Send to NEST and set excitation and recorders
'''
if nngt.get_config('with_nest'):
import nest
import nngt.simulation as ns
nest.ResetKernel()
gids = net.to_nest()
# add noise to the excitatory neurons
excs = list(pop["excitatory"].nest_gids)
ns.set_noise(excs, 10., 2.)
# record
groups = [key for key in net.population]
recorder, record = ns.monitor_groups(groups, net)
'''
Simulate and plot.
'''
simtime = 2000.
nest.Simulate(simtime)
if nngt.get_config('with_plot'):
ns.plot_activity(recorder,
record,
network=net,
show=True,
hist=False,
limits=(0, simtime))
'''
pop = nngt.NeuralPop.from_groups([oscill, burst, adapt],
names=['oscillators', 'bursters', 'adaptive'],
syn_spec=synapses)
'''
Create the network from this population,
using a Gaussian in-degree
'''
net = ng.gaussian_degree(100., 15., population=pop, weights=155., delays=5.)
'''
Send the network to NEST, monitor and simulate
'''
if nngt.get_config('with_nest'):
import nngt.simulation as ns
import nest
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads': 4})
gids = net.to_nest()
nngt.simulation.randomize_neural_states(net, {"w": ("uniform", 0, 200)})
recorders, records = ns.monitor_groups(pop.keys(), net)
nest.Simulate(1600.)
if nngt.get_config('with_plot'):
ns.plot_activity(recorders, records, network=net, show=True)
'local_num_threads': 4,
"rng_seeds": [5, 6, 7, 8]
})
gids = net.to_nest()
pg = ns.set_poisson_input(gids,
rate=p_rate,
syn_spec={
"weight": J_ex,
"delay": delay
})
ns.set_minis(net, base_rate=0.1)
recorders, records = ns.monitor_groups(["excitatory", "inhibitory"],
network=net)
nest.Simulate(simtime)
if nngt.get_config('with_plot'):
ideg = net.get_degrees("in", syn_type="inhibitory")
edeg = net.get_degrees("in", syn_type="excitatory")
# plot the basic activity
ns.plot_activity(recorders, records, network=net, show=False)
# sort by firing rate
ns.plot_activity(recorders,
records,
network=net,
sort="firing_rate",
show=False)
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")
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()
