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 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 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_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()
