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 main(plot_torus=True,
plot_target=True,
num_plot_tagets=3,
use_lr_connection_type=NETWORK_DICT["np"]):
"""
Main function running the test routines
:param plot_torus: Flag to plot neural layer
:param plot_target: Flag to plot targets to control established connections
:param num_plot_tagets: Plot connections of the num_plot_targts-th node
:param use_lr_connection_type: Define the type of long range connections
:return None
"""
torus_layer = create_torus_layer_uniform()
if plot_torus:
fig, _ = plt.subplots()
tp.PlotLayer(torus_layer, fig)
plt.show()
create_local_circular_connections(torus_layer)
debug_layer = create_distant_connections(
torus_layer, connection_type=use_lr_connection_type)
if plot_target:
choice = np.random.choice(np.asarray(debug_layer),
num_plot_tagets,
replace=False)
for c in choice:
tp.PlotTargets([int(c)], torus_layer)
plt.show()
adj_mat = create_adjacency_matrix(
nest.GetNodes(torus_layer)[0],
nest.GetNodes(torus_layer)[0])
eigenvalue_analysis(adj_mat, plot=True)
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_conn(self):
"""Plot the targets of a unit using nest.topology function."""
# TODO: Get our own version so we can plot convergent connections
import nest.topology as tp
import matplotlib.pyplot as plt
fig, ax = plt.subplots() # pylint:disable=invalid-name
tp.PlotLayer(self.target.gid, fig)
ctr = self.source.find_center_element(
population=self.source_population)
# Plot the kernel and mask if the connection is topological or rescaled.
try:
tp.PlotKernel(ax,
ctr,
self.nest_params['mask'],
kern=self.nest_params['kernel'],
kernel_color='green')
except (AttributeError, KeyError, ValueError):
# AttributeError, KeyError: if no nest_params mask or kernel
# ValueError: if the mask or kernel cannot be plotted (custom mask)
pass
try:
tp.PlotTargets(ctr,
self.target.gid,
tgt_model=self.target_population,
syn_type=self.synapse_model,
fig=fig,
tgt_size=40,
src_size=250,
tgt_color='red')
except ValueError:
print((f"Not plotting targets: the center unit {ctr[0]} has no " +
f"target within connection {self.__str__}"))
plt.suptitle(
f"Plot of targets of a single source unit.\n"
f"Target units' pop: {self.target.name},"
f"{str(self.target_population)} (targets in red),\n"
f"Source unit's population: {self.source.name},"
f"{str(self.source_population)}\n"
f"Connection name: {self.name},\n",
fontsize=7)
footnote = (
"NB: The actual connection probability might be smaller "
"than it seems if there is multiple units per grid position"
" in the target population(s)")
ax.annotate(footnote,
xy=(1, 0),
xycoords='axes fraction',
fontsize=5,
xytext=(0, -15),
textcoords='offset points',
ha='right',
va='top')
return fig
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 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 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 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 main(plot_torus=True,
plot_target=True,
num_plot_tagets=3,
use_lr_connection_type=NETWORK_DICT["np"]):
"""
Main function running the test routines
:param plot_torus: Flag to plot neural layer
:param plot_target: Flag to plot targets to control established connections
:param num_plot_tagets: Plot connections of the num_plot_targts-th node
:param use_lr_connection_type: Define the type of long range connections
"""
torus_layer = create_torus_layer_uniform()
if plot_torus:
fig, _ = plt.subplots()
tp.PlotLayer(torus_layer, fig)
plt.show()
create_local_circular_connections(torus_layer)
if use_lr_connection_type == NETWORK_DICT["np"]:
debug_layer = create_distant_np_connections(torus_layer)
elif use_lr_connection_type == NETWORK_DICT["random"]:
debug_layer = create_random_patches(torus_layer)
elif use_lr_connection_type == NETWORK_DICT["overlapping"]:
debug_layer = create_overlapping_patches(torus_layer)
elif use_lr_connection_type == NETWORK_DICT["shared"]:
debug_layer = create_shared_patches(torus_layer)
elif use_lr_connection_type == NETWORK_DICT["partially-overlapping"]:
debug_layer = create_partially_overlapping_patches(torus_layer)
else:
raise ValueError("Not a valid network")
if plot_target:
choice = np.random.choice(np.asarray(debug_layer),
num_plot_tagets,
replace=False)
for c in choice:
tp.PlotTargets([int(c)], torus_layer)
plt.show()
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()
}
},
'delays': 1.0
}
topo.ConnectLayers(a, b, conndict)
pylab.clf()
# plot targets of neurons in different grid locations
# first, clear existing figure, get current figure
pylab.clf()
fig = pylab.gcf()
# plot targets of two source neurons into same figure, with mask
for src_pos in [[15, 15], [0, 0]]:
# obtain node id for center
src = topo.GetElement(a, src_pos)
topo.PlotTargets(src, b, mask=conndict['mask'], fig=fig)
# beautify
pylab.axes().set_xticks(pylab.arange(-1.5, 1.55, 0.5))
pylab.axes().set_yticks(pylab.arange(-1.5, 1.55, 0.5))
pylab.grid(True)
pylab.axis([-2.0, 2.0, -2.0, 2.0])
pylab.axes().set_aspect('equal', 'box')
pylab.title('Connection targets')
# pylab.savefig('connex_ew.pdf')
# { 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')
def simulation(Params):
root_folder = Params['root_folder']
if not os.path.isdir(root_folder):
os.makedirs(root_folder)
net_folder = root_folder + Params['net_folder']
if not os.path.isdir(net_folder):
os.makedirs(net_folder)
conn_folder = net_folder + '/connections'
if not os.path.isdir(conn_folder):
os.makedirs(conn_folder)
data_folder = net_folder + Params['data_folder']
if not os.path.isdir(data_folder):
os.makedirs(data_folder)
#! =================
#! Import network
#! =================
# NEST Kernel and Network settings
nest.ResetKernel()
nest.ResetNetwork()
nest.SetKernelStatus({"local_num_threads": Params['threads'],'resolution': Params['resolution']})
nest.SetStatus([0],{'print_time': True})
# initialize random seed
import time
msd = int(round(time.time() * 1000))
nest.SetKernelStatus({'grng_seed' : msd})
# Tom debug
# nest.SetKernelStatus({'rng_seeds' : range(msd+Params['threads']+1, msd+2*Params['threads']+1)})
nest.SetKernelStatus({'rng_seeds': range(msd + Params['threads'] + 1, msd + 2 * Params['threads'] + 1)})
import importlib
network = importlib.import_module(Params['network'])
reload(network)
models, layers, conns = network.get_Network(Params)
#import network_full_keiko
#reload(network_full_keiko)
# models, layers, conns = network_full_keiko.get_Network(Params)
# TODO this should come from network file
synapse_models = ['AMPA_syn', 'NMDA_syn', 'GABA_A_syn', 'GABA_B_syn', 'reticular_projection']
# synapse_models = ['AMPA_syn', 'NMDA_syn', 'GABA_A_syn', 'GABA_B_syn']
# Create models
print('Creating models...', end="")
for m in models:
print('.', end="")
# print(m), print('\n')
nest.CopyModel(m[0], m[1], m[2])
print(' done.')
# Create layers, store layer info in Python variable
print('Creating layers...', end="")
for l in layers:
print('.', end="")
exec '%s = tp.CreateLayer(l[1])' % l[0] in globals(), locals()
print(' done.')
# now either create brand new connections, or load connectivity files from
# connections_file
connections_file = conn_folder + '/connections.pickle'
# assume alread runned before, and it is going to load connections and layers
if Params.has_key('load_connections') and Params['load_connections']:
if not os.path.isfile(connections_file):
raise IOError('File with connections was never created: %s' % connections_file)
print('Loading connections...', end="")
with open(connections_file) as f:
(pre, post_intact, post_scrambled, w, d, syn) = pickle.load(f)
print(' done.')
if Params['scrambled']:
post = post_scrambled
else:
post = post_intact
print('Connecting neurons...', end="")
con_dict = {'rule': 'one_to_one'}
for (pi, ti, wi, di, syni) in zip(pre, post, w, d, syn):
print('.', end="")
# print(syni)
syn_dict = {"model": syni,
'weight': wi,
'delay': di
}
nest.Connect(pi, ti, con_dict, syn_dict)
print(' done.')
# print('Connection retina...', end="")
# # Create connections, need to insert variable names
# for c in conns:
# if c[0]=='Retina_layer':
# eval('tp.ConnectLayers(%s,%s,c[2])' % (c[0], c[1]))
# print('done.')
else:
# Create connections, need to insert variable names
print('Creating connections...', end="")
for c in conns:
if Params['Np'] < 40:
print('%s -> %s' % (c[0], c[1]))
c[2]['allow_oversized_mask'] = True
else:
print('.', end="")
eval('tp.ConnectLayers(%s,%s,c[2])' % (c[0], c[1]))
print(' done.')
if Params.has_key('dump_connections') and Params['dump_connections']:
import glob
# First dump all nodes and connections to files
for l in layers:
print('dumping connections for %s' % l[0])
eval("tp.DumpLayerNodes(%s, conn_folder + '/nodes_%s.dump')" % (l[0], l[0]) )
for synapse in synapse_models:
eval("tp.DumpLayerConnections(%s, '%s', conn_folder + '/connections_%s_%s.dump')" % (l[0], synapse, l[0], synapse) )
# Now load all nodes, and mark which physical layer they belong to
all_nodes_files = glob.glob(conn_folder + "/nodes_*.dump")
if len(all_nodes_files) < 1:
raise ValueError('No files save for this configuration. First '
'run with load_connection = False')
pop_nodes = [None] * len(all_nodes_files)
pop_names = [None] * len(all_nodes_files)
perm_pop_nodes = [None] * len(all_nodes_files)
for (idx, pop_file) in enumerate(all_nodes_files):
population = pop_file[pop_file.find('nodes_')+6:-5]
pop_names[idx] = population
print('Loading nodes for population %s' % population)
pop_nodes[idx] = np.loadtxt(pop_file)
# identify physical layers
n_layer = 0
n_nodes = pop_nodes[idx].shape[0]
layers = np.zeros((n_nodes,1))
for i_line in range(n_nodes):
if pop_nodes[idx][i_line, 1] == -3.9 and pop_nodes[idx][i_line, 2] == 3.9:
n_layer += 1
layers[i_line] = n_layer
pop_nodes[idx] = np.append(pop_nodes[idx], layers, 1)
# Finally, load connections and save a big vector for each one of the 5
# parameters needed by nest.Connect: pre, post, w, d, and synapse_model
# Save unmodified and scrambled versions in network.pickle
all_conn_files = glob.glob(conn_folder + "/connections_*.dump")
pre = []
post_intact = []
post_scrambled = []
w = []
d = []
syn = []
for (idx, pop_conn_file) in enumerate(all_conn_files):
population = pop_conn_file[pop_conn_file.find('connections_')+12:-5]
print('Scrambling connections for %s (%d out of %d files)' % (population, idx+1, len(all_conn_files)))
synapse_model = [s for i,s in enumerate(synapse_models) if s in population][0]
print('Loading file')
pop_conns = np.loadtxt(pop_conn_file)
tn_lines = pop_conns.shape[0]
if tn_lines > 0:
syn.append(synapse_model)
pre.append(pop_conns[:,0].astype(int))
post_intact.append(pop_conns[:,1].astype(int))
w.append(pop_conns[:,2])
d.append(pop_conns[:,3].astype(int))
# Do the actual scrfambling
# TODO this should be optimized, it is way too slow
if any(w == population[0:2] for w in ['Vs', 'Vp']):
print('Scrambling...', end="")
for i_line in range(tn_lines):
if not i_line % round(tn_lines/10.):
print('%d%%, ' % (round(100.*i_line/tn_lines) if i_line > 0 else 0), end="")
for (i_pop, this_pop) in enumerate(pop_nodes):
t_idx = np.where(this_pop[:,0] == pop_conns[i_line, 1])[0]
if t_idx.size:
break
if not t_idx.size:
raise ValueError('Could not find tartget %s for node %s' % (pop_conns[i_line, 1], pop_conns[i_line, 0]))
if len(t_idx) > 1:
raise ValueError('Target %s apears in more than one population' % (pop_conns[i_line, 1], pop_conns[i_line, 0]))
new_t_idx = t_idx[0]
# For the moment, only scramble connections within the visual cortex
if any(w == pop_names[i_pop][0:2] for w in ['Vs', 'Vp']):
possible_targets = pop_nodes[i_pop][pop_nodes[i_pop][:,3] == pop_nodes[i_pop][new_t_idx,3], 0]
pop_conns[i_line, 1] = np.random.permutation(possible_targets)[0]
print('done.')
post_scrambled.append(pop_conns[:,1].astype(int))
# save results
print('Saving results...', end="")
with open(connections_file, 'w') as f:
pickle.dump((pre, post_intact, post_scrambled, w, d, syn), f)
print(' done.')
if Params.has_key('show_V4_num_conn_figure') and Params['show_V4_num_conn_figure']:
horizontal_nodes = nest.GetLeaves(Vp_horizontal, properties={'model': 'L4_exc'}, local_only=True)[0]
vertical_nodes = nest.GetLeaves(Vp_vertical, properties={'model': 'L4_exc'}, local_only=True)[0]
n_conns_hor = []
for (idx, horizontal_node) in enumerate(horizontal_nodes):
tgt_map = []
this_conns = nest.GetConnections([horizontal_node], horizontal_nodes, synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_hor.append(len(tgt_map))
plt.figure()
plt.hist(n_conns_hor, range(0, max(n_conns_hor + [30])))
plt.title('# of connections Vp(h) L4pyr -> Vp(h) L4Pyr')
n_conns_hor = []
for (idx, horizontal_node) in enumerate(horizontal_nodes):
tgt_map = []
this_conns = nest.GetConnections([horizontal_node], vertical_nodes, synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_hor.append(len(tgt_map))
# nest.DisconnectOneToOne(tp_node, tgt_map[0], {"synapse_model": "AMPA_syn"})
#nest.Disconnect([tp_node], tgt_map, 'one_to_one', {"synapse_model": "AMPA_syn"})
plt.figure()
plt.hist(n_conns_hor, range(0, max(n_conns_hor + [30])))
plt.title('# of connections Vp(h) L4pyr -> Vp(v) L4Pyr')
n_conns_ver = []
for (idx, vertical_node) in enumerate(vertical_nodes):
tgt_map = []
this_conns = nest.GetConnections([vertical_node], vertical_nodes, synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_ver.append(len(tgt_map))
plt.figure()
plt.hist(n_conns_ver, range(0, max(n_conns_ver + [30])))
plt.title('# of connections Vp(v) L4pyr -> Vp(v) L4Pyr')
n_conns_ver = []
for (idx, vertical_node) in enumerate(vertical_nodes):
tgt_map = []
this_conns = nest.GetConnections([vertical_node], horizontal_nodes, synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_ver.append(len(tgt_map))
plt.figure()
plt.hist(n_conns_ver, range(0, max(n_conns_ver + [30])))
plt.title('# of connections Vp(v) L4pyr -> Vp(h) L4Pyr')
plt.show()
# Check connections
if Params.has_key('show_center_connectivity_figure') and Params['show_center_connectivity_figure']:
# this code only gives one element, and you have no control over the
# model it comes from...
# tp.PlotTargets(tp.FindCenterElement(Vp_horizontal), Vp_horizontal, 'L56_exc', 'AMPA_syn')
# tp.PlotTargets(tp.FindCenterElement(Vp_horizontal), Vp_vertical, 'L56_exc', 'AMPA_syn')
# tp.PlotTargets(tp.FindCenterElement(Vp_vertical), Vp_horizontal, 'L56_exc', 'AMPA_syn')
# tp.PlotTargets(tp.FindCenterElement(Vp_vertical), Vp_vertical, 'L56_exc', 'AMPA_syn')
# this way I can get all nodes in the positions, and later filter per Model using GetStatus
# IMPORTANT: when layer has even number of neurons per line/column
# using center (0,0) makes the function return 4 neurons
# per postiion since they all have the same distance.
# Using (-0,1, 0,1) solves this problem.
# When using odd number perhaps this has to change...
for src_label in ['Tp_layer', 'Vp_vertical', 'Vp_horizontal']:
for tgt_label in ['Tp_layer', 'Vp_vertical', 'Vp_horizontal']:
if src_label == 'Tp_layer' and src_label == tgt_label:
continue
print('source population %s' % src_label)
print('target population %s' % tgt_label)
n_plot = 0
f = plt.figure()
f.canvas.set_window_title('AMPA Connections: %s -> %s' % (src_label, tgt_label))
all_center = eval('tp.FindNearestElement(%s, (-0.1, 0.1), True)[0]' % src_label)
for n in all_center:
s = nest.GetStatus([n])
p = tp.GetPosition([n])
# print('%s : (%2.2f, %2.2f)' % (s[0]['model'], p[0][0], p[0][1]))
m1 = s[0]['model'].name
print('Source neuron model %s' % m1)
if m1.find('_exc') > -1:
if src_label.find('Tp_') > -1:
print( 'found Tp_')
# target has to be one of Vp_ or Vs_ models
target_models = ['L23_exc', 'L4_exc', 'L56_exc']
elif src_label.find('Vp_') > -1 or src_label.find('Vs') > -1:
# if target is Vp_ of Vs_ too, then one of those models
if tgt_label.find('Vp_') > -1:
target_models = ['L23_exc', 'L4_exc', 'L56_exc']
elif tgt_label.find('Tp_') > -1:
# otherwise it has to be a Tp_target
target_models = ['Tp_exc']
else:
raise ValueError('Invalide target %s for source model: %s' % (tgt_label, src_label))
else:
raise ValueError('Invalide source model: %s' % (src_label))
for m2 in target_models:
print('Target neuron model %s' % m2)
try:
get_targets_command = 'tp.GetTargetNodes([n], %s, m2, "AMPA_syn")[0]' % tgt_label
print(get_targets_command)
targets = eval(get_targets_command)
if len(targets) > 0:
n_plot += 1
f.add_subplot(3,5,n_plot)
eval('tp.PlotTargets([n], %s, m2, "AMPA_syn", f)' % tgt_label)
plt.title('%s -> %s' % (m1, m2), fontsize=9)
except:
print('didnt work')
f.savefig(data_folder + '/connectivity_%s_%s.png' % (src_label, tgt_label), dpi=100)
if Params.has_key('show_V4_connectivity_figure') and Params['show_V4_connectivity_figure']:
Vp_hor_gids = tp.GetElement(Vp_horizontal, [0,0])
n_Vp_hor = len(Vp_hor_gids)
f = []
for idx in range(n_Vp_hor):
f.append(plt.figure())
positions = range(0,41,10)
positions[-1] -= 1
for xi in range(len(positions)):
for yi in range(len(positions)):
print("Position [%d,%d] : %d" %(xi,yi,yi*(len(positions))+xi+1))
x = positions[xi]
y = positions[yi]
Vp_hor_gids = tp.GetElement(Vp_horizontal, [x,y])
Vp_hor_status = nest.GetStatus(Vp_hor_gids)
for (idx, n) in enumerate(Vp_hor_status):
if n['Tau_theta'] == 2.0:
print(idx)
try:
f[idx].add_subplot(len(positions), len(positions), yi*(len(positions))+xi+1)
tp.PlotTargets([Vp_hor_gids[idx]], Vp_horizontal, 'L4_exc', 'AMPA_syn', f[idx])
except:
print('%i bad' % Vp_hor_gids[idx])
plt.show()
if Params.has_key('dry_run') and Params['dry_run']:
print('Only generation, loading and ploting network. No actual simulation done.')
return
'''
# pablo
# Create vertical grating
for n in nest.GetLeaves(Retina_layer)[0]:
retina_0 = (nest.GetLeaves(Retina_layer)[0])[0]
col = (n-retina_0)/Params['Np']
cells_per_degree = Params['Np']/Params['visSize']
cells_per_cycle = cells_per_degree/Params['spatial_frequency']
nest.SetStatus([n], { "phase": col * 360/(cells_per_cycle-1) })
'''
### keiko
if Params['lambda_dg'] >= 0:
[nest.SetStatus([n], {"phase": phaseInit(tp.GetPosition([n])[0],
Params["lambda_dg"],
Params["phi_dg"])})
for n in nest.GetLeaves(Retina_layer)[0]]
else:
# Leonardo: Random retina input
[nest.SetStatus([n], {"phase": phaseInit(tp.GetPosition([n])[0],
np.pi * np.random.rand(),
np.pi * np.random.rand())})
for n in nest.GetLeaves(Retina_layer)[0]]
# --------------------------------------------------------------------#
# ---------- SET IB NEURONS ----------------------------------------- #
# --------------------------------------------------------------------#
# 30% of Cortex L56 excitatory neurons are intrinsically bursting(IB) neuron.
# That is achieved by setting pacemaker current I_h.
# So select 30% of L56_exc neuron, and change h_g_peak from 0.0 to 1.0.
# (Other cortical neuron do not have I_h, thus h_g_peak=0.0)
L56_vertical_idx = [nd for nd in nest.GetLeaves(Vp_vertical)[0] if nest.GetStatus([nd], 'model')[0]=='L56_exc']
L56_horizontal_idx = [nd for nd in nest.GetLeaves(Vp_horizontal)[0] if nest.GetStatus([nd], 'model')[0]=='L56_exc']
num_neuron = len(L56_vertical_idx)
num_ib = int(num_neuron*0.3)
ridx_vertical = np.random.randint(num_neuron, size=(1,num_ib))[0]
ridx_horizontal = np.random.randint(num_neuron, size=(1,num_ib))[0]
for i in range(1,num_ib,1):
nest.SetStatus([L56_vertical_idx[ridx_vertical[i]]], {'g_peak_h': 1.0})
nest.SetStatus([L56_horizontal_idx[ridx_horizontal[i]]], {'g_peak_h': 1.0})
# initiate network activity
#nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'rate': Params['ret_rate']})
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'rate': Params['ret_rate']})
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 0.0})
nest.Simulate(500.0)
#! =================
#! Recording devices
#! =================
print('Connecting recorders...', end="")
nest.CopyModel('multimeter', 'RecordingNode',
params = {'interval' : Params['resolution'],
'record_from': ['V_m'],#,
# Put back when plotting synaptic currents, otherwise makes everything slower for no reason
# 'I_syn_AMPA',
# 'I_syn_NMDA',
# 'I_syn_GABA_A',
# 'I_syn_GABA_B',
# 'g_AMPA',
# 'g_NMDA',
# 'g_GABA_A',
# 'g_GABA_B'],
#'I_NaP',
#'I_KNa',
#'I_T',
#'I_h'
#]
'record_to' : ['memory'],
'withgid' : True,
'withtime' : True})
recorders = []
'''
for population, model in [(Retina_layer, 'Retina'),
(Tp_layer , 'Tp_exc'),
(Tp_layer , 'Tp_inh'),
(Rp_layer , 'Rp'),
(Vp_vertical, 'L23_exc'),
(Vp_horizontal, 'L23_exc'),
(Vp_vertical, 'L23_inh'),
(Vp_horizontal, 'L23_inh'),
(Vp_vertical, 'L4_exc'),
(Vp_horizontal, 'L4_exc'),
(Vp_vertical, 'L4_inh'),
(Vp_horizontal, 'L4_inh'),
(Vp_vertical, 'L56_exc'),
(Vp_horizontal, 'L56_exc'),
(Vp_vertical, 'L56_inh'),
(Vp_horizontal, 'L56_inh'),
(Vs_vertical, 'L23_exc'),
(Vs_horizontal, 'L23_exc'),
(Vs_vertical, 'L23_inh'),
(Vs_horizontal, 'L23_inh'),
(Vs_vertical, 'L4_exc'),
(Vs_horizontal, 'L4_exc'),
(Vs_vertical, 'L4_inh'),
(Vs_horizontal, 'L4_inh'),
(Vs_vertical, 'L56_exc'),
(Vs_horizontal, 'L56_exc'),
(Vs_vertical, 'L56_inh'),
(Vs_horizontal, 'L56_inh')]:
'''
# for population, model in [(Retina_layer, 'Retina'),
# (Tp_layer , 'Tp_exc'),
# (Tp_layer , 'Tp_inh'),
# (Vp_vertical, 'L4_exc'),
# (Vp_vertical, 'L4_inh'),
# (Vp_horizontal, 'L4_exc'),
# (Vp_vertical, 'L23_exc'),
# (Vp_horizontal, 'L23_exc'),
# (Vp_vertical, 'L56_exc'),
# (Rp_layer, 'Rp')]:
for population, model in [(Retina_layer, 'Retina'),
# (Tp_layer , 'Tp_exc'),
# (Tp_layer , 'Tp_inh'),
(Vp_vertical, 'L4_inh'),
(Vp_horizontal, 'L4_exc'),
(Vp_horizontal, 'L23_exc'),
# (Rp_layer, 'Rp'),
(Vp_vertical, 'L23_exc'),
(Vp_vertical, 'L4_exc'),
(Vp_vertical, 'L56_exc'),
(Vs_vertical, 'L23_exc'),
(Vs_vertical, 'L4_exc'),
(Vs_vertical, 'L56_exc'),
]:
print('.', end="")
rec = nest.Create('RecordingNode')
recorders.append([rec,population,model])
if (model=='Retina'):
nest.SetStatus(rec,{'record_from': ['rate']})
tgts = [nd for nd in nest.GetLeaves(population)[0] if nest.GetStatus([nd], 'model')[0]==model]
#nest.Connect(rec, tgts, None, {'delay': recorders_delay})
nest.Connect(rec, tgts)
print('done.')
#! =================
#! Spike detector
#! =================
print('Connecting detectors...', end="")
detectors = []
'''
for population, model in [(Retina_layer, 'Retina'),
(Tp_layer , 'Tp_exc'),
(Tp_layer , 'Tp_inh'),
(Rp_layer , 'Rp'),
(Vp_vertical, 'L23_exc'),
(Vp_horizontal, 'L23_exc'),
(Vp_vertical, 'L23_inh'),
(Vp_horizontal, 'L23_inh'),
(Vp_vertical, 'L4_exc'),
(Vp_horizontal, 'L4_exc'),
(Vp_vertical, 'L4_inh'),
(Vp_horizontal, 'L4_inh'),
(Vp_vertical, 'L56_exc'),
(Vp_horizontal, 'L56_exc'),
(Vp_vertical, 'L56_inh'),
(Vp_horizontal, 'L56_inh'),
(Vs_vertical, 'L23_exc'),
(Vs_horizontal, 'L23_exc'),
(Vs_vertical, 'L23_inh'),
(Vs_horizontal, 'L23_inh'),
(Vs_vertical, 'L4_exc'),
(Vs_horizontal, 'L4_exc'),
(Vs_vertical, 'L4_inh'),
(Vs_horizontal, 'L4_inh'),
(Vs_vertical, 'L56_exc'),
(Vs_horizontal, 'L56_exc'),
(Vs_vertical, 'L56_inh'),
(Vs_horizontal, 'L56_inh')]:
'''
'''
for population, model in [(Retina_layer, 'Retina'),
(Tp_layer , 'Tp_exc'),
(Tp_layer , 'Tp_inh'),
(Vp_vertical, 'L23_exc'),
(Vp_horizontal, 'L23_exc'),
(Vp_vertical, 'L4_exc'),
(Vp_horizontal, 'L4_exc')]:
'''
#Tom
for population, model in [(Retina_layer, 'Retina'),
# (Tp_layer, 'Tp_exc'),
# (Tp_layer, 'Tp_inh'),
(Vp_vertical, 'L23_exc'),
(Vp_vertical, 'L4_exc'),
(Vp_vertical, 'L56_exc'),
(Vp_vertical, 'L23_inh'),
(Vp_vertical, 'L4_inh'),
(Vp_vertical, 'L56_inh'),
(Vs_vertical, 'L23_exc'),
(Vs_vertical, 'L23_inh'),
(Vs_vertical, 'L4_exc'),
(Vs_vertical, 'L4_inh'),
(Vs_vertical, 'L56_exc'),
(Vs_vertical, 'L56_inh')]:
print('.', end="")
rec = nest.Create('spike_detector', params={"withgid": True, "withtime": True})
#rec = nest.Create('spike_detector')
detectors.append([rec,population,model])
tgts = [nd for nd in nest.GetLeaves(population)[0] if nest.GetStatus([nd], 'model')[0]==model]
if model == 'Retina':
for t in tgts:
try:
# nest.Connect([t], rec, None, {'delay': recorders_delay})
nest.Connect([t], rec)
print('connected %d' % t)
except:
print('%d did not work' % t)
else:
# nest.Connect(tgts, rec, None, {'delay': recorders_delay})
nest.Connect(tgts, rec)
print('done.')
#! ====================
#! Simulation
#! ====================
'''
# change gKL to 0.8 in all populations (necessary to get a little stronger evoked response)
for l in layers:
sim_elements = l[1]['elements']
for m in np.arange(0,np.size(sim_elements),1):
if(np.size(sim_elements)==1):
sim_model = sim_elements
else:
sim_model = sim_elements[m]
exec("la = %s" % l[0])
pop = [nd for nd in nest.GetLeaves(la)[0] if nest.GetStatus([nd], 'model')[0]==sim_model]
if (l[0]!='Retina_layer'):
for cell in pop:
nest.SetStatus([cell], {'g_KL':0.8})
'''
# Simulate
for t in Params['intervals']:
#if (t == 250.0): # Stimulus ON
# # if (t == 1500.0): # Stimulus ON
# nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 45.0})
#else: # Stimulus OFF
# nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 0.0})
if Params['input_flag']==True:
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': Params['ret_rate']})
else:
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 0.0})
nest.Simulate(t)
#! ====================
#! Plot Results
#! ====================
print("Creating figure 3...")
rows = 9
cols = 2
fig = plt.figure(num=None, figsize=(13, 24), dpi=100)
fig.subplots_adjust(hspace=0.4)
# Plot A: membrane potential rasters
recorded_models = [(Retina_layer,'Retina'),
(Vp_vertical,'L23_exc'),
(Vp_vertical,'L4_exc'),
(Vp_vertical,'L56_exc'),
(Vs_vertical,'L23_exc'),
(Vs_vertical,'L4_exc')]
#plotting.potential_raster(fig,recorders,recorded_models,0,Params['Np'],np.sum(Params['intervals']),Params['resolution'],rows,cols,0)
plotting.potential_raster(fig,recorders,recorded_models,0,Params['Np'],
np.sum(Params['intervals']),
Params['resolution'],
rows,cols,0)
#starting_neuron = 800+1
#plotting.potential_raster(fig,recorders,recorded_models,starting_neuron,Params['Np'],np.sum(Params['intervals']),Params['resolution'],rows,cols,0)
plt.title('Evoked')
# Plot B: individual intracellular traces
recorded_models =[(Vp_vertical,'L4_exc'),
(Vp_vertical,'L4_inh')]
#plotting.intracellular_potentials(fig, recorders, recorded_models, 21, rows, cols, 6) #original
# keiko
total_time = 0.0
for t in Params['intervals']:
total_time += t
#draw_neuron = (Params['Np']*Params['Np']/2)
#plotting.intracellular_potentials(fig, recorders, recorded_models, draw_neuron, rows, cols, 6, total_time)
plotting.intracellular_potentials(fig, recorders, recorded_models, 21, rows, cols, 6, total_time)
#plotting.intracellular_potentials(fig, recorders, recorded_models, 820, rows, cols, 6, total_time)
# Plot C: topographical activity of the vertical and horizontal layers
if Params.has_key('start_membrane_potential') and Params.has_key('end_membrane_potential'):
start = Params['start_membrane_potential']
stop = Params['end_membrane_potential']
else:
start = 130.0
stop = 140.0
recorded_models = [(Vp_vertical,'L23_exc')]
labels = ["Vertical"]
plotting.topographic_representation(fig,
recorders,
recorded_models,
labels,
Params['Np'],
np.sum(Params['intervals']),
Params['resolution'],
rows,
cols,
start,
stop,
8,
0)
recorded_models = [(Vp_horizontal,'L23_exc')]
labels = ["Horizontal"]
plotting.topographic_representation(fig,recorders,recorded_models,labels,Params['Np'],np.sum(Params['intervals']),Params['resolution'],rows,cols,start,stop,8,1)
fig.savefig(data_folder + '/figure3.png', dpi=100)
if Params.has_key('show_main_figure') and Params['show_main_figure']:
plt.show()
# Plot D: movie
#labels = ["Evoked_Vp_L23_Vertical","Evoked_Vp_L23_Horizontal"]
#recorded_models = [(Vp_vertical,'L23_exc'),(Vp_horizontal,'L23_exc')]
#plotting.makeMovie(fig,recorders,recorded_models,labels,Params['Np'],np.sum(Params['intervals']),Params['resolution'])
#! ====================
#! Save Results
#! ====================
print('save recorders data')
# Set folder
#rootdir = '/home/kfujii2/newNEST2/ht_model_pablo_based/data/'
#expdir = 'random/'
# expdir = 'random_full/'
# expdir = 'structured_full/'
# data_folder = rootdir + expdir
# To save spike data, set pairs of population id and its name
population_name = [ {'population': Retina_layer, 'name': 'Retina'},
{'population': Vp_vertical, 'name': 'Vp_v'},
{'population': Vp_horizontal, 'name': 'Vp_h'},
# {'population': Rp_layer, 'name': 'Rp'},
# {'population': Tp_layer, 'name': 'Tp'},
{'population': Vs_vertical, 'name': 'Vs_v'},
{'population': Vs_horizontal, 'name': 'Vs_h'}]
if Params.has_key('save_recorders') and Params['save_recorders']:
for rec, population, model in recorders:
# Get name of population
for p in range(0, len(population_name), 1):
if population_name[p]['population'] == population:
p_name = population_name[p]['name']
data = nest.GetStatus(rec)[0]['events']
if model == 'Retina':
scipy.io.savemat(data_folder + '/recorder_' + p_name + '_' + model + '.mat',
mdict={'senders': data['senders'],
'rate': data['rate']})
else:
scipy.io.savemat(data_folder + '/recorder_' + p_name + '_' + model + '.mat',
mdict={'senders': data['senders'],
'times': data['times'],
'V_m': data['V_m'],
#'I_syn_AMPA': data['I_syn_AMPA'],
#'I_syn_NMDA': data['I_syn_NMDA'],
#'I_syn_GABA_A': data['I_syn_GABA_A'],
#'I_syn_GABA_B': data['I_syn_GABA_B'],
# 'g_AMPA': data['g_AMPA'],
# 'g_NMDA': data['g_NMDA'],
# 'g_GABA_A': data['g_GABA_A'],
# 'g_GABA_B': data['g_GABA_B']
} )
print('save raster images')
plt.close()
for rec, population, model in detectors:
spikes = nest.GetStatus(rec, 'events')[0]
# Get name of population
for p in range(0, len(population_name), 1):
if population_name[p]['population'] == population:
p_name = population_name[p]['name']
if len(nest.GetStatus(rec)[0]['events']['senders']) > 3:
raster = raster_plot.from_device(rec, hist=True)
pylab.title( p_name + '_' + model )
f = raster[0].figure
f.set_size_inches(15, 9)
f.savefig(data_folder + '/spikes_' + p_name + '_' + model + '.png', dpi=100)
plt.close()
# Set filename and save spike data
filename = data_folder + '/spike_' + p_name + '_' + model + '.pickle'
pickle.dump(spikes, open(filename, 'w'))
scipy.io.savemat(data_folder + '/spike_' + p_name + '_' + model + '.mat', mdict={'senders': spikes['senders'], 'times': spikes['times']})
'''
filename_AMPA = data_folder + 'connection_' + p_name + '_AMPA_syn' + '.dat'
filename_NMDA = data_folder + 'connection_' + p_name + '_NMDA_syn' + '.dat'
filename_GABAA = data_folder + 'connection_' + p_name + '_GABA_A_syn' + '.dat'
filename_GABAB = data_folder + 'connection_' + p_name + '_GABA_B_syn' + '.dat'
tp.DumpLayerConnections(population, 'AMPA_syn', filename_AMPA)
tp.DumpLayerConnections(population, 'NMDA_syn', filename_NMDA)
tp.DumpLayerConnections(population, 'GABA_A_syn', filename_GABAA)
tp.DumpLayerConnections(population, 'GABA_B_syn', filename_GABAB)
'''
'''
for p in range(0, len(population_name), 1):
population = population_name[p]['population']
p_name = population_name[p]['name']
filename_nodes = data_folder + '/gid_' + p_name + '.dat'
tp.DumpLayerNodes(population, filename_nodes)
'''
network_script = Params['network'] + '.py'
shutil.copy2(network_script, data_folder + '/' + network_script)
shutil.copy2(network_script, conn_folder + '/' + network_script)
print('end')
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)
},
'kernel': 0.5,
'weights': {
'uniform': {
'min': 0.5,
'max': 2.0
}
},
'delays': 1.0
}
topo.ConnectLayers(a, b, conndict)
# plot targets of neurons in different grid locations
fig, ax = pylab.subplots(1)
# plot targets of two source neurons into same figure, with mask
for src_pos in [[15, 10], [0, 0]]:
# obtain node id for center
src = topo.GetElement(a, src_pos)
topo.PlotTargets(src, b, mask=conndict['mask'], fig=pylab.gcf())
# beautify
ax.set_xticks(pylab.arange(-1.5, 1.55, 0.5))
ax.set_yticks(pylab.arange(-1.5, 1.55, 0.5))
ax.grid(True)
ax.axis([-2.0, 2.0, -2.0, 2.0])
ax.set_aspect('equal', 'box')
ax.set_title('Connection targets')
pylab.savefig('figs/connex.png')
'weights': 1.0,
'delays': 1.0}
topo.ConnectLayers(a, b, conndict)
# plot targets of neurons in different grid locations
# first, clear existing figure, get current figure
pylab.clf()
fig = pylab.gcf()
# plot targets of two source neurons into same figure, with mask
# use different colors
for src_pos, color in [([15,15], 'blue'), ([0,0], 'green')]:
# obtain node id for center
src = topo.GetElement(a, src_pos)
topo.PlotTargets(src, b, mask=conndict['mask'], kernel=conndict['kernel'],
src_color=color, tgt_color=color, mask_color=color,
kernel_color=color, src_size=100,
fig=fig)
# beautify
pylab.axes().set_xticks(pylab.arange(-1.5, 1.55, 0.5))
pylab.axes().set_yticks(pylab.arange(-1.5, 1.55, 0.5))
pylab.grid(True)
pylab.axis([-2.0, 2.0, -2.0, 2.0])
pylab.axes().set_aspect('equal', 'box')
pylab.title('Connection targets, Gaussian kernel')
#pylab.savefig('gaussex.pdf')
def simulation(Params):
#! =================
#! Import network
#! =================
# NEST Kernel and Network settings
nest.ResetKernel()
nest.ResetNetwork()
nest.SetKernelStatus({"local_num_threads": Params['threads'],'resolution': Params['resolution']})
nest.SetStatus([0],{'print_time': True})
# initialize random seed
import time
msd = int(round(time.time() * 1000))
nest.SetKernelStatus({'grng_seed' : msd})
nest.SetKernelStatus({'rng_seeds' : range(msd+Params['threads']+1, msd+2*Params['threads']+1)})
import importlib
network = importlib.import_module(Params['network'])
reload(network)
models, layers, conns = network.get_Network(Params)
#import network_full_keiko
#reload(network_full_keiko)
# models, layers, conns = network_full_keiko.get_Network(Params)
# Create models
for m in models:
nest.CopyModel(m[0], m[1], m[2])
# Create layers, store layer info in Python variable
for l in layers:
exec '%s = tp.CreateLayer(l[1])' % l[0]
# Create connections, need to insert variable names
for c in conns:
eval('tp.ConnectLayers(%s,%s,c[2])' % (c[0], c[1]))
# Prepare for file IO
import glob
# --- Set folder information
data_folder = Params['data_folder']
if not os.path.isdir(data_folder):
os.makedirs(data_folder)
# --- To save spike data, set pairs of population id and its name
population_name = [ {'population': Retina_layer, 'name': 'Retina'},
{'population': Vp_vertical, 'name': 'Vp_v'},
{'population': Vp_horizontal, 'name': 'Vp_h'},
{'population': Rp_layer, 'name': 'Rp'},
{'population': Tp_layer, 'name': 'Tp'},
{'population': Vs_vertical, 'name': 'Vs_v'},
{'population': Vs_horizontal, 'name': 'Vs_h'}]
if Params.has_key('load_connections_from_file') and Params['load_connections_from_file']:
# Preparation
scramble_populations = [(Vp_vertical, 'Vp_vertical'),
(Vp_horizontal, 'Vp_horizontal')]
scramble_layers = ['L23_exc',
'L23_inh',
'L4_exc',
'L4_inh',
'L56_exc',
'L56_inh']
#scramble_layers = ['L4_exc']
# Get min &max index of each layer
h_min_idx = {}
h_max_idx = {}
v_min_idx = {}
v_max_idx = {}
target_neurons = []
for tmp_model in scramble_layers:
tmp_h = nest.GetLeaves(Vp_horizontal, properties={'model': tmp_model}, local_only=True)[0]
tmp_v = nest.GetLeaves(Vp_vertical, properties={'model': tmp_model}, local_only=True)[0]
h_min_idx[tmp_model] = min(tmp_h)
h_max_idx[tmp_model] = max(tmp_h)
v_min_idx[tmp_model] = min(tmp_v)
v_max_idx[tmp_model] = max(tmp_v)
target_neurons = target_neurons + range(h_min_idx[tmp_model], h_max_idx[tmp_model]+1) + range(v_min_idx[tmp_model], v_max_idx[tmp_model]+1)
# Save intact network information
for p in range(0, len(population_name), 1):
population = population_name[p]['population']
p_name = population_name[p]['name']
filename_AMPA = data_folder + 'connection_' + p_name + '_AMPA_syn' + '_intact.dat'
filename_NMDA = data_folder + 'connection_' + p_name + '_NMDA_syn' + '_intact.dat'
filename_GABAA = data_folder + 'connection_' + p_name + '_GABA_A_syn' + '_intact.dat'
filename_GABAB = data_folder + 'connection_' + p_name + '_GABA_B_syn' + '_intact.dat'
tp.DumpLayerConnections(population, 'AMPA_syn', filename_AMPA)
tp.DumpLayerConnections(population, 'NMDA_syn', filename_NMDA)
tp.DumpLayerConnections(population, 'GABA_A_syn', filename_GABAA)
tp.DumpLayerConnections(population, 'GABA_B_syn', filename_GABAB)
# Reset network
nest.ResetNetwork()
'''
# The following code works, but takes time longer
# Get the list of connection_file
file_list = glob.glob(data_folder+'connection_*_intact.dat')
# Remove a file if the size is zero
remove_idx = []
for file_idx in range(0,len(file_list)):
intact_filename = file_list[file_idx]
fsize=os.path.getsize(intact_filename)
if fsize == 0:
remove_idx = remove_idx + [file_idx]
remove_idx.sort()
remove_idx.reverse()
for i in remove_idx:
del file_list[i]
'''
# TODO : put GABA_A, GABA_B and NMDA connection files
file_list = []
file_list.append({'filename': data_folder + 'connection_Vp_h_AMPA_syn_intact.dat',
'synapse': 'AMPA'})
file_list.append({'filename': data_folder + 'connection_Vp_v_AMPA_syn_intact.dat',
'synapse': 'AMPA'})
# Do the following procedure for all connection files
for file_idx in range(0,len(file_list)):
# Set filenames
intact_filename = file_list[file_idx]['filename']
receptors = nest.GetDefaults('ht_neuron')['receptor_types']
syn_model = receptors[ file_list[file_idx]['synapse'] ]
scrambled_filename = intact_filename.replace('intact', 'scrambled')
print(intact_filename)
# Get original(intact) connectivity data
src_network = np.loadtxt(open(intact_filename,'rb'))
np_pre = src_network[:, 0].astype(int)
np_post = src_network[:, 1].astype(int)
np_w = src_network[:, 2]
np_d = src_network[:, 3]
if Params['scrambled']:
# Preserve the original structure if
# -- pre neruons are not in target populations (i.e. scramble_layers)
# -- OR
# -- post neurons are not in target populations(i.e. scramble_layers)
preserved_rows = np.where( ~np.in1d(np_pre,target_neurons) | ~np.in1d(np_post,target_neurons) )[0]
preserved_pre = np_pre[preserved_rows]
preserved_post = np_post[preserved_rows]
preserved_w = np_w[preserved_rows]
preserved_d = np_d[preserved_rows]
# If len(preserved_rows)==len(np_pre), that means all neurons do not belong to scramble target areas.
# If len(preserved_rows) < len(np_pre), that means there are some neurons which need to be scrambled.
if len(preserved_rows) > len(np_pre):
print('ERROR: preserved_rows should not be larger than np_pre')
elif len(preserved_rows) == len(np_pre):
scrambled_pre = preserved_pre.tolist()
scrambled_post = preserved_post.tolist()
scrambled_w = preserved_w.tolist()
scrambled_d = preserved_d.tolist()
else: # --- len(preserved_rows) < len(np_pre)
scrambled_pre = []
scrambled_post = []
scrambled_w = []
scrambled_d = []
for tmp_model_pre in scramble_layers:
for tmp_model_post in scramble_layers:
# Get row index such that
# --- pre_neuron is in "tmp_model_pre"
# --- AND
# --- post_neuron is in "tmp_model_post"
bool_pre_h = np.in1d(np_pre, range(h_min_idx[tmp_model_pre], h_max_idx[tmp_model_pre]+1))
bool_pre_v = np.in1d(np_pre, range(v_min_idx[tmp_model_pre], v_max_idx[tmp_model_pre]+1))
bool_post_h = np.in1d(np_post, range(h_min_idx[tmp_model_post], h_max_idx[tmp_model_post]+1))
bool_post_v = np.in1d(np_post, range(v_min_idx[tmp_model_post], v_max_idx[tmp_model_post]+1))
tmp_rows_pre_h = np.where(bool_pre_h & (bool_post_h|bool_post_v))[0]
tmp_rows_pre_v = np.where(bool_pre_v & (bool_post_h|bool_post_v))[0]
# Get connectivity data such that pre neuron is in "tmp_model_pre"
# --- pre, w and d information should be kept.
# --- post index should be scrambled.
tmp_pre = np_pre[np.append(tmp_rows_pre_h, tmp_rows_pre_v)].tolist()
tmp_w = np_w[np.append(tmp_rows_pre_h, tmp_rows_pre_v)].tolist()
tmp_d = np_d[np.append(tmp_rows_pre_h, tmp_rows_pre_v)].tolist()
tmp_post = []
num_pre_h = len(tmp_rows_pre_h)
num_pre_v = len(tmp_rows_pre_v)
# --- pre : population = horizontal, model = "tmp_model_pre"
# --- post: population = horizontal(1/2)+vertical(1/2), model = "tmp_model_post"
if num_pre_h > 0:
# Assign the same number of connections for horizontal population and vertical population
num_scrambled_h = int(round(num_pre_h / 2))
num_scrambled_v = num_pre_h - num_scrambled_h
# Choose post neuron index randomly
scrambled_h_idx = rd.randint(low =h_min_idx[tmp_model_post],
high=h_max_idx[tmp_model_post],
size=[num_scrambled_h, 1])
scrambled_v_idx = rd.randint(low =v_min_idx[tmp_model_post],
high=v_max_idx[tmp_model_post],
size=[num_scrambled_v, 1])
# append scrambled post index
tmp_post = tmp_post + np.append(scrambled_h_idx, scrambled_v_idx).tolist()
# --- pre : population = vertical, model = "tmp_model_pre"
# --- post: population = horizontal(1/2)+vertical(1/2), model = "tmp_model_post"
if num_pre_v > 0:
# Assign the same number of connections for horizontal population and vertical population
num_scrambled_h = int(round(num_pre_v / 2))
num_scrambled_v = num_pre_v - num_scrambled_h
# Choose post neuron index randomly
scrambled_h_idx = rd.randint(low =h_min_idx[tmp_model_post],
high=h_max_idx[tmp_model_post],
size=[num_scrambled_h, 1])
scrambled_v_idx = rd.randint(low =v_min_idx[tmp_model_post],
high=v_max_idx[tmp_model_post],
size=[num_scrambled_v, 1])
# append scrambled post index
tmp_post = tmp_post + np.append(scrambled_h_idx, scrambled_v_idx).tolist()
scrambled_pre = scrambled_pre + tmp_pre
scrambled_post = scrambled_post + tmp_post
scrambled_w = scrambled_w + tmp_w
scrambled_d = scrambled_d +tmp_d
# append preserved connection data
scrambled_pre = scrambled_pre + preserved_pre.tolist()
scrambled_post = scrambled_post + preserved_post.tolist()
scrambled_w = scrambled_w + preserved_w.tolist()
scrambled_d = scrambled_d + preserved_d.tolist()
# Save scrambled_data
scrambled_all_data = np.zeros([len(scrambled_pre), 4])
scrambled_all_data[:, 0] = scrambled_pre
scrambled_all_data[:, 1] = scrambled_post
scrambled_all_data[:, 2] = scrambled_w
scrambled_all_data[:, 3] = scrambled_d
np.savetxt(scrambled_filename, scrambled_all_data, fmt='%.6f')
# Connect
con_dict = {'rule': 'one_to_one'}
syn_dict = {"model": "ht_synapse",
'receptor_type': syn_model,
'weight': scrambled_w,
'delay': scrambled_d,
}
nest.Connect(scrambled_pre, scrambled_post, con_dict, syn_dict)
else:
# just convert data type(ndarray->list) and connect based on the original data
pre = np_pre.tolist()
post = np_post.tolist()
w = np_w.tolist()
d = np_d.tolist()
# Connect
con_dict = {'rule': 'one_to_one'}
syn_dict = {"model": "ht_synapse",
'receptor_type': 1,
'weight': w,
'delay': d,
}
nest.Connect(pre, post, con_dict, syn_dict)
# nest.DisconnectOneToOne(tp_node, tgt_map[0], {"synapse_model": "AMPA_syn"})
#nest.Disconnect([tp_node], tgt_map, 'one_to_one', {"synapse_model": "AMPA_syn"})
# Get target nodes for the vertical population
# tp_nodes = nest.GetLeaves(Tp_layer, local_only=True)[0]
if Params.has_key('show_V4_num_conn_figure') and Params['show_V4_num_conn_figure']:
horizontal_nodes = nest.GetLeaves(Vp_horizontal, properties={'model': 'L4_exc'}, local_only=True)[0]
vertical_nodes = nest.GetLeaves(Vp_vertical, properties={'model': 'L4_exc'}, local_only=True)[0]
n_conns_hor = []
for (idx, horizontal_node) in enumerate(horizontal_nodes):
tgt_map = []
this_conns = nest.GetConnections([horizontal_node], horizontal_nodes, synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_hor.append(len(tgt_map))
plt.figure()
plt.hist(n_conns_hor, range(0, max(n_conns_hor + [30])))
plt.title('# of connections Vp(h) L4pyr -> Vp(h) L4Pyr')
n_conns_hor = []
for (idx, horizontal_node) in enumerate(horizontal_nodes):
tgt_map = []
this_conns = nest.GetConnections([horizontal_node], vertical_nodes, synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_hor.append(len(tgt_map))
# nest.DisconnectOneToOne(tp_node, tgt_map[0], {"synapse_model": "AMPA_syn"})
#nest.Disconnect([tp_node], tgt_map, 'one_to_one', {"synapse_model": "AMPA_syn"})
plt.figure()
plt.hist(n_conns_hor, range(0, max(n_conns_hor + [30])))
plt.title('# of connections Vp(h) L4pyr -> Vp(v) L4Pyr')
n_conns_ver = []
for (idx, vertical_node) in enumerate(vertical_nodes):
tgt_map = []
this_conns = nest.GetConnections([vertical_node], vertical_nodes, synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_ver.append(len(tgt_map))
plt.figure()
plt.hist(n_conns_ver, range(0, max(n_conns_ver + [30])))
plt.title('# of connections Vp(v) L4pyr -> Vp(v) L4Pyr')
n_conns_ver = []
for (idx, vertical_node) in enumerate(vertical_nodes):
tgt_map = []
this_conns = nest.GetConnections([vertical_node], horizontal_nodes, synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_ver.append(len(tgt_map))
plt.figure()
plt.hist(n_conns_ver, range(0, max(n_conns_ver + [30])))
plt.title('# of connections Vp(v) L4pyr -> Vp(h) L4Pyr')
plt.show()
# Check connections
# Connections from Retina to TpRelay
# tp.PlotTargets(tp.FindCenterElement(Retina_layer), Tp_layer)
if Params.has_key('show_V4_connectivity_figure') and Params['show_V4_connectivity_figure']:
Vp_hor_gids = tp.GetElement(Vp_horizontal, [0,0])
n_Vp_hor = len(Vp_hor_gids)
f = []
for idx in range(n_Vp_hor):
f.append(plt.figure())
positions = range(0,41,10)
positions[-1] -= 1
for xi in range(len(positions)):
for yi in range(len(positions)):
print("Position [%d,%d] : %d" %(xi,yi,yi*(len(positions))+xi+1))
x = positions[xi]
y = positions[yi]
Vp_hor_gids = tp.GetElement(Vp_horizontal, [x,y])
Vp_hor_status = nest.GetStatus(Vp_hor_gids)
for (idx, n) in enumerate(Vp_hor_status):
if n['Tau_theta'] == 2.0:
print idx
try:
f[idx].add_subplot(len(positions), len(positions), yi*(len(positions))+xi+1)
tp.PlotTargets([Vp_hor_gids[idx]], Vp_horizontal, 'L4_exc', 'AMPA_syn', f[idx])
except:
print('%i bad' % Vp_hor_gids[idx])
plt.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()
'''
# pablo
# Create vertical grating
for n in nest.GetLeaves(Retina_layer)[0]:
retina_0 = (nest.GetLeaves(Retina_layer)[0])[0]
col = (n-retina_0)/Params['Np']
cells_per_degree = Params['Np']/Params['visSize']
cells_per_cycle = cells_per_degree/Params['spatial_frequency']
nest.SetStatus([n], { "phase": col * 360/(cells_per_cycle-1) })
'''
### keiko
if Params['lambda_dg'] >= 0:
[nest.SetStatus([n], {"phase": phaseInit(tp.GetPosition([n])[0],
Params["lambda_dg"],
Params["phi_dg"])})
for n in nest.GetLeaves(Retina_layer)[0]]
else:
# Leonardo: Random retina input
[nest.SetStatus([n], {"phase": phaseInit(tp.GetPosition([n])[0],
np.pi * np.random.rand(),
np.pi * np.random.rand())})
for n in nest.GetLeaves(Retina_layer)[0]]
# --------------------------------------------------------------------#
# ---------- SET IB NEURONS ----------------------------------------- #
# --------------------------------------------------------------------#
# 30% of Cortex L56 excitatory neurons are intrinsically bursting(IB) neuron.
# That is achieved by setting pacemaker current I_h.
# So select 30% of L56_exc neuron, and change h_g_peak from 0.0 to 1.0.
# (Other cortical neuron do not have I_h, thus h_g_peak=0.0)
L56_vertical_idx = [nd for nd in nest.GetLeaves(Vp_vertical)[0] if nest.GetStatus([nd], 'model')[0]=='L56_exc']
L56_horizontal_idx = [nd for nd in nest.GetLeaves(Vp_horizontal)[0] if nest.GetStatus([nd], 'model')[0]=='L56_exc']
num_neuron = len(L56_vertical_idx)
num_ib = int(num_neuron*0.3)
ridx_vertical = np.random.randint(num_neuron, size=(1,num_ib))[0]
ridx_horizontal = np.random.randint(num_neuron, size=(1,num_ib))[0]
for i in range(1,num_ib,1):
nest.SetStatus([L56_vertical_idx[ridx_vertical[i]]], {'h_g_peak': 1.0})
nest.SetStatus([L56_horizontal_idx[ridx_horizontal[i]]], {'h_g_peak': 1.0})
# initiate network activity
#nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'rate': Params['ret_rate']})
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'rate': Params['ret_rate']})
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 0.0})
nest.Simulate(500.0)
#! =================
#! Recording devices
#! =================
nest.CopyModel('multimeter', 'RecordingNode',
params = {'interval' : Params['resolution'],
#'record_from': ['V_m'],
'record_from': ['V_m',
'I_syn_AMPA',
'I_syn_NMDA',
'I_syn_GABA_A',
'I_syn_GABA_B',
'g_AMPA',
'g_NMDA',
'g_GABAA',
'g_GABAB',
'I_NaP',
'I_KNa',
'I_T',
'I_h'],
'record_to' : ['memory'],
'withgid' : True,
'withtime' : True})
recorders = []
'''
for population, model in [(Retina_layer, 'Retina'),
(Tp_layer , 'Tp_exc'),
(Tp_layer , 'Tp_inh'),
(Rp_layer , 'Rp'),
(Vp_vertical, 'L23_exc'),
(Vp_horizontal, 'L23_exc'),
(Vp_vertical, 'L23_inh'),
(Vp_horizontal, 'L23_inh'),
(Vp_vertical, 'L4_exc'),
(Vp_horizontal, 'L4_exc'),
(Vp_vertical, 'L4_inh'),
(Vp_horizontal, 'L4_inh'),
(Vp_vertical, 'L56_exc'),
(Vp_horizontal, 'L56_exc'),
(Vp_vertical, 'L56_inh'),
(Vp_horizontal, 'L56_inh'),
(Vs_vertical, 'L23_exc'),
(Vs_horizontal, 'L23_exc'),
(Vs_vertical, 'L23_inh'),
(Vs_horizontal, 'L23_inh'),
(Vs_vertical, 'L4_exc'),
(Vs_horizontal, 'L4_exc'),
(Vs_vertical, 'L4_inh'),
(Vs_horizontal, 'L4_inh'),
(Vs_vertical, 'L56_exc'),
(Vs_horizontal, 'L56_exc'),
(Vs_vertical, 'L56_inh'),
(Vs_horizontal, 'L56_inh')]:
'''
for population, model in [(Retina_layer, 'Retina'),
(Tp_layer , 'Tp_exc'),
(Tp_layer , 'Tp_inh'),
(Vp_vertical, 'L4_exc'),
(Vp_vertical, 'L4_inh'),
(Vp_horizontal, 'L4_exc'),
(Vp_vertical, 'L23_exc'),
(Vp_horizontal, 'L23_exc'),
(Vp_vertical, 'L56_exc'),
(Rp_layer, 'Rp')]:
rec = nest.Create('RecordingNode')
recorders.append([rec,population,model])
if (model=='Retina'):
nest.SetStatus(rec,{'record_from': ['rate']})
tgts = [nd for nd in nest.GetLeaves(population)[0] if nest.GetStatus([nd], 'model')[0]==model]
nest.Connect(rec, tgts)
#! =================
#! Spike detector
#! =================
detectors = []
'''
for population, model in [(Retina_layer, 'Retina'),
(Tp_layer , 'Tp_exc'),
(Tp_layer , 'Tp_inh'),
(Rp_layer , 'Rp'),
(Vp_vertical, 'L23_exc'),
(Vp_horizontal, 'L23_exc'),
(Vp_vertical, 'L23_inh'),
(Vp_horizontal, 'L23_inh'),
(Vp_vertical, 'L4_exc'),
(Vp_horizontal, 'L4_exc'),
(Vp_vertical, 'L4_inh'),
(Vp_horizontal, 'L4_inh'),
(Vp_vertical, 'L56_exc'),
(Vp_horizontal, 'L56_exc'),
(Vp_vertical, 'L56_inh'),
(Vp_horizontal, 'L56_inh'),
(Vs_vertical, 'L23_exc'),
(Vs_horizontal, 'L23_exc'),
(Vs_vertical, 'L23_inh'),
(Vs_horizontal, 'L23_inh'),
(Vs_vertical, 'L4_exc'),
(Vs_horizontal, 'L4_exc'),
(Vs_vertical, 'L4_inh'),
(Vs_horizontal, 'L4_inh'),
(Vs_vertical, 'L56_exc'),
(Vs_horizontal, 'L56_exc'),
(Vs_vertical, 'L56_inh'),
(Vs_horizontal, 'L56_inh')]:
'''
for population, model in [(Retina_layer, 'Retina'),
(Tp_layer , 'Tp_exc'),
(Tp_layer , 'Tp_inh'),
(Vp_vertical, 'L4_exc'),
(Vp_horizontal, 'L4_exc')]:
rec = nest.Create('spike_detector', params={"withgid": True, "withtime": True})
#rec = nest.Create('spike_detector')
detectors.append([rec,population,model])
tgts = [nd for nd in nest.GetLeaves(population)[0] if nest.GetStatus([nd], 'model')[0]==model]
if model == 'Retina':
for t in tgts:
try:
nest.Connect([t], rec)
print('connected %d' % t)
except:
print('%d did not work' % t)
else:
nest.Connect(tgts, rec)
#! ====================
#! Simulation
#! ====================
'''
# change gKL to 0.8 in all populations (necessary to get a little stronger evoked response)
for l in layers:
sim_elements = l[1]['elements']
for m in np.arange(0,np.size(sim_elements),1):
if(np.size(sim_elements)==1):
sim_model = sim_elements
else:
sim_model = sim_elements[m]
exec("la = %s" % l[0])
pop = [nd for nd in nest.GetLeaves(la)[0] if nest.GetStatus([nd], 'model')[0]==sim_model]
if (l[0]!='Retina_layer'):
for cell in pop:
nest.SetStatus([cell], {'g_KL':0.8})
'''
# Simulate
for t in Params['intervals']:
#if (t == 250.0): # Stimulus ON
# # if (t == 1500.0): # Stimulus ON
# nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 45.0})
#else: # Stimulus OFF
# nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 0.0})
if Params['input_flag']==True:
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': Params['ret_rate']})
else:
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 0.0})
nest.Simulate(t)
'''
data_folder = Params['data_folder']
if not os.path.isdir(data_folder):
os.makedirs(data_folder)
'''
#! ====================
#! Plot Results
#! ====================
if Params.has_key('show_main_figure') and Params['show_main_figure']:
print "plotting..."
rows = 9
cols = 2
fig = plt.figure(num=None, figsize=(13, 24), dpi=100)
fig.subplots_adjust(hspace=0.4)
# Plot A: membrane potential rasters
recorded_models = [(Retina_layer,'Retina'),
(Vp_vertical,'L23_exc'),
(Vp_vertical,'L4_exc'),
(Vp_vertical,'L56_exc'),
(Rp_layer,'Rp'),
(Tp_layer,'Tp_exc')]
#plotting.potential_raster(fig,recorders,recorded_models,0,Params['Np'],np.sum(Params['intervals']),Params['resolution'],rows,cols,0)
plotting.potential_raster(fig,recorders,recorded_models,0,Params['Np'],np.sum(Params['intervals']),Params['resolution'],rows,cols,0)
#starting_neuron = 800+1
#plotting.potential_raster(fig,recorders,recorded_models,starting_neuron,Params['Np'],np.sum(Params['intervals']),Params['resolution'],rows,cols,0)
plt.title('Evoked')
# Plot B: individual intracellular traces
recorded_models =[(Vp_vertical,'L4_exc'),
(Vp_vertical,'L4_inh')]
#plotting.intracellular_potentials(fig, recorders, recorded_models, 21, rows, cols, 6) #original
# keiko
total_time = 0.0
for t in Params['intervals']:
total_time += t
#draw_neuron = (Params['Np']*Params['Np']/2)
#plotting.intracellular_potentials(fig, recorders, recorded_models, draw_neuron, rows, cols, 6, total_time)
plotting.intracellular_potentials(fig, recorders, recorded_models, 21, rows, cols, 6, total_time)
#plotting.intracellular_potentials(fig, recorders, recorded_models, 820, rows, cols, 6, total_time)
# Plot C: topographical activity of the vertical and horizontal layers
if Params.has_key('start_membrane_potential') and Params.has_key('end_membrane_potential'):
start = Params['start_membrane_potential']
stop = Params['end_membrane_potential']
else:
start = 130.0
stop = 140.0
recorded_models = [(Vp_vertical,'L23_exc')]
labels = ["Vertical"]
plotting.topographic_representation(fig,
recorders,
recorded_models,
labels,
Params['Np'],
np.sum(Params['intervals']),
Params['resolution'],
rows,
cols,
start,
stop,
8,
0)
recorded_models = [(Vp_horizontal,'L23_exc')]
labels = ["Horizontal"]
plotting.topographic_representation(fig,recorders,recorded_models,labels,Params['Np'],np.sum(Params['intervals']),Params['resolution'],rows,cols,start,stop,8,1)
fig.savefig(data_folder + 'figure3.png', dpi=100)
plt.show()
# Plot D: movie
#labels = ["Evoked_Vp_L23_Vertical","Evoked_Vp_L23_Horizontal"]
#recorded_models = [(Vp_vertical,'L23_exc'),(Vp_horizontal,'L23_exc')]
#plotting.makeMovie(fig,recorders,recorded_models,labels,Params['Np'],np.sum(Params['intervals']),Params['resolution'])
#! ====================
#! Save Results
#! ====================
print('save recorders data')
for rec, population, model in recorders:
# Get name of population
for p in range(0, len(population_name), 1):
if population_name[p]['population'] == population:
p_name = population_name[p]['name']
data = nest.GetStatus(rec)[0]['events']
if model == 'Retina':
scipy.io.savemat(data_folder + '/recorder_' + p_name + '_' + model + '.mat',
mdict={'senders': data['senders'],
'rate': data['rate']})
else:
scipy.io.savemat(data_folder + '/recorder_' + p_name + '_' + model + '.mat',
mdict={'senders': data['senders'],
'V_m': data['V_m'],
'I_syn_AMPA': data['I_syn_AMPA'],
'I_syn_NMDA': data['I_syn_NMDA'],
'I_syn_GABA_A': data['I_syn_GABA_A'],
'I_syn_GABA_B': data['I_syn_GABA_B'],
'g_AMPA': data['g_AMPA'],
'g_NMDA': data['g_NMDA'],
'g_GABAA': data['g_GABAA'],
'g_GABAB': data['g_GABAB']} )
print('save raster images')
plt.close()
for rec, population, model in detectors:
spikes = nest.GetStatus(rec, 'events')[0]
# Get name of population
for p in range(0, len(population_name), 1):
if population_name[p]['population'] == population:
p_name = population_name[p]['name']
if len(nest.GetStatus(rec)[0]['events']['senders']) > 3:
raster = raster_plot.from_device(rec, hist=True)
pylab.title( p_name + '_' + model )
f = raster[0].figure
f.set_size_inches(15, 9)
f.savefig(data_folder + 'spikes_' + p_name + '_' + model + '.png', dpi=100)
plt.close()
# Set filename and save spike data
filename = data_folder + 'spike_' + p_name + '_' + model + '.pickle'
pickle.dump(spikes, open(filename, 'w'))
scipy.io.savemat(data_folder + '/spike_' + p_name + '_' + model + '.mat', mdict={'senders': spikes['senders'], 'times': spikes['times']})
#filename_AMPA = data_folder + 'connection_' + p_name + '_AMPA_syn' + '.dat'
#tp.DumpLayerConnections(population, 'AMPA_syn', filename_AMPA)
'''
filename_AMPA = data_folder + 'connection_' + p_name + '_AMPA_syn' + '.dat'
filename_NMDA = data_folder + 'connection_' + p_name + '_NMDA_syn' + '.dat'
filename_GABAA = data_folder + 'connection_' + p_name + '_GABA_A_syn' + '.dat'
filename_GABAB = data_folder + 'connection_' + p_name + '_GABA_B_syn' + '.dat'
tp.DumpLayerConnections(population, 'AMPA_syn', filename_AMPA)
tp.DumpLayerConnections(population, 'NMDA_syn', filename_NMDA)
tp.DumpLayerConnections(population, 'GABA_A_syn', filename_GABAA)
tp.DumpLayerConnections(population, 'GABA_B_syn', filename_GABAB)
'''
'''
for p in range(0, len(population_name), 1):
population = population_name[p]['population']
p_name = population_name[p]['name']
filename_nodes = data_folder + '/gid_' + p_name + '.dat'
tp.DumpLayerNodes(population, filename_nodes)
'''
network_script = Params['network'] + '.py'
shutil.copy2(network_script, Params['data_folder'] + network_script)
print('end')
def simulation(Params):
#! =================
#! Import network
#! =================
import importlib
import time
#network_names = ['network_full_keiko', 'network_full_leonardo']
network_names = ['network_full_keiko', 'network_full_keiko']
colors = [[0, 0, 1, .5], [0, 1, 0, .5]]
fig, axs = plt.subplots(2, 2)
for (inet, netname) in enumerate(network_names):
# NEST Kernel and Network settings
nest.ResetKernel()
nest.ResetNetwork()
nest.SetKernelStatus({
"local_num_threads": Params['threads'],
'resolution': Params['resolution']
})
nest.SetStatus([0], {'print_time': True})
# initialize random seed
msd = int(round(time.time() * 1000))
nest.SetKernelStatus({'grng_seed': msd})
nest.SetKernelStatus({
'rng_seeds':
range(msd + Params['threads'] + 1, msd + 2 * Params['threads'] + 1)
})
network = importlib.import_module(netname)
reload(network)
models, layers, conns = network.get_Network(Params)
# import network_full_keiko
# reload(network_full_keiko)
# models, layers, conns = network_full_keiko.get_Network(Params)
# Create models
for m in models:
nest.CopyModel(m[0], m[1], m[2])
# Create layers, store layer info in Python variable
for l in layers:
exec '%s = tp.CreateLayer(l[1])' % l[0]
# Create connections, need to insert variable names
for c in conns:
eval('tp.ConnectLayers(%s,%s,c[2])' % (c[0], c[1]))
if Params.has_key('show_V4_num_conn_figure'
) and Params['show_V4_num_conn_figure']:
horizontal_nodes = nest.GetLeaves(Vp_horizontal,
properties={'model': 'L4_exc'},
local_only=True)[0]
vertical_nodes = nest.GetLeaves(Vp_vertical,
properties={'model': 'L4_exc'},
local_only=True)[0]
print('Ploting #1')
n_conns_hor = []
for (idx, horizontal_node) in enumerate(horizontal_nodes):
tgt_map = []
this_conns = nest.GetConnections([horizontal_node],
horizontal_nodes,
synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_hor.append(len(tgt_map))
plt.axes(axs[0, 0])
plt.hist(n_conns_hor,
range(0, max(n_conns_hor + [30])),
fc=colors[inet])
plt.title('# of connections Vp(h) L4pyr -> Vp(h) L4Pyr')
print('Ploting #2')
n_conns_hor = []
for (idx, horizontal_node) in enumerate(horizontal_nodes):
tgt_map = []
this_conns = nest.GetConnections([horizontal_node],
vertical_nodes,
synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_hor.append(len(tgt_map))
# nest.DisconnectOneToOne(tp_node, tgt_map[0], {"synapse_model": "AMPA_syn"})
#nest.Disconnect([tp_node], tgt_map, 'one_to_one', {"synapse_model": "AMPA_syn"})
plt.axes(axs[0, 1])
plt.hist(n_conns_hor,
range(0, max(n_conns_hor + [30])),
fc=colors[inet])
plt.title('# of connections Vp(h) L4pyr -> Vp(v) L4Pyr')
print('Ploting #3')
n_conns_ver = []
for (idx, vertical_node) in enumerate(vertical_nodes):
tgt_map = []
this_conns = nest.GetConnections([vertical_node],
vertical_nodes,
synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_ver.append(len(tgt_map))
plt.axes(axs[1, 1])
plt.hist(n_conns_ver,
range(0, max(n_conns_ver + [30])),
fc=colors[inet])
plt.title('# of connections Vp(v) L4pyr -> Vp(v) L4Pyr')
print('Ploting #4')
n_conns_ver = []
for (idx, vertical_node) in enumerate(vertical_nodes):
tgt_map = []
this_conns = nest.GetConnections([vertical_node],
horizontal_nodes,
synapse_model='AMPA_syn')
tgt_map.extend([conn[1] for conn in this_conns])
n_conns_ver.append(len(tgt_map))
plt.axes(axs[1, 0])
plt.hist(n_conns_ver,
range(0, max(n_conns_ver + [30])),
fc=colors[inet])
plt.title('# of connections Vp(v) L4pyr -> Vp(h) L4Pyr')
plt.show()
# Check connections
# Connections from Retina to TpRelay
# tp.PlotTargets(tp.FindCenterElement(Retina_layer), Tp_layer)
if Params.has_key('show_V4_connectivity_figure'
) and Params['show_V4_connectivity_figure']:
Vp_hor_gids = tp.GetElement(Vp_horizontal, [0, 0])
n_Vp_hor = len(Vp_hor_gids)
f = []
for idx in range(n_Vp_hor):
f.append(plt.figure())
positions = range(0, 41, 10)
positions[-1] -= 1
for xi in range(len(positions)):
for yi in range(len(positions)):
print("Position [%d,%d] : %d" % (xi, yi, yi *
(len(positions)) + xi + 1))
x = positions[xi]
y = positions[yi]
Vp_hor_gids = tp.GetElement(Vp_horizontal, [x, y])
Vp_hor_status = nest.GetStatus(Vp_hor_gids)
for (idx, n) in enumerate(Vp_hor_status):
if n['Tau_theta'] == 2.0:
print idx
try:
f[idx].add_subplot(len(positions), len(positions),
yi * (len(positions)) + xi + 1)
tp.PlotTargets([Vp_hor_gids[idx]], Vp_horizontal,
'L4_exc', 'AMPA_syn', f[idx])
except:
print('%i bad' % Vp_hor_gids[idx])
plt.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()
'''
# pablo
# Create vertical grating
for n in nest.GetLeaves(Retina_layer)[0]:
retina_0 = (nest.GetLeaves(Retina_layer)[0])[0]
col = (n-retina_0)/Params['Np']
cells_per_degree = Params['Np']/Params['visSize']
cells_per_cycle = cells_per_degree/Params['spatial_frequency']
nest.SetStatus([n], { "phase": col * 360/(cells_per_cycle-1) })
'''
### keiko
if Params['lambda_dg'] >= 0:
[
nest.SetStatus(
[n], {
"phase":
phaseInit(
tp.GetPosition([n])[0], Params["lambda_dg"],
Params["phi_dg"])
}) for n in nest.GetLeaves(Retina_layer)[0]
]
else:
# Leonardo: Random retina input
[
nest.SetStatus(
[n], {
"phase":
phaseInit(
tp.GetPosition([n])[0], np.pi * np.random.rand(),
np.pi * np.random.rand())
}) for n in nest.GetLeaves(Retina_layer)[0]
]
# --------------------------------------------------------------------#
# ---------- SET IB NEURONS ----------------------------------------- #
# --------------------------------------------------------------------#
# 30% of Cortex L56 excitatory neurons are intrinsically bursting(IB) neuron.
# That is achieved by setting pacemaker current I_h.
# So select 30% of L56_exc neuron, and change h_g_peak from 0.0 to 1.0.
# (Other cortical neuron do not have I_h, thus h_g_peak=0.0)
L56_vertical_idx = [
nd for nd in nest.GetLeaves(Vp_vertical)[0]
if nest.GetStatus([nd], 'model')[0] == 'L56_exc'
]
L56_horizontal_idx = [
nd for nd in nest.GetLeaves(Vp_horizontal)[0]
if nest.GetStatus([nd], 'model')[0] == 'L56_exc'
]
num_neuron = len(L56_vertical_idx)
num_ib = int(num_neuron * 0.3)
ridx_vertical = np.random.randint(num_neuron, size=(1, num_ib))[0]
ridx_horizontal = np.random.randint(num_neuron, size=(1, num_ib))[0]
for i in range(1, num_ib, 1):
nest.SetStatus([L56_vertical_idx[ridx_vertical[i]]], {'h_g_peak': 1.0})
nest.SetStatus([L56_horizontal_idx[ridx_horizontal[i]]],
{'h_g_peak': 1.0})
# initiate network activity
#nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'rate': Params['ret_rate']})
nest.SetStatus(
nest.GetLeaves(Retina_layer)[0], {'rate': Params['ret_rate']})
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 0.0})
nest.Simulate(500.0)
#! =================
#! Recording devices
#! =================
nest.CopyModel(
'multimeter',
'RecordingNode',
params={
'interval':
Params['resolution'],
#'record_from': ['V_m'],
'record_from': [
'V_m', 'I_syn_AMPA', 'I_syn_NMDA', 'I_syn_GABA_A',
'I_syn_GABA_B', 'g_AMPA', 'g_NMDA', 'g_GABAA', 'g_GABAB',
'I_NaP', 'I_KNa', 'I_T', 'I_h'
],
'record_to': ['memory'],
'withgid':
True,
'withtime':
True
})
recorders = []
'''
for population, model in [(Retina_layer, 'Retina'),
(Tp_layer , 'Tp_exc'),
(Tp_layer , 'Tp_inh'),
(Rp_layer , 'Rp'),
(Vp_vertical, 'L23_exc'),
(Vp_horizontal, 'L23_exc'),
(Vp_vertical, 'L23_inh'),
(Vp_horizontal, 'L23_inh'),
(Vp_vertical, 'L4_exc'),
(Vp_horizontal, 'L4_exc'),
(Vp_vertical, 'L4_inh'),
(Vp_horizontal, 'L4_inh'),
(Vp_vertical, 'L56_exc'),
(Vp_horizontal, 'L56_exc'),
(Vp_vertical, 'L56_inh'),
(Vp_horizontal, 'L56_inh'),
(Vs_vertical, 'L23_exc'),
(Vs_horizontal, 'L23_exc'),
(Vs_vertical, 'L23_inh'),
(Vs_horizontal, 'L23_inh'),
(Vs_vertical, 'L4_exc'),
(Vs_horizontal, 'L4_exc'),
(Vs_vertical, 'L4_inh'),
(Vs_horizontal, 'L4_inh'),
(Vs_vertical, 'L56_exc'),
(Vs_horizontal, 'L56_exc'),
(Vs_vertical, 'L56_inh'),
(Vs_horizontal, 'L56_inh')]:
'''
for population, model in [(Retina_layer, 'Retina'), (Tp_layer, 'Tp_exc'),
(Tp_layer, 'Tp_inh'), (Vp_vertical, 'L4_exc'),
(Vp_vertical, 'L4_inh'),
(Vp_horizontal, 'L4_exc'),
(Vp_vertical, 'L23_exc'),
(Vp_horizontal, 'L23_exc'),
(Vp_vertical, 'L56_exc'), (Rp_layer, 'Rp')]:
rec = nest.Create('RecordingNode')
recorders.append([rec, population, model])
if (model == 'Retina'):
nest.SetStatus(rec, {'record_from': ['rate']})
tgts = [
nd for nd in nest.GetLeaves(population)[0]
if nest.GetStatus([nd], 'model')[0] == model
]
nest.Connect(rec, tgts)
#! =================
#! Spike detector
#! =================
detectors = []
'''
for population, model in [(Retina_layer, 'Retina'),
(Tp_layer , 'Tp_exc'),
(Tp_layer , 'Tp_inh'),
(Rp_layer , 'Rp'),
(Vp_vertical, 'L23_exc'),
(Vp_horizontal, 'L23_exc'),
(Vp_vertical, 'L23_inh'),
(Vp_horizontal, 'L23_inh'),
(Vp_vertical, 'L4_exc'),
(Vp_horizontal, 'L4_exc'),
(Vp_vertical, 'L4_inh'),
(Vp_horizontal, 'L4_inh'),
(Vp_vertical, 'L56_exc'),
(Vp_horizontal, 'L56_exc'),
(Vp_vertical, 'L56_inh'),
(Vp_horizontal, 'L56_inh'),
(Vs_vertical, 'L23_exc'),
(Vs_horizontal, 'L23_exc'),
(Vs_vertical, 'L23_inh'),
(Vs_horizontal, 'L23_inh'),
(Vs_vertical, 'L4_exc'),
(Vs_horizontal, 'L4_exc'),
(Vs_vertical, 'L4_inh'),
(Vs_horizontal, 'L4_inh'),
(Vs_vertical, 'L56_exc'),
(Vs_horizontal, 'L56_exc'),
(Vs_vertical, 'L56_inh'),
(Vs_horizontal, 'L56_inh')]:
'''
for population, model in [(Retina_layer, 'Retina'), (Tp_layer, 'Tp_exc'),
(Tp_layer, 'Tp_inh'), (Vp_vertical, 'L4_exc'),
(Vp_horizontal, 'L4_exc')]:
rec = nest.Create('spike_detector',
params={
"withgid": True,
"withtime": True
})
#rec = nest.Create('spike_detector')
detectors.append([rec, population, model])
tgts = [
nd for nd in nest.GetLeaves(population)[0]
if nest.GetStatus([nd], 'model')[0] == model
]
if model == 'Retina':
for t in tgts:
try:
nest.Connect([t], rec)
print('connected %d' % t)
except:
print('%d did not work' % t)
else:
nest.Connect(tgts, rec)
#! ====================
#! Simulation
#! ====================
'''
# change gKL to 0.8 in all populations (necessary to get a little stronger evoked response)
for l in layers:
sim_elements = l[1]['elements']
for m in np.arange(0,np.size(sim_elements),1):
if(np.size(sim_elements)==1):
sim_model = sim_elements
else:
sim_model = sim_elements[m]
exec("la = %s" % l[0])
pop = [nd for nd in nest.GetLeaves(la)[0] if nest.GetStatus([nd], 'model')[0]==sim_model]
if (l[0]!='Retina_layer'):
for cell in pop:
nest.SetStatus([cell], {'g_KL':0.8})
'''
# Simulate
for t in Params['intervals']:
#if (t == 250.0): # Stimulus ON
# # if (t == 1500.0): # Stimulus ON
# nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 45.0})
#else: # Stimulus OFF
# nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 0.0})
if Params['input_flag'] == True:
nest.SetStatus(
nest.GetLeaves(Retina_layer)[0],
{'amplitude': Params['ret_rate']})
else:
nest.SetStatus(nest.GetLeaves(Retina_layer)[0], {'amplitude': 0.0})
nest.Simulate(t)
#! ====================
#! Plot Results
#! ====================
if Params.has_key('show_main_figure') and Params['show_main_figure']:
print "plotting..."
rows = 9
cols = 2
fig = plt.figure()
fig.subplots_adjust(hspace=0.4)
# Plot A: membrane potential rasters
recorded_models = [(Retina_layer, 'Retina'), (Vp_vertical, 'L23_exc'),
(Vp_vertical, 'L4_exc'), (Vp_vertical, 'L56_exc'),
(Rp_layer, 'Rp'), (Tp_layer, 'Tp_exc')]
#plotting.potential_raster(fig,recorders,recorded_models,0,Params['Np'],np.sum(Params['intervals']),Params['resolution'],rows,cols,0)
plotting.potential_raster(fig, recorders, recorded_models, 0,
Params['Np'], np.sum(Params['intervals']),
Params['resolution'], rows, cols, 0)
#starting_neuron = 800+1
#plotting.potential_raster(fig,recorders,recorded_models,starting_neuron,Params['Np'],np.sum(Params['intervals']),Params['resolution'],rows,cols,0)
plt.title('Evoked')
# Plot B: individual intracellular traces
recorded_models = [(Vp_vertical, 'L4_exc'), (Vp_vertical, 'L4_inh')]
#plotting.intracellular_potentials(fig, recorders, recorded_models, 21, rows, cols, 6) #original
# keiko
total_time = 0.0
for t in Params['intervals']:
total_time += t
#draw_neuron = (Params['Np']*Params['Np']/2)
#plotting.intracellular_potentials(fig, recorders, recorded_models, draw_neuron, rows, cols, 6, total_time)
plotting.intracellular_potentials(fig, recorders, recorded_models, 21,
rows, cols, 6, total_time)
#plotting.intracellular_potentials(fig, recorders, recorded_models, 820, rows, cols, 6, total_time)
# Plot C: topographical activity of the vertical and horizontal layers
recorded_models = [(Vp_vertical, 'L23_exc')]
labels = ["Vertical"]
start = 130.0
stop = 140.0
#start = 650.0
#stop = 660.0
plotting.topographic_representation(fig, recorders, recorded_models,
labels, Params['Np'],
np.sum(Params['intervals']),
Params['resolution'], rows, cols,
start, stop, 8, 0)
recorded_models = [(Vp_horizontal, 'L23_exc')]
labels = ["Horizontal"]
start = 130.0
stop = 140.0
#start = 650.0
#stop = 660.0
plotting.topographic_representation(fig, recorders, recorded_models,
labels, Params['Np'],
np.sum(Params['intervals']),
Params['resolution'], rows, cols,
start, stop, 8, 1)
plt.show()
# Plot D: movie
#labels = ["Evoked_Vp_L23_Vertical","Evoked_Vp_L23_Horizontal"]
#recorded_models = [(Vp_vertical,'L23_exc'),(Vp_horizontal,'L23_exc')]
#plotting.makeMovie(fig,recorders,recorded_models,labels,Params['Np'],np.sum(Params['intervals']),Params['resolution'])
#! ====================
#! Save Results
#! ====================
print('save recorders data')
# Set folder
#rootdir = '/home/kfujii2/newNEST2/ht_model_pablo_based/data/'
#expdir = 'random/'
# expdir = 'random_full/'
# expdir = 'structured_full/'
# data_folder = rootdir + expdir
data_folder = Params['data_folder']
if not os.path.isdir(data_folder):
os.makedirs(data_folder)
# To save spike data, set pairs of population id and its name
population_name = [{
'population': Retina_layer,
'name': 'Retina'
}, {
'population': Vp_vertical,
'name': 'Vp_v'
}, {
'population': Vp_horizontal,
'name': 'Vp_h'
}, {
'population': Rp_layer,
'name': 'Rp'
}, {
'population': Tp_layer,
'name': 'Tp'
}, {
'population': Vs_vertical,
'name': 'Vs_v'
}, {
'population': Vs_horizontal,
'name': 'Vs_h'
}]
for rec, population, model in recorders:
# Get name of population
for p in range(0, len(population_name), 1):
if population_name[p]['population'] == population:
p_name = population_name[p]['name']
data = nest.GetStatus(rec)[0]['events']
if model == 'Retina':
scipy.io.savemat(data_folder + '/recorder_' + p_name + '_' +
model + '.mat',
mdict={
'senders': data['senders'],
'rate': data['rate']
})
else:
scipy.io.savemat(data_folder + '/recorder_' + p_name + '_' +
model + '.mat',
mdict={
'senders': data['senders'],
'V_m': data['V_m'],
'I_syn_AMPA': data['I_syn_AMPA'],
'I_syn_NMDA': data['I_syn_NMDA'],
'I_syn_GABA_A': data['I_syn_GABA_A'],
'I_syn_GABA_B': data['I_syn_GABA_B'],
'g_AMPA': data['g_AMPA'],
'g_NMDA': data['g_NMDA'],
'g_GABAA': data['g_GABAA'],
'g_GABAB': data['g_GABAB']
})
print('save raster images')
plt.close()
for rec, population, model in detectors:
spikes = nest.GetStatus(rec, 'events')[0]
# Get name of population
for p in range(0, len(population_name), 1):
if population_name[p]['population'] == population:
p_name = population_name[p]['name']
if len(nest.GetStatus(rec)[0]['events']['senders']) > 3:
raster = raster_plot.from_device(rec, hist=True)
pylab.title(p_name + '_' + model)
f = raster[0].figure
f.set_size_inches(15, 9)
f.savefig(data_folder + 'spikes_' + p_name + '_' + model + '.png',
dpi=100)
plt.close()
# Set filename and save spike data
filename = data_folder + 'spike_' + p_name + '_' + model + '.pickle'
pickle.dump(spikes, open(filename, 'w'))
scipy.io.savemat(data_folder + '/spike_' + p_name + '_' + model +
'.mat',
mdict={
'senders': spikes['senders'],
'times': spikes['times']
})
'''
filename_AMPA = data_folder + 'connection_' + p_name + '_AMPA_syn' + '.dat'
filename_NMDA = data_folder + 'connection_' + p_name + '_NMDA_syn' + '.dat'
filename_GABAA = data_folder + 'connection_' + p_name + '_GABA_A_syn' + '.dat'
filename_GABAB = data_folder + 'connection_' + p_name + '_GABA_B_syn' + '.dat'
tp.DumpLayerConnections(population, 'AMPA_syn', filename_AMPA)
tp.DumpLayerConnections(population, 'NMDA_syn', filename_NMDA)
tp.DumpLayerConnections(population, 'GABA_A_syn', filename_GABAA)
tp.DumpLayerConnections(population, 'GABA_B_syn', filename_GABAB)
'''
'''
for p in range(0, len(population_name), 1):
population = population_name[p]['population']
p_name = population_name[p]['name']
filename_nodes = data_folder + '/gid_' + p_name + '.dat'
tp.DumpLayerNodes(population, filename_nodes)
'''
network_script = Params['network'] + '.py'
shutil.copy2(network_script, Params['data_folder'] + network_script)
print('end')
}, {
"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
#! =================
"positions": poss,
"elements": "iaf_psc_alpha",
"extent": [1.1, 1.1]
}
my_layer_dict_on_grid = {
"rows": 11,
"columns": 11,
"extent": [11.0, 11.0],
"elements": 'iaf_psc_alpha'
}
# connectivity specifications with a mask
conndict = {
'connection_type': 'divergent',
'mask': {
'rectangular': {
'lower_left': [-2.0, -1.0],
'upper_right': [2.0, 1.0]
}
}
}
my_layer = topp.CreateLayer(my_layer_dict_on_grid)
topp.PlotLayer(my_layer)
topp.ConnectLayers(my_layer, my_layer, conndict)
nest.PrintNetwork(depth=1)
topp.PlotTargets([5], my_layer)
plt.show()
