"""gradient.py explores the effect of adding a gradient of tonic
gabaar in the Iascone model."""
from tonic_GABAAR import *
from pathlib import Path
from g_scale import *
# sample gbar_exGABALeak values to study in a gradient in the model
# doubling the gbar_exGABALeak values to make similar total amounts to
# flat distribution.
print("soma, 25 ums 50 ums, 100 ums")
[1e-4, 2e-4, 4e-4, 8e-4, 20e-4]
for gbar_exGABALeak in [20e-4]:
set_tonic(0)
print(f"linear gbar_exGABALeak proximal 0 to distal {1e4*gbar_exGABALeak}")
g_piecewise_linear('d', 'exGABALeak', 0, 0, 379, gbar_exGABALeak)
h.v_init=-66
my_run(400)
#############
#
# store control tonic GABAAR pre bAP V's
#
#############
control_soma=[soma_voltageVector[pre_bAP_index]] # list of control
# soma voltage
control_dend_100=[] # list of control_dend_100
control_dend_50=[] # list of control_dend_50
control_dend_25=[] # list of control_dend_25
for vector in dend_100:
control_dend_100.append(vector[pre_bAP_index])
for vector in dend_50:
control_dend_50.append(vector[pre_bAP_index])
for vector in dend_25:
control_dend_25.append(vector[pre_bAP_index])
delta_bAP_V_soma=control_soma[0]-blocked_soma[0]
delta_bAP_V_dend_100=[]
for element in zip(control_dend_100, blocked_dend_100):
delta_bAP_V_dend_100.append(element[0]-element[1])
delta_bAP_V_dend_50=[]
for element in zip(control_dend_50, blocked_dend_50):
delta_bAP_V_dend_50.append(element[0]-element[1])
delta_bAP_V_dend_25=[]
for element in zip(control_dend_25, blocked_dend_25):
delta_bAP_V_dend_25.append(element[0]-element[1])
print(f"{gbar_exGABALeak*1e4}, " + \
f"{np.mean(delta_bAP_V_soma):.3f}+-{np.std(delta_bAP_V_soma):.3f} " + \
f"{np.mean(delta_bAP_V_dend_25):.3f}+-{np.std(delta_bAP_V_dend_25):.3f} " + \
f"{np.mean(delta_bAP_V_dend_50):.3f}+-{np.std(delta_bAP_V_dend_50):.3f} " + \
f"{np.mean(delta_bAP_V_dend_100):.3f}+-{np.std(delta_bAP_V_dend_100):.3f} ")