def test_fitzHughNagumo_gilzenrat_figure_2(self):
# Isolate the FitzHughNagumo mechanism for testing and recreate figure 2 from the gilzenrat paper
initial_v = 0.2
initial_w = 0.0
F = IntegratorMechanism(name='IntegratorMech-FitzHughNagumoFunction',
function=FitzHughNagumoIntegrator(
initial_v=initial_v,
initial_w=initial_w,
time_step_size=0.01,
time_constant_w=1.0,
time_constant_v=0.01,
a_v=-1.0,
b_v=1.0,
c_v=1.0,
d_v=0.0,
e_v=-1.0,
f_v=1.0,
threshold=0.5,
mode=1.0,
uncorrelated_activity=0.0,
a_w=1.0,
b_w=-1.0,
c_w=0.0))
plot_v_list = [initial_v]
plot_w_list = [initial_w]
# found this stimulus by guess and check b/c one was not provided with Figure 2 params
stimulus = 0.073
# increase range to 200 to match Figure 2 in Gilzenrat
for i in range(10):
results = F.execute(stimulus)
plot_v_list.append(results[0][0][0])
plot_w_list.append(results[1][0][0])
# ** uncomment the lines below if you want to view the plot:
# from matplotlib import pyplot as plt
# plt.plot(plot_v_list)
# plt.plot(plot_w_list)
# plt.show()
np.testing.assert_allclose(plot_v_list, [
0.2, 0.22493312915681499, 0.24840327807265583, 0.27101619694032797,
0.29325863380332173, 0.31556552465130933, 0.33836727470568129,
0.36212868305470697, 0.38738542852040492, 0.41478016676749552,
0.44509530539552955
])
print(plot_w_list)
np.testing.assert_allclose(plot_w_list, [
0.0, 0.0019900332500000003, 0.0042083541185625045,
0.0066381342093118408, 0.009268739886338381, 0.012094486544132229,
0.015114073825358726, 0.018330496914962583, 0.021751346023501487,
0.025389465931011893, 0.029263968140538919
])
def test_reset_state_integrator_mechanism(self):
A = IntegratorMechanism(name='A', function=DriftDiffusionIntegrator())
# Execute A twice
# [0] saves decision variable only (not time)
original_output = [A.execute(1.0)[0], A.execute(1.0)[0]]
# SAVING STATE - - - - - - - - - - - - - - - - - - - - - - - - -
reinitialize_values = []
for attr in A.function.stateful_attributes:
reinitialize_values.append(getattr(A.function, attr))
# Execute A twice AFTER saving the state so that it continues accumulating.
# We expect the next two outputs to repeat once we reset the state b/c we will return it to the current state
output_after_saving_state = [A.execute(1.0)[0], A.execute(1.0)[0]]
# RESETTING STATE - - - - - - - - - - - - - - - - - - - - - - - -
A.reinitialize(*reinitialize_values)
# We expect these results to match the results from immediately after saving the state
output_after_reinitialization = [A.execute(1.0)[0], A.execute(1.0)[0]]
assert np.allclose(output_after_saving_state,
output_after_reinitialization)
assert np.allclose(
original_output,
[np.array([[1.0]]), np.array([[2.0]])])
assert np.allclose(
output_after_reinitialization,
[np.array([[3.0]]), np.array([[4.0]])])