115 lines
3.3 KiB
Python
115 lines
3.3 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""
|
|
Script to run STN +Kv1.1 model
|
|
|
|
"""
|
|
__author__ = "Nils A. Koch"
|
|
__copyright__ = "Copyright 2022, Nils A. Koch"
|
|
__license__ = "MIT"
|
|
|
|
import numpy as np
|
|
import os
|
|
from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict
|
|
from Code.Functions.STN_fxns_Kv import STN_Kv_mut
|
|
|
|
|
|
|
|
# model parameters
|
|
dt = 0.01
|
|
sec = 2
|
|
low = 0
|
|
high = 0.005
|
|
number_steps = 100
|
|
initial_period = 1000
|
|
num_gating = 17
|
|
num_current = 9
|
|
d = 61.4
|
|
r = d/2 * 10**-6 # radius in meters
|
|
vol = np.pi * r**3 # volume in liters
|
|
C, surf_area = capacitance(d, 1)
|
|
stim_time, I_in, stim_num, V_m = stimulus_init(low, high, number_steps, initial_period, dt, sec)
|
|
shift, scale, slope_shift, E, currents_included, b_param, g = init_dict(np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut','Ca_conc', 'E_Ca']))
|
|
V_init = -70
|
|
|
|
# initialize arrays
|
|
current = np.zeros((num_current, stim_num))
|
|
gating = np.zeros((num_gating, stim_num))
|
|
|
|
# initialize dictionary
|
|
ind_dict = {}
|
|
i = 0
|
|
for var in np.array(['m', 'h', 'n', 'a', 'b', 'c', 'd1', 'd2', 'p', 'q', 'r', 's', 'u', 's_mut', 'u_mut', 'Ca_conc','E_Ca']):
|
|
ind_dict[var] = i
|
|
i += 1
|
|
i = 0
|
|
for var in np.array(['Na', 'Kd', 'A', 'L', 'Kv', 'Kv_mut', 'T', 'Leak', 'Ca_K']):
|
|
ind_dict[var] = i
|
|
i += 1
|
|
|
|
# gating parameters
|
|
b_param['m'] = np.array([-40., -1., 1.])
|
|
b_param['h'] = np.zeros(4)
|
|
b_param['h'] = np.array([-45.5, 1., 1., 0.05])
|
|
b_param['n'] = np.array([-41., -1., 1.])
|
|
b_param['p'] = np.array([-56., -1., 1.])
|
|
b_param['q'] = np.array([-85., 1., 1.])
|
|
b_param['r'] = np.array([0.17, -0.08, 1.])
|
|
b_param['a'] = np.array([-45., -14.7, 1.])
|
|
b_param['b'] = np.array([-90., 7.5, 1.])
|
|
b_param['c'] = np.array([-30.6, -5., 1.])
|
|
b_param['d1'] = np.array([-60, 7.5, 1.])
|
|
b_param['d2'] = np.array([0.1, 0.02, 1.])
|
|
|
|
b_param['s'] = np.array([-14.16, -10.15, 1.])
|
|
b_param['u'] = np.zeros(4)
|
|
b_param['u'] = np.array([-31., 5.256, 1., 0.245])
|
|
b_param['s_mut'] = np.array([-14.16, -10.15, 1.])
|
|
b_param['u_mut'] = np.zeros(4)
|
|
b_param['u_mut'] = np.array([-31., 5.256, 1., 0.245])
|
|
|
|
# reversal potentials
|
|
E["Na"] = 60.
|
|
E["K"] = -90.
|
|
E["Leak"] = -60.
|
|
|
|
# model currents
|
|
currents_included["Na"] = True
|
|
currents_included["Kd"] =True
|
|
currents_included["Kv"] = True
|
|
currents_included["Kv_mut"] = True
|
|
currents_included["L"] = True
|
|
currents_included["T"] = True
|
|
currents_included["Ca_K"] = True
|
|
currents_included["A"] =True
|
|
currents_included["Leak"] = True
|
|
|
|
# model conductances
|
|
Kv_ratio = 0.1
|
|
g["Na"] = 49. * surf_area
|
|
g["Kd"] = 27. * (1-Kv_ratio)* surf_area
|
|
if currents_included["Kv_mut"]==True:
|
|
g["Kv"] = 27. * Kv_ratio/2 * surf_area
|
|
else:
|
|
g["Kv"] = 27. * Kv_ratio/2 * surf_area * 2
|
|
g["Kv_mut"] = 27. * Kv_ratio/2 * surf_area
|
|
g["A"] = 5. * surf_area
|
|
g["L"] = 5. * surf_area
|
|
g["T"] = 5. * surf_area
|
|
g["Ca_K"] = 1 * surf_area
|
|
g["Leak"] = 0.035 * surf_area
|
|
|
|
prominence = 50
|
|
min_spike_height = 0
|
|
desired_AUC_width = high/5
|
|
|
|
folder = '../Neuron_models'
|
|
mut = 'STN_Kv'
|
|
mutations = {mut:{'g_ratio': 1, 'activation_Vhalf_diff': 0, 'activation_k_ratio':0}}
|
|
if not os.path.isdir(folder):
|
|
os.makedirs(folder)
|
|
|
|
STN_Kv_mut(V_init, g, E, I_in, dt, currents_included, stim_time, stim_num, C, shift, scale, b_param, slope_shift,
|
|
gating, current, prominence, desired_AUC_width, mutations, mut, folder, high, low, number_steps,
|
|
initial_period, sec, vol)
|
|
|