0)
spikes_in = p_in.get_data()
data_out = p_out.get_data()
fig_settings = {
'lines.linewidth': 0.5,
'axes.linewidth': 0.5,
'axes.labelsize': 'small',
'legend.fontsize': 'small',
'font.size': 8
}
plt.rcParams.update(fig_settings)
plt.figure(1, figsize=(6, 8))
# PyNN/NineML simulation
sim.setup(timestep=dt)
celltype = Dynamics(name='iaf',
subnodes={'nrn': read("../sources/BrunelIaF.xml")['BrunelIaF'],
'syn': read("../sources/AlphaPSR.xml")['AlphaPSR']})
celltype.connect_ports('syn.i_synaptic', 'nrn.i_synaptic')
p = sim.Population(2, nineml_cell_type('BrunelIaF', celltype, {'syn': 'syn_weight'})(**cell_parameters))
stim = sim.Population(1, sim.SpikeSourceArray(spike_times=spike_times))
prj = sim.Projection(stim, p,
sim.AllToAllConnector(),
sim.StaticSynapse(weight=w_eff, delay=delay),
receptor_type='syn')
p.record(['nrn_v', 'syn_a', 'syn_b'])
sim.run(t_stop)
nrn_data = p.get_data().segments[0]
expected = np.zeros((1 + int(round(t_stop/dt)),))
tau_syn = cell_parameters["syn_tau"]
tp = np.arange(0, t_stop - spike_times[0] - delay, dt)/tau_syn
expected[1 + int(round((spike_times[0] + delay)/dt)):] = w_eff * tp * np.exp(-tp)
synaptic_current = nrn_data.filter(name='syn_a')[0]
# for convenience of plotting, we overwrite the synaptic current recorded from the second neuron
ii_tgs = all_cells[list(ii_tgs)]
ee_srcs = all_cells[list(ee_srcs)]
ee_tgs = all_cells[list(ee_tgs)]
ei_srcs = all_cells[list(ei_srcs)]
ei_tgs = all_cells[list(ei_tgs)]
ie_srcs = all_cells[list(ie_srcs)]
ie_tgs = all_cells[list(ie_tgs)]
exc_distr = RandomDistribution('normal', [3.125, 10e-2], rng=rng)
exc_syn = sim.StaticSynapse(weight=exc_distr, delay=delay_distr)
#if numpy.any(self.conn_list[:, 0] >= projection.pre.size):
assert np.any(internal_conn_ee.conn_list[:, 0]) < ee_srcs.size
prj_exc_exc = sim.Projection(all_cells,
all_cells,
internal_conn_ee,
exc_syn,
receptor_type='excitatory')
inh_distr = RandomDistribution('normal', [5, 2.1e-4], rng=rng)
inh_syn = sim.StaticSynapse(weight=inh_distr, delay=delay_distr)
iis = all_cells[[e[0] for e in IIlist]]
iit = all_cells[[e[1] for e in IIlist]]
rng = NumpyRNG(seed=64754)
delay_distr = RandomDistribution('normal', [50, 100e-3], rng=rng)
prj_inh_inh = sim.Projection(all_cells,
all_cells,
internal_conn_ii,
inh_syn,
def std_pynn_simulation(test_component, parameters, initial_values,
synapse_components, records, plot=True, sim_time=100.,
synapse_weights=1.0, syn_input_rate=100):
from nineml.abstraction_layer.flattening import ComponentFlattener
import pyNN.neuron as sim
import pyNN.neuron.nineml as pyNNml
from pyNN.neuron.nineml import CoBaSyn
from pyNN.utility import init_logging
init_logging(None, debug=True)
sim.setup(timestep=0.01, min_delay=0.1)
synapse_components_ML = [CoBaSyn(namespace=ns, weight_connector=wc)
for (ns, wc) in synapse_components]
celltype_cls = pyNNml.nineml_celltype_from_model(
name=test_component.name,
nineml_model=test_component,
synapse_components=synapse_components_ML,
)
parameters = ComponentFlattener.flatten_namespace_dict(parameters)
initial_values = ComponentFlattener.flatten_namespace_dict(initial_values)
cells = sim.Population(1, celltype_cls, parameters)
# Set Initial Values:
for state, state_initial_value in initial_values.iteritems():
cells.initialize(state, state_initial_value)
# For each synapse type, create a spike source:
if synapse_components:
input = sim.Population(
len(synapse_components), sim.SpikeSourcePoisson,
{'rate': syn_input_rate})
connector = sim.OneToOneConnector(weights=synapse_weights, delays=0.5)
conn = []
for i, (ns, weight_connector) in enumerate(synapse_components):
proj = sim.Projection(input[i:i + 1], cells, connector, target=ns),
conn.append(proj)
# Setup the Records:
for record in records:
cells.record(record.what)
cells.record('spikes')
# Run the simulation:
sim.run(sim_time)
if len(records) == 0:
assert False
# Write the Results to a file:
cells.write_data("Results/nineml.pkl")
# Plot the values:
results = cells.get_data().segments[0]
# Create a list of the tags:
tags = []
for record in records:
if not record.tag in tags:
tags.append(record.tag)
# Plot the graphs:
if plot:
import pylab
nGraphs = len(tags)
# Plot the Records:
for graphIndex, tag in enumerate(tags):
pylab.subplot(nGraphs, 1, graphIndex + 1)
for r in records:
if r.tag != tag:
continue
trace = results.filter(name=r.what)[0]
pylab.plot(trace.times, trace, label=r.label)
pylab.ylabel(tag)
pylab.legend()
# Plot the spikes:
# pylab.subplot(nGraphs,1, len(tags)+1)
# t_spikes = cells[0:1].getSpikes()[:1]
# pylab.plot( [1,3],[1,3],'x' )
# print t_spikes
# if t_spikes:
# pylab.scatter( t_spikes, t_spikes )
# Add the X axis to the last plot:
pylab.xlabel('t [ms]')
# pylab.suptitle("From Tree-Model Pathway")
pylab.show()
sim.end()
return results
