def start_simulation(self):
nest.Simulate(self.parameters['Time before stimulation'])
nest.SetStatus(self.stim2_i, {'rate': self.parameters['Stimulus rate']})
nest.Simulate(self.parameters['Time of stimulation'])
nest.SetStatus(self.stim2_i, {'rate': 0.0})
nest.Simulate(self.parameters['Time after Stimulation'])
rec_ex_true = nest.GetLeaves(self.rec_ex, local_only=True)[0]
rec_in_true = nest.GetLeaves(self.rec_in, local_only=True)[0]
self.events_ex = nest.GetStatus(rec_ex_true, "events")[0]
self.events_in = nest.GetStatus(rec_in_true, "events")[0]
# self.events_ex = nest.GetStatus(self.rec_ex, "events")[0]
# self.events_in = nest.GetStatus(self.rec_in, "events")[0]
self.df_ex = makePandas(self.events_ex, tp.FindCenterElement(self.l)[0])
self.df_in = makePandas(self.events_in, tp.FindCenterElement(self.l)[0])
def test_GetCenterElement(self):
"""Interface and result check for finding center element.
This function is Py only, so we also need to check results."""
# nodes at [-1,0,1]x[-1,0,1], column-wise
ldict = {'elements': 'iaf_psc_alpha', 'rows': 3, 'columns': 3,
'extent': (2., 2.)}
nest.ResetKernel()
l = topo.CreateLayer(ldict)
# single layer
n = topo.FindCenterElement(l)
self.assertEqual(n, (6,))
# two layers
n = topo.FindCenterElement(l * 2)
self.assertEqual(n, (6,) * 2)
def _build(self):
'''Create populations.'''
ldict_s = {
'elements': 'iaf_neuron',
'positions': [[0.] * self._dimensions],
'extent': [self._L] * self._dimensions,
'edge_wrap': True
}
x = rnd.uniform(-self._L / 2., self._L / 2., self._N)
y = rnd.uniform(-self._L / 2., self._L / 2., self._N)
if self._dimensions == 3:
z = rnd.uniform(-self._L / 2., self._L / 2., self._N)
pos = zip(x, y, z)
else:
pos = zip(x, y)
ldict_t = {
'elements': 'iaf_neuron',
'positions': pos,
'extent': [self._L] * self._dimensions,
'edge_wrap': True
}
self._ls = topo.CreateLayer(ldict_s)
self._lt = topo.CreateLayer(ldict_t)
self._driver = topo.FindCenterElement(self._ls)
def test_PlotTargets(self):
"""Test plotting targets."""
ldict = {'elements': ['iaf_neuron', 'iaf_psc_alpha'], 'rows': 3,
'columns': 3,
'extent': [2., 2.], 'edge_wrap': True}
cdict = {'connection_type': 'divergent',
'mask': {'grid': {'rows': 2, 'columns': 2}}}
nest.ResetKernel()
l = topo.CreateLayer(ldict)
ian = [gid for gid in nest.GetLeaves(l)[0]
if nest.GetStatus([gid], 'model')[0] == 'iaf_neuron']
ipa = [gid for gid in nest.GetLeaves(l)[0]
if nest.GetStatus([gid], 'model')[0] == 'iaf_psc_alpha']
# connect ian -> all using static_synapse
cdict.update({'sources': {'model': 'iaf_neuron'},
'synapse_model': 'static_synapse'})
topo.ConnectLayers(l, l, cdict)
for k in ['sources', 'synapse_model']:
cdict.pop(k)
# connect ipa -> ipa using stdp_synapse
cdict.update({'sources': {'model': 'iaf_psc_alpha'},
'targets': {'model': 'iaf_psc_alpha'},
'synapse_model': 'stdp_synapse'})
topo.ConnectLayers(l, l, cdict)
for k in ['sources', 'targets', 'synapse_model']:
cdict.pop(k)
ctr = topo.FindCenterElement(l)
fig = topo.PlotTargets(ctr, l)
fig.gca().set_title('Plain call')
self.assertTrue(True)
def distancePlotsLazy(Start, End, Step, network, folder):
for events, title, neurons_folder in zip([network.events_ex, network.events_in], ["Excitatory", "Inhibitory"], ['/excitatory_neurons', '/inhibitory_neurons']):
for start, end in zip(np.arange(Start, End, Step), np.arange(Start + Step, End + Step, Step)):
plt.close()
currentTitle = title + 'Neurons from ' + str(start) + ' to ' + str(end)
print(currentTitle)
times, neurons = magic.distance(events, start, end, tp.FindCenterElement(network.l)[0])
magic.raster_plot(senders=times, timeS=neurons, title=currentTitle, gridSize=network.gridSize)
plt.savefig(folder + neurons_folder + '/dist_' + str(end) + '_raster_' + title + '.png',
dpi=300)
def plot_layer_targets(cds, savename):
layer = topo.CreateLayer(layer_dict)
for cd in cds:
topo.ConnectLayers(layer, layer, cd)
ctr = topo.FindCenterElement(layer)
fig = topo.PlotLayer(layer, nodesize=20)
topo.PlotTargets(ctr, layer, fig=fig, tgt_color="red")
plt.savefig("%s.png" % savename)
plt.close()
def plot_layer_targets(cds, savename):
nest.ResetKernel()
layer = topp.CreateLayer({"rows":rowcol, "columns":rowcol,
"extent":extent, "elements":model})
for cd in cds:
topp.ConnectLayers(layer, layer, cd)
ctr = topp.FindCenterElement(layer)
fig = topp.PlotLayer(layer, nodesize=20)
topp.PlotTargets(ctr,layer, fig=fig, tgt_color="red")
pylab.savefig("%s.png"%savename)
pylab.close()
def plotKernel(self, folder):
fig, ax = plt.subplots()
tp.PlotLayer(self.l, fig, nodesize=10)
ctr_elem = tp.FindCenterElement(self.l)
# import ipdb; ipdb.set_trace()
# tp.PlotKernel(ax, ctr_elem, mask=self.cdict_i2i['mask'], kern=self.cdict_i2i['kernel'])
tp.PlotTargets(ctr_elem, self.l, fig=fig, tgt_color='red',
mask=self.cdict_e2e['mask'], kernel=self.cdict_e2e['kernel'],
kernel_color='green', mask_color='green',
syn_type='exc_recurrent')
# tp.PlotKernel(ax, ctr_elem, mask=self.cdict_e2e['mask'], mask_color='green')
plt.xlabel(r'X')
plt.ylabel(r'Y')
plt.xlim(-.5, .5)
plt.ylim(-.5, .5)
plt.savefig(folder+'/kernel.pdf')
def plot_layer_targets(cds, savename):
nest.ResetKernel()
layer = topp.CreateLayer({
'rows': rowcol,
'columns': rowcol,
'extent': extent,
'elements': model
})
for cd in cds:
topp.ConnectLayers(layer, layer, cd)
ctr = topp.FindCenterElement(layer)
fig = topp.PlotLayer(layer, nodesize=20)
topp.PlotTargets(ctr, layer, fig=fig, tgt_color='red')
pylab.savefig('%s.png' % savename)
pylab.close()
def get_Relative_Weight(params, radius):
# Create a fictional network and count the number of target connections
layerProps = {
'rows': params['N'],
'columns': params['N'],
'extent': [params['visSize'], params['visSize']],
'edge_wrap': True,
'elements': 'iaf_cond_exp'
}
l = tp.CreateLayer(layerProps)
dict = {
"connection_type": "convergent",
"mask": {
"circular": {
"radius": radius
}
},
"kernel": 1.0,
"weights": {
"gaussian": {
"p_center": 1.0,
"sigma": radius / 3.0
}
}
}
tp.ConnectLayers(l, l, dict)
ctr = tp.FindCenterElement(l)
targets = tp.GetTargetNodes(ctr, l)
# print ("Number of targets = ",len(targets[0]))
conn = nest.GetConnections(ctr, targets[0])
st = nest.GetStatus(conn)
w = 0.0
for n in np.arange(len(st)):
w += st[n]['weight']
# print ("Total weight = ",w)
return w, len(targets[0])
def plot_connections(layer_dict, conn_dict, file_name):
conn_dict['synapse_model'] = 'static_synapse'
layer = tp.CreateLayer(layer_dict)
tp.ConnectLayers(layer, layer, conn_dict)
fig = tp.PlotLayer(layer)
tp.PlotTargets(tp.FindCenterElement(layer),
layer,
fig=fig,
tgt_color='red')
pylab.savefig(file_name)
#pylab.show()
tp.DumpLayerConnections(layer, conn_dict['synapse_model'],
file_name + ".dump")
positions = tp.GetTargetPositions(layer, layer)
def _build(self):
'''Create populations.'''
if self._open_bc:
ldict_s = {
'elements': 'iaf_psc_alpha',
'positions': [[self._x_d, self._y_d]],
'extent': [self._L] * self._dimensions,
'edge_wrap': False
}
x = rnd.uniform(-self._L / 2., self._L / 2., self._N)
y = rnd.uniform(-self._L / 2., self._L / 2., self._N)
pos = list(zip(x, y))
ldict_t = {
'elements': 'iaf_psc_alpha',
'positions': pos,
'extent': [self._L] * self._dimensions,
'edge_wrap': False
}
self._ls = topo.CreateLayer(ldict_s)
self._lt = topo.CreateLayer(ldict_t)
self._driver = nest.GetLeaves(self._ls)[0]
else:
ldict_s = {
'elements': 'iaf_psc_alpha',
'positions': [[0.] * self._dimensions],
'extent': [self._L] * self._dimensions,
'edge_wrap': True
}
x = rnd.uniform(-self._L / 2., self._L / 2., self._N)
y = rnd.uniform(-self._L / 2., self._L / 2., self._N)
if self._dimensions == 3:
z = rnd.uniform(-self._L / 2., self._L / 2., self._N)
pos = list(zip(x, y, z))
else:
pos = list(zip(x, y))
ldict_t = {
'elements': 'iaf_psc_alpha',
'positions': pos,
'extent': [self._L] * self._dimensions,
'edge_wrap': True
}
self._ls = topo.CreateLayer(ldict_s)
self._lt = topo.CreateLayer(ldict_t)
self._driver = topo.FindCenterElement(self._ls)
def conn_figure(fig,
layer,
connd,
targets=None,
showmask=True,
showkern=False,
xticks=range(-5, 6),
yticks=range(-5, 6),
xlim=[-5.5, 5.5],
ylim=[-5.5, 5.5]):
if targets is None:
targets = ((tp.FindCenterElement(layer), 'red'), )
tp.PlotLayer(layer, fig=fig, nodesize=60)
for src, clr in targets:
if showmask:
mask = connd['mask']
else:
mask = None
if showkern:
kern = connd['kernel']
else:
kern = None
tp.PlotTargets(src,
layer,
fig=fig,
mask=mask,
kernel=kern,
src_size=250,
tgt_color=clr,
tgt_size=20,
kernel_color='green')
beautify_layer(layer,
fig,
xlim=xlim,
ylim=ylim,
xticks=xticks,
yticks=yticks,
xlabel='',
ylabel='')
fig.gca().grid(False)
def _build(self):
'''Create populations.'''
ldict_s = {
'elements': 'iaf_neuron',
'positions': [(0., 0.)],
'extent': [self._L] * 2,
'edge_wrap': True
}
x = rnd.uniform(-self._L / 2., self._L / 2., self._N)
y = rnd.uniform(-self._L / 2., self._L / 2., self._N)
ldict_t = {
'elements': 'iaf_neuron',
'positions': zip(x, y),
'extent': [self._L] * 2,
'edge_wrap': True
}
self._ls = topo.CreateLayer(ldict_s)
self._lt = topo.CreateLayer(ldict_t)
self._driver = topo.FindCenterElement(self._ls)
def test_PlotKernel(self):
"""Test plotting kernels."""
ldict = {'elements': 'iaf_neuron', 'rows': 3, 'columns': 3,
'extent': [2., 2.], 'edge_wrap': True}
nest.ResetKernel()
l = topo.CreateLayer(ldict)
f = plt.figure()
a1 = f.add_subplot(221)
ctr = topo.FindCenterElement(l)
topo.PlotKernel(a1, ctr, {'circular': {'radius': 1.}},
{'gaussian': {'sigma': 0.2}})
a2 = f.add_subplot(222)
topo.PlotKernel(a2, ctr, {
'doughnut': {'inner_radius': 0.5, 'outer_radius': 0.75}})
a3 = f.add_subplot(223)
topo.PlotKernel(a3, ctr, {'rectangular': {'lower_left': [-.5, -.5],
'upper_right': [0.5, 0.5]}})
self.assertTrue(True)
def csa_topology_example():
# layers have 20x20 neurons and extent 1 x 1
pop1 = topo.CreateLayer({'elements': 'iaf_neuron',
'rows': 20, 'columns': 20})
pop2 = topo.CreateLayer({'elements': 'iaf_neuron',
'rows': 20, 'columns': 20})
# create CSA-style geometry functions and metric
g1 = geometryFunction(pop1)
g2 = geometryFunction(pop2)
d = csa.euclidMetric2d(g1, g2)
# Gaussian connectivity profile, sigma = 0.2, cutoff at 0.5
cs = csa.cset(csa.random * (csa.gaussian(0.2, 0.5) * d), 10000.0, 1.0)
# create connections
nest.CGConnect(pop1, pop2, cs, {"weight": 0, "delay": 1})
# show targets of center neuron
topo.PlotTargets(topo.FindCenterElement(pop1), pop2)
def conn_figure_3d(fig,
layer,
connd,
targets=None,
showmask=True,
showkern=False,
xticks=range(-5, 6),
yticks=range(-5, 6),
xlim=[-5.5, 5.5],
ylim=[-5.5, 5.5]):
if targets is None:
targets = ((tp.FindCenterElement(layer), 'red'), )
tp.PlotLayer(layer, fig=fig, nodesize=20, nodecolor=(.5, .5, 1.))
for src, clr in targets:
if showmask:
mask = connd['mask']
else:
mask = None
if showkern:
kern = connd['kernel']
else:
kern = None
tp.PlotTargets(src,
layer,
fig=fig,
mask=mask,
kernel=kern,
src_size=250,
tgt_color=clr,
tgt_size=60,
kernel_color='green')
ax = fig.gca()
ax.set_aspect('equal', 'box')
plt.draw()
}, {
"targets": {
"model": "TpInter"
}
}]:
retThal.update(conn)
topo.ConnectLayers(retina, Tp, retThal)
#! Checks on connections
#! ---------------------
#! As a very simple check on the connections created, we inspect
#! the connections from the central node of various layers.
#! Connections from Retina to TpRelay
topo.PlotTargets(topo.FindCenterElement(retina), Tp, 'TpRelay', 'AMPA')
pylab.title('Connections Retina -> TpRelay')
pylab.show()
#! Connections from TpRelay to L4pyr in Vp (horizontally tuned)
topo.PlotTargets(topo.FindCenterElement(Tp), Vp_h, 'L4pyr', 'AMPA')
pylab.title('Connections TpRelay -> Vp(h) L4pyr')
pylab.show()
#! Connections from TpRelay to L4pyr in Vp (vertically tuned)
topo.PlotTargets(topo.FindCenterElement(Tp), Vp_v, 'L4pyr', 'AMPA')
pylab.title('Connections TpRelay -> Vp(v) L4pyr')
pylab.show()
#! Recording devices
#! =================
# l1_children is a work-around until NEST 3.0 is released
l1_children = nest.hl_api.GetChildren(l1)[0]
# extract position information, transpose to list of x, y and z positions
xpos, ypos, zpos = zip(*topo.GetPosition(l1_children))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xpos, ypos, zpos, s=15, facecolor='b', edgecolor='none')
# Gaussian connections in full volume [-0.75,0.75]**3
topo.ConnectLayers(l1, l1,
{'connection_type': 'divergent', 'allow_autapses': False,
'mask': {'volume': {'lower_left': [-0.75, -0.75, -0.75],
'upper_right': [0.75, 0.75, 0.75]}},
'kernel': {'gaussian': {'p_center': 1., 'sigma': 0.25}}})
# show connections from center element
# sender shown in red, targets in green
ctr = topo.FindCenterElement(l1)
xtgt, ytgt, ztgt = zip(*topo.GetTargetPositions(ctr, l1)[0])
xctr, yctr, zctr = topo.GetPosition(ctr)[0]
ax.scatter([xctr], [yctr], [zctr], s=40, facecolor='r', edgecolor='none')
ax.scatter(xtgt, ytgt, ztgt, s=40, facecolor='g', edgecolor='g')
tgts = topo.GetTargetNodes(ctr, l1)[0]
d = topo.Distance(ctr, tgts)
plt.figure()
plt.hist(d, 25)
# plt.show()
'mask': {
'rectangular': {
'lower_left': [-2., -1.],
'upper_right': [2., 1.]
}
}
}
tp.ConnectLayers(l, l, conndict)
# { end #}
fig = plt.figure()
fig.add_subplot(121)
conn_figure(fig,
l,
conndict,
targets=((tp.FindCenterElement(l), 'red'),
(tp.FindNearestElement(l, [4., 5.]), 'yellow')))
# same another time, with periodic bcs
lpbc = tp.CreateLayer({
'rows': 11,
'columns': 11,
'extent': [11., 11.],
'elements': 'iaf_neuron',
'edge_wrap': True
})
tp.ConnectLayers(lpbc, lpbc, conndict)
fig.add_subplot(122)
conn_figure(fig,
lpbc,
conndict,
# { vislayer #}
l = tp.CreateLayer({'rows': 21, 'columns': 21, 'elements': 'iaf_neuron'})
conndict = {
'connection_type': 'divergent',
'mask': {
'circular': {
'radius': 0.4
}
},
'kernel': {
'gaussian': {
'p_center': 1.0,
'sigma': 0.15
}
}
}
tp.ConnectLayers(l, l, conndict)
fig = tp.PlotLayer(l, nodesize=80)
ctr = tp.FindCenterElement(l)
tp.PlotTargets(ctr,
l,
fig=fig,
mask=conndict['mask'],
kernel=conndict['kernel'],
src_size=250,
tgt_color='red',
tgt_size=20,
kernel_color='green')
# { end #}
plt.savefig('../user_manual_figures/vislayer.png', bbox_inches='tight')
g1 = geometryFunction(pop1)
g2 = geometryFunction(pop2)
d = csa.euclidMetric2d(g1, g2)
"""
The connection set ``cs`` describes a Gaussian connectivity profile
with sigma = 0.2 and cutoff at 0.5, and two values (10000.0 and 1.0)
used as weight and delay, respectively.
"""
cs = csa.cset(csa.random * (csa.gaussian(0.2, 0.5) * d), 10000.0, 1.0)
"""
We can now connect the populations using the `CGConnect` function.
It takes the IDs of pre- and postsynaptic neurons (``pop1`` and
``pop2``), the connection set (``cs``) and a dictionary that maps
the parameters weight and delay to positions in the value set
associated with the connection set.
"""
# This is a work-around until NEST 3.0 is released. It will issue a deprecation
# warning.
pop1_gids = nest.GetLeaves(pop1)[0]
pop2_gids = nest.GetLeaves(pop2)[0]
nest.CGConnect(pop1_gids, pop2_gids, cs, {"weight": 0, "delay": 1})
"""
Finally, we use the `PlotTargets` function to show all targets in
``pop2`` starting at the center neuron of ``pop1``.
"""
topo.PlotTargets(topo.FindCenterElement(pop1), pop2)
nest.Connect(Neuron_GIDs, sd_list[-1])
multimeter_exc.append(
nest.Create("multimeter",
params={
"interval":
0.1,
"record_from": ["V_m", "g_ex", "g_in"],
"withgid":
True,
"to_file":
True,
"label":
"mm_vLSPS_postsynaptic_cell_%s_%s" %
(vS1_Layer_Name[l], n_type)
}))
print(ntop.FindCenterElement(SubSubRegion_Excitatory[-1]))
nest.Connect(multimeter_exc[-1],
ntop.FindCenterElement(SubSubRegion_Excitatory[-1]))
elif n_type_info['EorI'] == "I":
pos = None
pos = Neuron_pos[np.where(Neuron_pos[:, :, :, 3] == n_type_index)]
nest.SetDefaults(
elements, {
"I_e": float(neuron_info['I_ex']),
"V_th": float(neuron_info['spike_threshold']),
"V_reset": float(neuron_info['reset_value']),
"t_ref": float(neuron_info['absolute_refractory_period'])
})
newlayer = CreateSubSubRegion(topo_extend, topo_center, pos,
neuronmodel)
SubSubRegion_Inhibitory.append(newlayer)