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()