'b': 60.,
'C_m': 200.,
'V_m': -60.,
'w': 0.,
'tau_syn_ex': 0.2
}
pop = nngt.NeuralPop.uniform(1000,
neuron_model='aeif_psc_alpha',
neuron_param=di_param)
net = nngt.generation.erdos_renyi(avg_deg=100,
nodes=1000,
population=pop,
weights=43.)
gids = net.to_nest()
''' Record from it '''
rec_param = [{'to_file': True}]
recorder, recorded = monitor_nodes(gids, params=rec_param, network=net)
nest.Simulate(2000.)
activity = pna.analysis.raster_analysis(recorder[0], limits=[300., np.inf])
''' Plot the spikes with sorted neurons '''
pprint(activity.properties())
pna.plot.raster_plot(activity, sort='spikes')
plt.show()
def activity_simulation(net, pop, sim_duration=sim_duration):
'''
Execute activity simulation on the generated graph
--------------------------------------------------
Input : net : nngt generated graph with model
pop : neurons population
sim_duration : time (s) duration of simulation
Output :
activity graphs
'''
if (nngt.get_config('with_nest')) and (simulate_activity is True):
print("SIMULATE ACTIVITY")
import nngt.simulation as ns
import nest
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads': 14})
nest.SetKernelStatus({'resolution': .5})
gids = net.to_nest()
# nngt.simulation.randomize_neural_states(net, {"w": ("uniform", 0, 200),
# "V_m": ("uniform", -70., -40.)})
nngt.simulation.randomize_neural_states(net, {
"w": ("normal", 50., 5.),
"V_m": ("uniform", -80., -50.)
})
# add noise to the excitatory neurons
excs = list(gids)
# ns.set_noise(excs, 10., 2.)
# ns.set_poisson_input(gids, rate=3500., syn_spec={"weight": 34.})
target_rate = 25.
# 2019 11 13 OLD VERSION TO THIS DATE
# According to Mallory nngt now dealse differently with
# minis-rate and weight, automatically normalizing with the degree
average_degree = net.edge_nb() / float(net.node_nb())
syn_rate = target_rate / average_degree
print("syn_rate", syn_rate)
print("avg_deg", average_degree)
# ns.set_minis(net, base_rate=syn_rate, weight=.4, gids=gids
ns.set_minis(net,
base_rate=target_rate,
weight=.4 * synaptic_weigth,
gids=gids)
vm, vs = ns.monitor_nodes([1, 10], "voltmeter")
recorders, records = ns.monitor_groups(pop.keys(), net)
nest.Simulate(sim_duration)
if plot_activity is True:
print("Plot raster")
ns.plot_activity(vm, vs, network=net, show=False)
ns.plot_activity(recorders,
records,
network=net,
show=False,
histogram=True)
plt.show()
if animation_movie is True:
print("Process animation")
anim = nngt.plot.AnimationNetwork(recorders,
net,
resolution=0.1,
interval=20,
decimate=-1)
# ~ anim.save_movie("structured_bursting.mp4", fps=2, start=650.,
# ~ stop=1500, interval=20)
# ~ anim.save_movie("structured_bursting.mp4", fps=5, num_frames=50)
anim.save_movie("structured_bursting.mp4", fps=15, interval=10)
if save_spikes is True:
print("Save sikes of neurons")
nngt.simulation.save_spikes("spikes_output.dat")
#~ nngt.generation.connect_neural_types(graph, 1, 1, "random_scale_free", {"in_exp":2.1, "out_exp":2.9, "density":0.05})
#~ nngt.generation.connect_neural_types(graph, -1, 1, "erdos_renyi", {"density": 0.05})
#~ nngt.generation.connect_neural_types(graph, -1, -1, "erdos_renyi", {"density": 0.01})
graph = nngt.generation.erdos_renyi(density=0.1, population=pop, weight_prop={"distrib":"gaussian", "distrib_prop":{"avg":avg}})
#~ nngt.generation.random_scale_free(2.2, 2.9, density=0.15, from_graph=graph)
#~ graph = nngt.generation.newman_watts(10, 0.1, population=pop)
#-----------------------------------------------------------------------------#
# NEST objects
#------------------------
#
subnet, gids = make_nest_network(graph)
recorders, record = monitor_nodes(gids, ["spike_detector"], [["spikes"]], network=graph)
recorders2, record2 = monitor_nodes((gids[0],), ["multimeter"], [["V_m","w"]])
set_noise(gids, 0., 200.)
rate = 20000.
if nmodel == "aeif_cond_exp":
rate = 56000.
#~ set_poisson_input(gids[670:870], rate)
set_poisson_input(gids[:800], rate)
set_poisson_input(gids[800:], 0.75*rate)
#-----------------------------------------------------------------------------#
# Names
def test_utils():
'''
Check NEST utility functions
'''
nest = pytest.importorskip("nest")
import nngt.simulation as ns
nest.ResetKernel()
resol = nest.GetKernelStatus('resolution')
w = 5.
net, gids = make_nest_net(100, w, deg=10)
# set inputs
ns.set_noise(gids, 0., 20.)
assert len(nest.GetConnections()) == net.edge_nb() + net.node_nb()
ns.set_poisson_input(gids, 5.)
assert len(nest.GetConnections()) == net.edge_nb() + 2 * net.node_nb()
ns.set_minis(net, base_rate=1.5, weight=2.)
assert len(nest.GetConnections()) == net.edge_nb() + 3 * net.node_nb()
ns.set_step_currents(gids[::2], times=[40., 60.], currents=[800., 0.])
min_conn = net.edge_nb() + 3 * net.node_nb() + 1
max_conn = net.edge_nb() + 4 * net.node_nb()
num_conn = len(nest.GetConnections())
assert min_conn <= num_conn <= max_conn
# check randomization of neuronal properties
vms = {d['V_m'] for d in nest.GetStatus(gids)}
assert len(vms) == 1
ns.randomize_neural_states(net, {'V_m': ('uniform', -70., -60.)})
vms = {d['V_m'] for d in nest.GetStatus(gids)}
assert len(vms) == net.node_nb()
# monitoring nodes
sd, _ = ns.monitor_groups(net.population, net)
assert len(nest.GetConnections()) == num_conn + net.node_nb()
vm, rec = ns.monitor_nodes(gids[0],
nest_recorder='voltmeter',
params={'interval': resol})
assert len(nest.GetConnections()) == num_conn + net.node_nb() + 1
nest.Simulate(100.)
ns.plot_activity(show=False)
ns.plot_activity(vm, rec, show=True)
def activity_simulation(net, pop, sim_duration=5000.):
'''
Execute activity simulation on the generated graph
--------------------------------------------------
Input : net : nngt generated graph with model
pop : neurons population
sim_duration : time (s) duration of simulation
Output :
activity graphs
'''
if nngt.get_config('with_nest'):
import nngt.simulation as ns
import nest
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads': 14})
gids = net.to_nest()
#nngt.simulation.randomize_neural_states(net, {"w": ("uniform", 0, 200),
# "V_m": ("uniform", -70., -40.)})
nngt.simulation.randomize_neural_states(net, {
"w": ("normal", 50., 5.),
"V_m": ("uniform", -80., -50.)
})
# add noise to the excitatory neurons
excs = list(gids)
#ns.set_noise(excs, 10., 2.)
#ns.set_poisson_input(gids, rate=3500., syn_spec={"weight": 34.})
target_rate = 25.
average_degree = net.edge_nb() / float(net.node_nb())
syn_rate = target_rate / average_degree
print("syn_rate", syn_rate)
print("avg_deg", average_degree)
ns.set_minis(net, base_rate=syn_rate, weight_fraction=.4, gids=gids)
vm, vs = ns.monitor_nodes([1], "voltmeter")
recorders, records = ns.monitor_groups(pop.keys(), net)
nest.Simulate(5000)
# ~ if nngt.get_config('with_plot'):
# ~ ns.plot_activity(vm, vs, network=net, show=False)
# ~ ns.plot_activity(recorders, records, network=net, show=False, hist=True)
anim = nngt.plot.AnimationNetwork(recorders,
net,
resolution=0.1,
interval=20,
decimate=-1)
# ~ anim.save_movie("structured_bursting.mp4", fps=2, start=650.,
# ~ stop=1500, interval=20)
# ~ anim.save_movie("structured_bursting.mp4", fps=5, num_frames=50)
anim.save_movie("structured_bursting.mp4", fps=15, interval=10)
plt.show()