def singlepop(steady_state, tau_m=.02, p0=((0.,),(1.,)), weights={'distribution':'delta', 'loc':.005}, bgfr=100, network_update_callback=lambda s: None, update_method='approx', simulation_configuration=None, tol=None, checkpoint_callback=None, nsyn=1):
# Settings:
t0 = 0.
dt = .001
dv = .001
v_min = -.01
v_max = .02
tf = .1
# Create simulation:
b1 = ExternalPopulation(bgfr)
i1 = InternalPopulation(v_min=v_min, tau_m=tau_m, v_max=v_max, dv=dv, update_method=update_method, p0=p0, tol=tol)
b1_i1 = Connection(b1, i1, nsyn, weights=weights)
network = Network([b1, i1], [b1_i1], update_callback=network_update_callback)
if simulation_configuration is None:
simulation_configuration = SimulationConfiguration(dt, tf, t0=t0)
simulation = Simulation(network=network, simulation_configuration=simulation_configuration, checkpoint_callback=checkpoint_callback)
simulation.run()
b1.plot()
i1.plot_probability_distribution()
i1.plot()
assert i1.n_edges == i1.n_bins+1
# Test steady-state:
np.testing.assert_almost_equal(i1.get_firing_rate(.05), steady_state, 12)
def test_singlepop():
# Settings:
t0 = 0.
dt = .001
dv = .001
v_min = -.01
v_max = .02
tf = .2
verbose = False
# Create simulation:
b1 = ExternalPopulation(50)
b2 = ExternalPopulation(50)
i1 = InternalPopulation(v_min=v_min, v_max=v_max, dv=dv, update_method='exact')
b1_i1 = Connection(b1, i1, 1, weights=[.005], probs=[1.])
b2_i1 = Connection(b2, i1, 1, weights=[.005], probs=[1.])
simulation = Simulation([b1, b2, i1], [b1_i1, b2_i1], verbose=verbose)
simulation.run(dt=dt, tf=tf, t0=t0)
np.testing.assert_almost_equal(i1.t_record[-1], .2, 15)
np.testing.assert_almost_equal(i1.firing_rate_record[-1], 5.3550005434746355, 12)
assert i1.n_bins == (v_max - v_min)/dv
assert i1.n_edges - 1 == i1.n_bins
assert len(simulation.population_list) == 3
i1.plot_probability_distribution()
def test_delay_doublepop():
# Settings:
t0 = 0.
dt = .001
tf = .010
verbose = False
# Create populations:
b1 = ExternalPopulation(50)
i1 = InternalPopulation(v_min=0, v_max=.02, dv=.001, update_method='exact')
i2 = InternalPopulation(v_min=0, v_max=.02, dv=.001, update_method='exact')
# Create connections:
b1_i1 = Connection(b1, i1, 2, weights=[.005], probs=[1.])
i1_i2 = Connection(i1, i2, 20, weights=[.005], probs=[1.], delay=2 * dt)
# Create and run simulation:
simulation = Simulation([b1, i1, i2], [b1_i1, i1_i2], verbose=verbose)
simulation.run(dt=dt, tf=tf, t0=t0)
true_ans = np.array([
0, 0.0, 0.0, 0.0, 1.9089656152757652e-13, 1.9787511418980406e-10,
9.5007650186649266e-09, 1.3334881090883857e-07, 1.0103767575651715e-06,
5.3604521936092067e-06, 2.2383604753409621e-05
])
np.testing.assert_almost_equal(i2.firing_rate_record, true_ans, 12)
def test_singlepop():
# Settings:
t0 = 0.
dt = .001
dv = .001
v_min = -.01
v_max = .02
tf = .2
verbose = False
# Create simulation:
b1 = ExternalPopulation(50)
b2 = ExternalPopulation(50)
i1 = InternalPopulation(v_min=v_min,
v_max=v_max,
dv=dv,
update_method='exact')
b1_i1 = Connection(b1, i1, 1, weights=[.005], probs=[1.])
b2_i1 = Connection(b2, i1, 1, weights=[.005], probs=[1.])
simulation = Simulation([b1, b2, i1], [b1_i1, b2_i1], verbose=verbose)
simulation.run(dt=dt, tf=tf, t0=t0)
np.testing.assert_almost_equal(i1.t_record[-1], .2, 15)
np.testing.assert_almost_equal(i1.firing_rate_record[-1],
5.3550005434746355, 12)
assert i1.n_bins == (v_max - v_min) / dv
assert i1.n_edges - 1 == i1.n_bins
assert len(simulation.population_list) == 3
i1.plot_probability_distribution()
def singlepop(steady_state, tau_m=.02, p0=((0.,),(1.,)), weights={'distribution':'delta', 'loc':.005}, bgfr=100, network_update_callback=lambda s: None, update_method='approx', simulation_configuration=None, tol=None):
# Settings:
t0 = 0.
dt = .001
dv = .001
v_min = -.01
v_max = .02
tf = .1
# Create simulation:
b1 = ExternalPopulation(bgfr)
i1 = InternalPopulation(v_min=v_min, tau_m=tau_m, v_max=v_max, dv=dv, update_method=update_method, p0=p0, tol=tol)
b1_i1 = Connection(b1, i1, 1, weights=weights)
network = Network([b1, i1], [b1_i1], update_callback=network_update_callback)
if simulation_configuration is None:
simulation_configuration = SimulationConfiguration(dt, tf, t0=t0)
simulation = Simulation(network=network, simulation_configuration=simulation_configuration)
simulation.run()
b1.plot()
i1.plot_probability_distribution()
i1.plot()
assert i1.n_edges == i1.n_bins+1
# Test steady-state:
np.testing.assert_almost_equal(i1.get_firing_rate(.05), steady_state, 12)
def get_simulation(dv=.001,
verbose=False,
update_method='approx',
approx_order=None,
tol=1e-8):
import scipy.stats as sps
# Create simulation:
b1 = ExternalPopulation(100)
i1 = InternalPopulation(v_min=0,
v_max=.02,
dv=dv,
update_method=update_method,
approx_order=approx_order,
tol=tol)
b1_i1 = Connection(b1,
i1,
1,
delay=0.0,
distribution=sps.expon,
N=201,
scale=.005)
simulation = Simulation([b1, i1], [b1_i1], verbose=verbose)
return simulation
def test_delay_singlepop():
# Settings:
t0 = 0.
dt = .001
tf = .005
verbose = False
# Create simulation:
b1 = ExternalPopulation('Heaviside(t)*100')
i1 = InternalPopulation(v_min=0, v_max=.02, dv=.001, update_method='exact')
b1_i1 = Connection(b1, i1, 1, weights=[.005], probs=[1.], delay=2*dt)
simulation = Simulation([b1, i1], [b1_i1], verbose=verbose)
simulation.run(dt=dt, tf=tf, t0=t0)
true_ans = np.array([0, 0.0, 0.0, 0.00066516669656511084, 0.025842290308637855, 0.08117342489138904])
np.testing.assert_almost_equal(i1.firing_rate_record, true_ans, 12)
def test_simulation_configuration():
sc = SimulationConfiguration(dt=.001, tf=.01, t0=0)
sc.to_json()
sc.to_json(StringIO.StringIO())
s = Simulation(simulation_configuration=sc)
assert s.t0 == sc.t0
assert s.tf == sc.tf
assert s.dt == sc.dt
def get_simulation(dt=.001, dv=.001, tf=.2, verbose=False, update_method='exact', approx_order=None, tol=1e-8):
# Create simulation:
b1 = ExternalPopulation('100')
i1 = InternalPopulation(v_min=0, v_max=.02, dv=dv, update_method=update_method, approx_order=approx_order, tol=tol)
b1_i1 = Connection(b1, i1, 1, weights=[.005], probs=[1.], delay=0.0)
simulation = Simulation([b1, i1], [b1_i1], dt=dt, tf=tf, verbose=verbose)
return simulation
def test_delay_doublepop():
# Settings:
t0 = 0.
dt = .001
tf = .010
verbose = False
# Create populations:
b1 = ExternalPopulation(50)
i1 = InternalPopulation(v_min=0, v_max=.02, dv=.001, update_method='exact')
i2 = InternalPopulation(v_min=0, v_max=.02, dv=.001, update_method='exact')
# Create connections:
b1_i1 = Connection(b1, i1, 2, weights=[.005], probs=[1.])
i1_i2 = Connection(i1, i2, 20, weights=[.005], probs=[1.], delay=2*dt)
# Create and run simulation:
simulation = Simulation([b1, i1, i2], [b1_i1, i1_i2], verbose=verbose)
simulation.run(dt=dt, tf=tf, t0=t0)
true_ans = np.array([0, 0.0, 0.0, 0.0, 1.9089656152757652e-13, 1.9787511418980406e-10, 9.5007650186649266e-09, 1.3334881090883857e-07, 1.0103767575651715e-06, 5.3604521936092067e-06, 2.2383604753409621e-05])
np.testing.assert_almost_equal(i2.firing_rate_record, true_ans, 12)
(target_layer,
target_celltype)] * internal_population_sizes[source_layer,
source_celltype]
weight = conn_weights[source_celltype]
curr_connection = Connection(source_population,
target_population,
nsyn,
weights=[weight],
probs=[1.],
delay=0)
connection_list.append(curr_connection)
# Create simulation:
population_list = background_population_dict.values(
) + internal_population_dict.values()
simulation = Simulation(population_list, connection_list, verbose=True)
# Run simulation:
simulation.run(dt=dt, tf=tf, t0=t0)
# Visualize:
y_label_dict = {23: '2/3', 4: '4', 5: '5', 6: '6'}
fig, axes = plt.subplots(nrows=4, ncols=1, **{'figsize': (4, 8)})
for row_ind, layer in enumerate([23, 4, 5, 6]):
for plot_color, celltype in zip(['r', 'b'], ['e', 'i']):
curr_population = internal_population_dict[layer, celltype]
axes[row_ind].plot(curr_population.t_record,
curr_population.firing_rate_record, plot_color)
axes[row_ind].set_xlim([0, tf])
axes[row_ind].set_ylim(ymin=0)
delay=delay * 1e-3)
i2_i2 = Connection(i2,
i2,
C_I,
weights=[J_in * 1e-3],
probs=[1.],
delay=delay * 1e-3)
i1_i2 = Connection(i1,
i2,
C_E,
weights=[J_ex * 1e-3],
probs=[1.],
delay=delay * 1e-3)
i2_i1 = Connection(i2,
i1,
C_I,
weights=[J_in * 1e-3],
probs=[1.],
delay=delay * 1e-3)
simulation = Simulation([b1, i1, i2],
[b1_i1, i1_i1, b1_i2, i2_i2, i1_i2, i2_i1],
verbose=verbose)
simulation.run(dt=dt, tf=tf, t0=t0)
i1 = simulation.population_list[1]
print 'DipDe Mean Exc rate: ', np.array(i1.firing_rate_record).mean(), '[Hz]'
i2 = simulation.population_list[2]
print 'DipDe Mean Inh rate: ', np.array(i2.firing_rate_record).mean(), '[Hz]'
def test_marshal_simulation():
from dipde.examples.excitatory_inhibitory import get_network
simulation_configuration_full = SimulationConfiguration(dt=.001, tf=.02, t0=0)
simulation_configuration_p1 = SimulationConfiguration(dt=.001, tf=.01, t0=0)
simulation_configuration_p2 = SimulationConfiguration(dt=.001, tf=.01, t0=0)
# Run full simulation:
simulation_full = Simulation(network=get_network(), simulation_configuration=simulation_configuration_full)
assert simulation_full.completed == False
simulation_full.run()
assert simulation_full.completed == True
# Run simulation, part 1:
simulation_p1 = Simulation(network=get_network(), simulation_configuration=simulation_configuration_p1)
simulation_p1.run()
s_midway = simulation_p1.to_json()
# Run simulation, part 2:
simulation_p2 = Simulation(**json.loads(s_midway))
simulation_p2.simulation_configuration = simulation_configuration_p2
simulation_p2.run()
# Run copy half way, round trip, and then finish:
# Compare:
y1 = simulation_full.network.population_list[1].firing_rate_record
y2 = simulation_p2.network.population_list[1].firing_rate_record
assert len(y1) == len(y2)
for y1i, y2i in zip(y1, y2):
np.testing.assert_almost_equal(y1i, y2i, 12)
simulation_full.to_json(StringIO.StringIO())
# Create recurrent connections:
for source_layer, source_celltype in itertools.product([23, 4, 5, 6], ["e", "i"]):
for target_layer, target_celltype in itertools.product([23, 4, 5, 6], ["e", "i"]):
source_population = internal_population_dict[source_layer, source_celltype]
target_population = internal_population_dict[target_layer, target_celltype]
nsyn = (
connection_probabilities[(source_layer, source_celltype), (target_layer, target_celltype)]
* internal_population_sizes[source_layer, source_celltype]
)
weight = conn_weights[source_celltype]
curr_connection = Connection(source_population, target_population, nsyn, weights=[weight], probs=[1.0], delay=0)
connection_list.append(curr_connection)
# Create simulation:
population_list = background_population_dict.values() + internal_population_dict.values()
simulation = Simulation(population_list, connection_list, verbose=True)
# Run simulation:
simulation.run(dt=dt, tf=tf, t0=t0)
# Visualize:
y_label_dict = {23: "2/3", 4: "4", 5: "5", 6: "6"}
fig, axes = plt.subplots(nrows=4, ncols=1, **{"figsize": (4, 8)})
for row_ind, layer in enumerate([23, 4, 5, 6]):
for plot_color, celltype in zip(["r", "b"], ["e", "i"]):
curr_population = internal_population_dict[layer, celltype]
axes[row_ind].plot(curr_population.t_record, curr_population.firing_rate_record, plot_color)
axes[row_ind].set_xlim([0, tf])
axes[row_ind].set_ylim(ymin=0)
axes[row_ind].set_ylabel("Layer %s\nfiring rate (Hz)" % y_label_dict[layer])
