1019 lines
57 KiB
Python
1019 lines
57 KiB
Python
"""
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Functions for model from Pospischil et al. 2008 - RS Pyramidal, RS inhibitory, and FS
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"""
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__author__ = "Nils A. Koch"
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__copyright__ = 'Copyright 2022, Nils A. Koch'
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__license__ = "MIT"
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import gc
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import h5py
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import scipy
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import json
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import copy
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import os
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import numpy as np
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from numba import types, njit
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from numba.typed import Dict
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from scipy import signal
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from Code.Functions.Utility_fxns import stimulus_init, NumpyEncoder
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@njit
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def Pospischil(V, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
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b_param, slope_shift,gating, current, ind_dict):
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""" Simulate current and Vm system of ODEs from Pospischil et al. 2008 (https://doi.org/10.1007/s00422-008-0263-8)
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for I_in
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Parameters
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----------
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V : float
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Initial membrane potential
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V_T : float
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Threshold potential
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g : dict
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maximal conductance for currents
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E : dict
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Reversal potentials for currents
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I_in : array
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Current stimulus input to model
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dt : float
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fixed time step
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currents_included : dict
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Boolean to include current in simulation or not
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stim_time :
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length of stimulus ()
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stim_num : int
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number of different stimulus currents
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C : float
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membrane capacitance
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tau_max_p : float
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maximal adaptation (M-current) time constant
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shift : dict
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for each gating parameter
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scale : dict
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for each gating parameter
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b_param : dict
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Boltzmann parameter values for each gating parameter
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slope_shift : dict
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for each gating parameter
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gating : array
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array of gating values over time
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current : array
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array of current values over time
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ind_dict: dict
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Dictionary of indices in arrays for gating variables
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Returns
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-------
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V_m : array
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Simulated membrane potential in response to I_in
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"""
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V_m = np.zeros((stim_num, stim_time))
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# initialize gating
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# Na activation
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m_inf = (1 / (1 + np.exp((V - shift["m"] - slope_shift["m"] - b_param["m"][0]) / b_param["m"][1]))) ** b_param["m"][2]
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gating[ind_dict['m'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * m_inf
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# Na inactivation
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h_inf = ((1 - b_param["h"][3]) / (1 + np.exp((V - shift["h"] - slope_shift["h"] - b_param["h"][0]) / b_param["h"][1]))
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+ b_param["h"][3]) ** b_param["h"][2]
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gating[ind_dict['h'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * h_inf
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# K activation
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n_inf = (1 / (1 + np.exp((V - shift["n"] - slope_shift["n"] - b_param["n"][0]) / b_param["n"][1]))) ** \
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b_param["n"][2]
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gating[ind_dict['n'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * n_inf
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# Kv1.1 activation
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s_inf = (1 / (1 + np.exp((V - shift["s"] - slope_shift["s"] - b_param["s"][0]) / b_param["s"][1]))) ** \
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b_param["s"][2]
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gating[ind_dict['s'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * s_inf
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# Kv1.1 inactivation
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u_inf = ((1 - b_param["u"][3]) / (1 + np.exp((V - shift["u"] - slope_shift["u"] - b_param["u"][0])
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/ b_param["u"][1])) + b_param["u"][3]) ** b_param["u"][2]
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gating[ind_dict['u'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * u_inf
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# mutant Kv1.1 activation
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s_mut_inf = (1 / (1 + np.exp((V - shift["s_mut"] - slope_shift["s_mut"] - b_param["s_mut"][0]) / b_param["s_mut"][1]))) ** \
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b_param["s_mut"][2]
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gating[ind_dict['s_mut'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * s_mut_inf
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# mutant Kv1.1 inactivation
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u_mut_inf = ((1 - b_param["u_mut"][3]) / (1 + np.exp((V - shift["u"] - slope_shift["u_mut"] - b_param["u_mut"][0])
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/ b_param["u_mut"][1])) + b_param["u_mut"][3]) ** \
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b_param["u_mut"][2]
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gating[ind_dict['u_mut'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * u_mut_inf
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# M-type K activation
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p_inf = (1 / (1 + np.exp((V - shift["p"] - slope_shift["p"] - b_param["p"][0]) / b_param["p"][1]))) ** \
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b_param["p"][2]
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gating[ind_dict['p'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * p_inf
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# L-type Ca activation
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q_inf = (1 / (1 + np.exp((V - shift["q"] - slope_shift["q"] - b_param["q"][0]) / b_param["q"][1]))) ** \
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b_param["q"][2]
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gating[ind_dict['q'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * q_inf
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# L-type Ca inactivation
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r_inf = (1 / (1 + np.exp((V - shift["r"] - slope_shift["r"] - b_param["r"][0]) / b_param["r"][1]))) ** \
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b_param["r"][2]
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gating[ind_dict['r'], :] = np.ones((stim_num), dtype=np.dtype('float64')) * r_inf
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# initialize current arrays
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current[ind_dict['Na'], :] = np.zeros((stim_num), dtype=np.dtype('float64'))
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current[ind_dict['Kd'], :] = np.zeros((stim_num), dtype=np.dtype('float64'))
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current[ind_dict['M'], :] = np.zeros((stim_num), dtype=np.dtype('float64'))
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current[ind_dict['Kv'], :] = np.zeros((stim_num), dtype=np.dtype('float64'))
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current[ind_dict['Kv_mut'], :] = np.zeros((stim_num), dtype=np.dtype('float64'))
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current[ind_dict['Leak'], :] = np.zeros((stim_num), dtype=np.dtype('float64'))
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current[ind_dict['L'], :] = np.zeros((stim_num), dtype=np.dtype('float64'))
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#intialize V
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V_m[:,0] = V
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t = 1
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while t < stim_time:
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# Na activation
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m_inf = (1 / (1 + np.exp((V_m[:, t - 1] - shift["m"] - slope_shift["m"] - b_param["m"][0]) / b_param["m"][1]))) ** \
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b_param["m"][2]
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alpha_m = (-0.32 * (V_m[:, t - 1] - shift["m"] - V_T - 13)) / (np.exp(-1 * (V_m[:, t - 1]- shift["m"] - V_T - 13) / 4) - 1)
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beta_m = (0.28 * (V_m[:, t - 1] - shift["m"] - V_T - 40)) / (np.exp((V_m[:, t - 1]- shift["m"] - V_T - 40) / 5) - 1)
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tau_m = 1 / (alpha_m + beta_m)
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m_dot = (m_inf - gating[ind_dict['m'], :]) * (1 / (tau_m * scale["m"]))
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gating[ind_dict['m'], :] = gating[ind_dict['m'], :] + m_dot * dt
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gating[ind_dict['m'], :][gating[ind_dict['m'], :] > 1.] = 1.
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gating[ind_dict['m'], :][gating[ind_dict['m'], :] < 0.] = 0.
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# Na inactivation
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h_inf = ((1 - b_param["h"][3]) / (1 + np.exp((V_m[:, t - 1]- shift["h"] - slope_shift["h"] - b_param["h"][0])
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/ b_param["h"][1])) + b_param["h"][3]) ** b_param["h"][2]
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alpha_h = 0.128 * np.exp(-1 * (V_m[:, t - 1]- shift["h"] - V_T - 17) / 18)
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beta_h = 4 / (1 + np.exp(-1 * (V_m[:, t - 1]- shift["h"] - V_T - 40) / 5))
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tau_h = 1 / (alpha_h + beta_h)
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h_dot = (h_inf - gating[ind_dict['h'], :]) * (1 / (tau_h * scale["h"]))
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gating[ind_dict['h'], :] = gating[ind_dict['h'], :] + h_dot * dt
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gating[ind_dict['h'], :][gating[ind_dict['h'], :] > 1.] = 1.
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gating[ind_dict['h'], :][gating[ind_dict['h'], :] < 0.] = 0.
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# K activation
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n_inf = (1 / (1 + np.exp((V_m[:, t - 1]- shift["n"] - slope_shift["n"] - b_param["n"][0]) / b_param["n"][1]))) ** \
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b_param["n"][2]
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alpha_n = (-0.032 * (V_m[:, t - 1]- shift["n"] - V_T - 15)) / (np.exp(-1 * (V_m[:, t - 1]- shift["n"] - V_T - 15) / 5) - 1)
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beta_n = (0.5 * np.exp(-1 * (V_m[:, t - 1]- shift["n"] - V_T - 10) / 40))
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tau_n = 1 / (alpha_n + beta_n)
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n_dot = (n_inf - gating[ind_dict['n'], :]) * (1 / (tau_n * scale["n"]))
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gating[ind_dict['n'], :] = gating[ind_dict['n'], :] + n_dot * dt
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gating[ind_dict['n'], :][gating[ind_dict['n'], :] > 1.] = 1.
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gating[ind_dict['n'], :][gating[ind_dict['n'], :] < 0.] = 0.
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# Kv1.1 activation
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s_inf = (1 / (1 + np.exp((V_m[:, t - 1]- shift["s"] - slope_shift["s"] - b_param["s"][0]) / b_param["s"][1]))) ** \
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b_param["s"][2]
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temp = 36
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vBreak = -46.7
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amp1 = 52.7
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amp2 = 15.98
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vh1 = -49.87
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vh2 = -41.64
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offset = 0.9
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slope1 = 5
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slope2 = 24.99
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sigswitch = 1 / (1 + np.exp((V_m[:, t - 1]- shift["s"] - vBreak) / 3))
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sig1 = sigswitch * amp1 / (1 + np.exp((V_m[:, t - 1]- shift["s"] - vh1) / slope1))
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sig2 = (1 - sigswitch) * offset + (amp2 - offset) / (1 + np.exp((V_m[:, t - 1]- shift["s"] - vh2) / slope2))
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sTauFunc = sig1 + sig2
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s_tau_Q10 = (7.54 * np.exp(-(V_m[:, t - 1]- shift["s"]) / 379.7) * np.exp(-temp / 35.66)) ** ((temp - 25) / 10)
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tau_s = sTauFunc / s_tau_Q10
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s_dot = (s_inf - gating[ind_dict['s'], :]) * (1 / (tau_s * scale["s"]))
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gating[ind_dict['s'], :] = gating[ind_dict['s'], :] + s_dot * dt
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gating[ind_dict['s'], :][gating[ind_dict['s'], :] > 1.] = 1.
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gating[ind_dict['s'], :][gating[ind_dict['s'], :] < 0.] = 0.
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# Kv1.1 inactivation
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u_inf = ((1 - b_param["u"][3]) / (1 + np.exp((V_m[:, t - 1]- shift["u"] - slope_shift["u"] - b_param["u"][0])
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/ b_param["u"][1])) + b_param["u"][3]) ** b_param["u"][2]
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u_tau_Q10 = 2.7 ** ((temp - 25) / 10)
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tau_u = (86.86 + (408.78 / (1 + np.exp((V_m[:, t - 1] - shift["u"] + 13.6) / 7.46)))) / u_tau_Q10
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u_dot = (u_inf - gating[ind_dict['u'], :]) * (1 / (tau_u * scale["u"]))
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gating[ind_dict['u'], :] = gating[ind_dict['u'], :] + u_dot * dt
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gating[ind_dict['u'], :][gating[ind_dict['u'], :] > 1.] = 1.
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gating[ind_dict['u'], :][gating[ind_dict['u'], :] < 0.] = 0.
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# mutant Kv1.1 activation
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s_mut_inf = (1 / (1 + np.exp((V_m[:, t - 1]- shift["s_mut"] - slope_shift["s_mut"] - b_param["s_mut"][0]) / b_param["s_mut"][1]))) ** \
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b_param["s_mut"][2]
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s_mut_dot = (s_mut_inf - gating[ind_dict['s_mut'], :]) * (1 / (tau_s * scale["s_mut"]))
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gating[ind_dict['s_mut'], :] = gating[ind_dict['s_mut'], :] + s_mut_dot * dt
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gating[ind_dict['s_mut'], :][gating[ind_dict['s_mut'], :] > 1.] = 1.
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gating[ind_dict['s_mut'], :][gating[ind_dict['s_mut'], :] < 0.] = 0.
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# mutant Kv1.1 inactivation
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u_mut_inf = ((1 - b_param["u_mut"][3]) / (1 + np.exp((V_m[:, t - 1]- shift["u_mut"] - slope_shift["u_mut"] -
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b_param["u_mut"][0])/ b_param["u_mut"][1])) +
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b_param["u_mut"][3]) ** b_param["u_mut"][2]
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u_mut_dot = (u_mut_inf - gating[ind_dict['u_mut'], :]) * (1 / (tau_u * scale["u_mut"]))
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gating[ind_dict['u_mut'], :] = gating[ind_dict['u_mut'], :] + u_mut_dot * dt
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gating[ind_dict['u_mut'], :][gating[ind_dict['u_mut'], :] > 1.] = 1.
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gating[ind_dict['u_mut'], :][gating[ind_dict['u_mut'], :] < 0.] = 0.
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# M-type activation
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p_inf = (1 / (1 + np.exp((V_m[:, t - 1]- shift["p"] - slope_shift["p"] - b_param["p"][0]) / b_param["p"][1]))) ** \
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b_param["p"][2]
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tau_p = tau_max_p / (3.3 * np.exp((V_m[:, t - 1]- shift["p"] + 45) / 20) + np.exp((V_m[:, t - 1]- shift["p"] + 45) / -20))
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p_dot = (p_inf - gating[ind_dict['p'], :]) * (1 / (tau_p * scale["p"]))
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gating[ind_dict['p'], :] = gating[ind_dict['p'], :] + p_dot * dt
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gating[ind_dict['p'], :][gating[ind_dict['p'], :] > 1.] = 1.
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gating[ind_dict['p'], :][gating[ind_dict['p'], :] < 0.] = 0.
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# L-type Ca activation
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q_inf = (1 / (1 + np.exp((V_m[:, t - 1]- shift["q"] - slope_shift["q"] - b_param["q"][0]) / b_param["q"][1]))) ** \
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b_param["q"][2]
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alpha_q = (0.055 * (-27 - V_m[:, t - 1]- shift["q"])) / (np.exp((-27 - V_m[:, t - 1]- shift["q"]) / 3.8) - 1)
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beta_q = 0.94 * np.exp((-75 - V_m[:, t - 1]- shift["q"]) / 17)
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tau_q = 1 / (alpha_q + beta_q)
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q_dot = (q_inf - gating[ind_dict['q'], :]) * (1 / (tau_q * scale["q"]))
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gating[ind_dict['q'], :] = gating[ind_dict['q'], :] + q_dot * dt
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gating[ind_dict['q'], :][gating[ind_dict['q'], :] > 1.] = 1.
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gating[ind_dict['q'], :][gating[ind_dict['q'], :] < 0.] = 0.
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# L-type Ca inactivation
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r_inf = (1 / (1 + np.exp((V_m[:, t - 1]- shift["r"] - slope_shift["r"] - b_param["r"][0]) / b_param["r"][1]))) ** \
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b_param["r"][2]
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alpha_r = 0.000457 * np.exp((-13 - V_m[:, t - 1]- shift["r"]) / 50)
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beta_r = 0.0065 / (np.exp((-15 - V_m[:, t - 1]- shift["r"]) / 28) + 1)
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tau_r = 1 / (alpha_r + beta_r)
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r_dot = (r_inf - gating[ind_dict['r'], :]) * (1 / (tau_r * scale["r"]))
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gating[ind_dict['r'], :] = gating[ind_dict['r'], :] + r_dot * dt
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gating[ind_dict['r'], :][gating[ind_dict['r'], :] > 1.] = 1.
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gating[ind_dict['r'], :][gating[ind_dict['r'], :] < 0.] = 0.
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# update currents
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current[ind_dict['Leak'], :] = g["Leak"] * (V_m[:, t - 1]- E["Leak"]) * currents_included["Leak"]
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current[ind_dict['Na'], :] = g["Na"] * (gating[ind_dict['m'], :] ** 3) * gating[ind_dict['h'], :] * (V_m[:, t - 1]- E["Na"]) * \
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currents_included["Na"]
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current[ind_dict['Kd'], :] = g["Kd"] * (gating[ind_dict['n'], :] ** 4) * (V_m[:, t - 1]- E["K"]) * currents_included["Kd"]
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current[ind_dict['M'], :] = g["M"] * gating[ind_dict['p'], :] * (V_m[:, t - 1]- E["K"]) * currents_included["M"]
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current[ind_dict['Kv'], :] = g["Kv"] * gating[ind_dict['s'], :] * gating[ind_dict['u'], :] * (V_m[:, t - 1]- E["K"]) * currents_included["Kv"]
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current[ind_dict['Kv_mut'], :] = g["Kv_mut"] * gating[ind_dict['s_mut'], :] * gating[ind_dict['u_mut'], :] * (V_m[:, t - 1]- E["K"]) * \
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currents_included["Kv_mut"]
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current[ind_dict['L'], :] = g["L"] * ((gating[ind_dict['q'], :]) ** 2) * gating[ind_dict['r'], :] * (V_m[:, t - 1]- E["Ca"]) * \
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currents_included["L"]
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# update dV/dt
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V_dot = (1 / C) * (I_in[t, :] - (current[ind_dict['Leak'], :] + current[ind_dict['Na'], :] +
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current[ind_dict['Kd'], :] + current[ind_dict['M'], :] +
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current[ind_dict['Kv'], :] + current[ind_dict['Kv_mut'], :]
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+ current[ind_dict['L'], :]))
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# update V_m
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V_m[:, t] = V_m[:, t - 1] + V_dot * dt
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t += 1
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return V_m
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def Pospischil_mut(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
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b_param, slope_shift, gating, current, prominence, desired_AUC_width, mutations, mut, folder,
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high, low, number_steps, initial_period, sec):
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""" Simulate mutations for Pospischil models
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Parameters
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----------
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V_init : float
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Initial membrane potential
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V_T : float
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Threshold potential
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g : dict
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maximal conductance for currents
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E : dict
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Reversal potentials for currents
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I_in : array
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Current stimulus input to model
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dt : float
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fixed time step
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currents_included : dict
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Boolean to include current in simulation or not
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stim_time :
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length of stimulus ()
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stim_num : int
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number of different stimulus currents
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C : float
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membrane capacitance
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tau_max_p : float
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maximal adaptation (M-current) time constant
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shift : dict
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for each gating parameter
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scale : dict
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for each gating parameter
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b_param : dict
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Boltzmann parameter values for each gating parameter
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slope_shift : dict
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for each gating parameter
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gating : array
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array of gating values over time
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current : array
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array of current values over time
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prominence : float
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required spike prominence
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desired_AUC_width : float
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width of AUC from rheobase to rheobase+desired_AUC_width
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mutations : dict
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dictionary of mutation effects
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mut : str
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mutation
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folder : str
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folder to save hdf5 file in
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high : float
|
|
upper current step magnitude
|
|
low : float
|
|
lowest current step magnitude
|
|
number_steps : int
|
|
number of current steps between low and high
|
|
initial_period : float
|
|
length of I = 0 before current step
|
|
sec : float
|
|
length of current step in seconds
|
|
wildtype : bool
|
|
if wildtype model
|
|
|
|
Returns
|
|
-------
|
|
saves mut.hdf5 file to folder
|
|
"""
|
|
# setup for simulation
|
|
g_multi = copy.deepcopy(g)
|
|
E_multi = copy.deepcopy(E)
|
|
b_param_multi = copy.deepcopy(b_param)
|
|
shift_multi = copy.deepcopy(shift)
|
|
scale_multi = copy.deepcopy(scale)
|
|
slope_shift_multi = copy.deepcopy(slope_shift)
|
|
currents_included_multi = copy.deepcopy(currents_included)
|
|
gating2 = copy.deepcopy(gating)
|
|
current2 = copy.deepcopy(current)
|
|
|
|
g_effect = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in g_multi.items():
|
|
g_effect[k] = v
|
|
b_param_effect = Dict.empty(key_type=types.unicode_type, value_type=types.float64[:], )
|
|
for k, v in b_param_multi.items():
|
|
b_param_effect[k] = np.array(v)
|
|
shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in shift_multi.items():
|
|
shift2[k] = v
|
|
scale2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in scale_multi.items():
|
|
scale2[k] = v
|
|
slope_shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in slope_shift_multi.items():
|
|
slope_shift2[k] = v
|
|
E2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in E_multi.items():
|
|
E2[k] = v
|
|
currents_included2 = Dict.empty(key_type=types.unicode_type, value_type=types.boolean, )
|
|
for k, v in currents_included_multi.items():
|
|
currents_included2[k] = v
|
|
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
|
|
i = 0
|
|
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
|
|
ind_dict[var] = i
|
|
i += 1
|
|
i = 0
|
|
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
|
|
ind_dict[var] = i
|
|
i += 1
|
|
|
|
# effect of mutation
|
|
g_effect['Kv_mut'] = g['Kv_mut'] * mutations[mut]['g_ratio']
|
|
b_param_effect['s_mut'][0] = b_param_effect['s_mut'][0] + mutations[mut]['activation_Vhalf_diff']
|
|
b_param_effect['s_mut'][1] = b_param_effect['s_mut'][1] * mutations[mut]['activation_k_ratio']
|
|
#########################################
|
|
|
|
# initial simulation for current range from low to high
|
|
stim_start = int(initial_period * 1 / dt)
|
|
V_m = Pospischil(V_init * np.ones((stim_num)), V_T, g_effect, E2, I_in, dt, currents_included2, stim_time, stim_num,
|
|
C, tau_max_p, shift2, scale2, b_param_effect, slope_shift2, gating2, current2, ind_dict)
|
|
# g2, b_param2,
|
|
# create hdf5 file for the mutation and save data and metadata
|
|
fname = os.path.join(folder, "{}.hdf5".format(mut.replace(" ", "_")))
|
|
with h5py.File(fname, "a") as f:
|
|
data = f.create_group("data")
|
|
data.create_dataset('V_m', data=V_m, dtype='float64', compression="gzip", compression_opts=9)
|
|
min_spike_height = V_m[0, stim_start] + prominence
|
|
metadata = {'I_high': high, 'I_low': low, 'stim_num': number_steps, 'stim_time': stim_time, 'sec': sec,
|
|
'dt': dt, 'initial_period': initial_period, 'g': json.dumps(dict(g_effect)),
|
|
'E': json.dumps(dict(E2)), 'C': C, 'V_T': V_T, 'V_init': V_init, 'tau_max_p': tau_max_p,
|
|
'ind_dict': json.dumps(dict(ind_dict)), 'b_param': json.dumps(dict(b_param_effect), cls=NumpyEncoder),
|
|
'currents': json.dumps(dict(currents_included2)), 'shift': json.dumps(dict(shift2)),
|
|
'scale': json.dumps(dict(scale2)), 'slope_shift': json.dumps(dict(slope_shift2)),
|
|
'prominence': prominence, 'desired_AUC_width': desired_AUC_width, 'mut': str(mut),
|
|
'min_spike_height': min_spike_height,}
|
|
data.attrs.update(metadata)
|
|
|
|
# firing frequency analysis and rheobase
|
|
I_mag = np.arange(low, high, (high - low) / stim_num)
|
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
|
with h5py.File(fname, "r+") as f:
|
|
analysis = f.create_group("analysis")
|
|
for i in np.array(['spike_times', 'spike_times_rheo', 'ISI', 'Freq', 'amplitude']):
|
|
analysis.create_dataset(i, (I_mag.shape[0],), dtype=dtyp, compression="gzip", compression_opts=9)
|
|
analysis.create_dataset('F_inf', (I_mag.shape[0],), dtype='float64')
|
|
analysis.create_dataset('F_null', (I_mag.shape[0],), dtype='float64')
|
|
analysis.create_dataset('AP', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
|
analysis.create_dataset('AP_rheo', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
|
analysis.create_dataset('rheobase', (1,), dtype='float64', compression="gzip", compression_opts=9)
|
|
try:
|
|
for stim in range(I_mag.shape[0]):
|
|
# spike detection
|
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m[stim, stim_start:], min_spike_height,
|
|
prominence=prominence, distance=1 * 1 / dt)
|
|
analysis['spike_times'][stim] = ap_all * dt
|
|
if analysis['spike_times'][stim].size == 0:
|
|
analysis['AP'][stim] = False
|
|
else:
|
|
analysis['AP'][stim] = True
|
|
analysis['amplitude'][stim] = np.array(ap_prop["peak_heights"])
|
|
analysis['ISI'][stim] = np.array([x - analysis['spike_times'][stim][i - 1] if i else None for i, x in
|
|
enumerate(analysis['spike_times'][stim])][1:]) # msec
|
|
analysis['Freq'][stim] = 1 / (analysis['ISI'][stim] / 1000)
|
|
|
|
if analysis['Freq'][stim].size != 0:
|
|
analysis['F_null'][stim] = analysis['Freq'][stim][0]
|
|
last_second_spikes = analysis['spike_times'][stim] > np.int(
|
|
f['data'].attrs['stim_time'] * dt - (500 + initial_period)) # spikes in last second
|
|
if np.any(last_second_spikes == True):
|
|
last_second_spikes_1 = np.delete(last_second_spikes, 0) # remove first spike because it has no ISI/freq
|
|
analysis['F_inf'][stim] = np.mean(analysis['Freq'][stim][last_second_spikes_1])
|
|
else: # if no ISI detected
|
|
analysis['F_null'][stim] = 0
|
|
analysis['F_inf'][stim] = 0
|
|
except:
|
|
print('Freq analysis unsuccessful')
|
|
|
|
# rheobase
|
|
# find where the first AP occurs
|
|
with h5py.File(fname, "r+") as f:
|
|
if np.any(f['analysis']['AP'][:] == True):
|
|
# setup current range to be between the current step before and with first AP
|
|
I_first_spike = I_mag[np.argwhere(f['analysis']['AP'][:] == True)[0][0]]
|
|
I_before_spike = I_mag[np.argwhere(f['analysis']['AP'][:] == True)[0][0] - 1]
|
|
number_steps_rheo = 100
|
|
stim_time, I_in_rheo, stim_num_rheo, V_m_rheo = stimulus_init(I_before_spike, I_first_spike, number_steps_rheo,
|
|
initial_period, dt, sec)
|
|
|
|
# simulate this current step for better rheobase resolution
|
|
V_m_rheo = Pospischil(V_init * np.ones((stim_num_rheo)), V_T, g_effect, E2, I_in_rheo, dt,
|
|
currents_included2, stim_time, stim_num_rheo, C, tau_max_p, shift2, scale2,
|
|
b_param_effect, slope_shift2, np.zeros((len(b_param), stim_num_rheo)),
|
|
np.zeros((len(currents_included), stim_num_rheo)), ind_dict)
|
|
|
|
f['analysis'].create_dataset('I_first_spike', data=I_first_spike, dtype='float64')
|
|
f['analysis'].create_dataset('I_before_spike', data=I_before_spike, dtype='float64')
|
|
f['data'].create_dataset('V_m_rheo', data=V_m_rheo, dtype='float64', compression="gzip", compression_opts=9)
|
|
|
|
# run AP detection
|
|
for stim in range(I_in_rheo.shape[1]):
|
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_rheo[stim, stim_start:], min_spike_height,
|
|
prominence=prominence, distance=1 * 1 / dt)
|
|
f['analysis']['spike_times_rheo'][stim] = ap_all * dt
|
|
if f['analysis']['spike_times_rheo'][stim].size == 0:
|
|
f['analysis']['AP_rheo'][stim] = False
|
|
else:
|
|
f['analysis']['AP_rheo'][stim] = True
|
|
# find rheobase as smallest current step to elicit AP and save
|
|
f['analysis']['rheobase'][:] = I_in_rheo[-1, np.argwhere(f['analysis']['AP_rheo'][:] == True)[0][0]] * 1000
|
|
|
|
# AUC
|
|
# find rheobase of steady state firing (F_inf)
|
|
with h5py.File(fname, "a") as f:
|
|
F_inf = f['analysis']['F_inf'][:]
|
|
try:
|
|
# setup current range to be in current step between no and start of steady state firing
|
|
F_inf_start_ind = np.argwhere(F_inf != 0)[0][0]
|
|
dI = (high- low) / stim_num
|
|
I_start = I_mag[F_inf_start_ind - 1]
|
|
I_end = I_mag[F_inf_start_ind]
|
|
I_rel = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
|
I_rel = np.reshape(I_rel, (1, I_rel.shape[0]))
|
|
I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel
|
|
I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel
|
|
stim_num = I_in.shape[1]
|
|
|
|
# simulate response to this current step range
|
|
V_m_inf_rheo = Pospischil(V_init, V_T, g_effect, E2, I_in, dt, currents_included2, stim_time, stim_num, C,
|
|
tau_max_p, shift2, scale2, b_param_effect, slope_shift2,
|
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
|
ind_dict)
|
|
# save response, analyse and save
|
|
for i in np.array(['spike_times_Finf', 'ISI_Finf', 'Freq_Finf']):
|
|
f['analysis'].require_dataset(i, shape=(I_rel.shape[1],), dtype=dtyp, compression="gzip", compression_opts=9)
|
|
f['analysis'].require_dataset('F_inf_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
|
f['analysis'].require_dataset('F_null_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
|
for stim in range(I_rel.shape[1]):
|
|
# spike peak detection
|
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_inf_rheo[stim, stim_start:], min_spike_height,
|
|
prominence=prominence, distance=1 * 1 / dt)
|
|
f['analysis']['spike_times_Finf'][stim] = ap_all * dt
|
|
f['analysis']['ISI_Finf'][stim] = np.array(
|
|
[x - f['analysis']['spike_times_Finf'][stim][i - 1] if i else None for i, x in
|
|
enumerate(f['analysis']['spike_times_Finf'][stim])][1:]) # msec
|
|
f['analysis']['Freq_Finf'][stim] = 1 / (f['analysis']['ISI_Finf'][stim] / 1000)
|
|
if f['analysis']['Freq_Finf'][stim].size != 0:
|
|
last_second_spikes = f['analysis']['spike_times_Finf'][stim] > np.int(
|
|
f['data'].attrs['stim_time'] * dt - (500 + initial_period))
|
|
if np.any(last_second_spikes == True):
|
|
last_second_spikes_1 = np.delete(last_second_spikes, 0) # remove first spike because it has no ISI/freq
|
|
f['analysis']['F_inf_Finf'][stim] = np.mean(
|
|
f['analysis']['Freq_Finf'][stim][last_second_spikes_1])
|
|
else:
|
|
f['analysis']['F_null_Finf'][stim] = 0
|
|
f['analysis']['F_inf_Finf'][stim] = 0
|
|
# find AUC relative to F_inf rheobase
|
|
# setup current inputs with current range around F_inf rheobase
|
|
I_first_inf = I_rel[0, np.argwhere(f['analysis']['F_inf_Finf'][:] != 0)[0][0]]
|
|
I_start = I_first_inf - 0.00005
|
|
I_end = I_first_inf + desired_AUC_width * high
|
|
I_rel2 = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
|
I_rel2 = np.reshape(I_rel2, (1, I_rel2.shape[0]))
|
|
I_in2 = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel2
|
|
I_in2[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel2
|
|
stim_num = I_in2.shape[1]
|
|
# simulate response to this current step range
|
|
V_m_AUC = Pospischil(V_init* np.ones(stim_num), V_T, g_effect, E2, I_in2, dt, currents_included2, stim_time, stim_num, C,
|
|
tau_max_p, shift2, scale2, b_param_effect, slope_shift2,
|
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
|
ind_dict)
|
|
# save data and analysis for this current step range including AUC
|
|
f['data'].require_dataset('V_m_AUC', shape=V_m_AUC.shape, dtype='float64')
|
|
f['data']['V_m_AUC'][:] = V_m_AUC
|
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
|
for i in np.array(['spike_times_Finf_AUC', 'ISI_Finf_AUC', 'Freq_Finf_AUC']): # analysis_names:
|
|
f['analysis'].require_dataset(i, shape=(I_rel2.shape[1],), dtype=dtyp, compression="gzip",
|
|
compression_opts=9)
|
|
f['analysis'].require_dataset('F_inf_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
|
f['analysis'].require_dataset('F_null_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
|
for stim in range(I_rel2.shape[1]):
|
|
# spike peak detection
|
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_AUC[stim, stim_start:], min_spike_height,
|
|
prominence=prominence, distance=1 * 1 / dt)
|
|
f['analysis']['spike_times_Finf_AUC'][stim] = ap_all * dt
|
|
f['analysis']['ISI_Finf_AUC'][stim] = np.array(
|
|
[x - f['analysis']['spike_times_Finf_AUC'][stim][i - 1] if i else None for i, x in
|
|
enumerate(f['analysis']['spike_times_Finf_AUC'][stim])][1:]) # msec
|
|
f['analysis']['Freq_Finf_AUC'][stim] = 1 / (f['analysis']['ISI_Finf_AUC'][stim] / 1000)
|
|
if f['analysis']['Freq_Finf_AUC'][stim].size != 0:
|
|
last_second_spikes = f['analysis']['spike_times_Finf_AUC'][stim] > np.int(
|
|
f['data'].attrs['stim_time'] * dt - (500 + f['data'].attrs['initial_period']))
|
|
if np.any(last_second_spikes == True):
|
|
last_second_spikes_1 = np.delete(last_second_spikes, 0) # remove first spike because it has no ISI/freq
|
|
f['analysis']['F_inf_Finf_AUC'][stim] = np.mean(
|
|
f['analysis']['Freq_Finf_AUC'][stim][last_second_spikes_1])
|
|
else:
|
|
f['analysis']['F_null_Finf_AUC'][stim] = 0
|
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
|
f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip",
|
|
compression_opts=9)
|
|
f['analysis']['AUC'][:] = np.trapz(f['analysis']['F_inf_Finf_AUC'][:],
|
|
I_rel2[:] * 1000, dI * 1000)
|
|
except:
|
|
f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip",
|
|
compression_opts=9)
|
|
f['analysis']['AUC'][:] = 0
|
|
|
|
# ramp protocol #####################
|
|
# setup ramp current
|
|
sec = 4
|
|
ramp_len = np.int(sec *1000 *1/dt)
|
|
stim_time = ramp_len *2
|
|
I_amp = np.array([0, -0.000001])
|
|
I_amp = np.reshape(I_amp, (1,I_amp.shape[0]))
|
|
I_step = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1))@ I_amp
|
|
I_step[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_amp
|
|
stim_num_step = I_step.shape[1]
|
|
start = np.int(initial_period * 1 / dt)
|
|
I_step[start:np.int(start+ramp_len),0] = np.linspace(0, high, ramp_len)
|
|
I_step[np.int(start + ramp_len):np.int(start + ramp_len*2),0] = np.linspace(high, 0, ramp_len)
|
|
stim_time_step = I_step.shape[0]
|
|
|
|
# simulate response to ramp
|
|
V_m_ramp = Pospischil(-70 * np.ones(stim_num_step), V_T, g_effect, E2, I_step, dt, currents_included2,
|
|
stim_time_step, stim_num_step, C, tau_max_p, shift2, scale2, b_param_effect, slope_shift2,
|
|
np.zeros((len(b_param), stim_num_step)), np.zeros((len(currents_included), stim_num_step)),
|
|
ind_dict)
|
|
|
|
# save ramp firing properties
|
|
with h5py.File(fname, "a") as f:
|
|
f['data'].create_dataset('V_m_ramp', data=V_m_ramp, dtype='float64', compression="gzip", compression_opts=9)
|
|
# spike peak detection
|
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_ramp[0, stim_start:], min_spike_height,
|
|
prominence=prominence, distance=1 * 1 / dt)
|
|
if ap_all.shape[0] != 0:
|
|
if ap_all[0] < stim_start + ramp_len:
|
|
f['analysis'].create_dataset('ramp_I_up', data =I_step[stim_start+ap_all[0], 0] *1000, dtype='float64')
|
|
else:
|
|
f['analysis'].create_dataset('ramp_I_up', data =np.NaN, dtype='float64')
|
|
if ap_all[-1] > stim_start + ramp_len:
|
|
f['analysis'].create_dataset('ramp_I_down', data =I_step[stim_start+ap_all[-1], 0]*1000, dtype='float64')
|
|
else:
|
|
f['analysis'].create_dataset('ramp_I_down', data =np.NaN, dtype='float64')
|
|
f['analysis'].create_dataset('hysteresis', data =f['analysis']['ramp_I_down'][()] - f['analysis']['ramp_I_up'][()], dtype='float64')
|
|
else: # if no spikes in response to ramp
|
|
f['analysis'].create_dataset('ramp_I_up', data =np.NaN, dtype='float64')
|
|
f['analysis'].create_dataset('ramp_I_down', data =np.NaN, dtype='float64')
|
|
f['analysis'].create_dataset('hysteresis', data =np.NaN, dtype='float64')
|
|
if f.__bool__():
|
|
f.close()
|
|
gc.collect()
|
|
|
|
|
|
def SA_Pospischil(V_init, V_T, g, E, I_in, dt, currents_included, stim_time, stim_num, C, tau_max_p, shift, scale,
|
|
b_param, slope_shift, gating, current, prominence, desired_AUC_width, folder, high, low,
|
|
number_steps, initial_period, sec, lin_array, log_array, alt_types, alt_ind, alt):
|
|
""" Sensitivity analysis for Pospischil models
|
|
|
|
Parameters
|
|
----------
|
|
V_init : float
|
|
Initial membrane potential
|
|
V_T : float
|
|
Threshold potential
|
|
g : dict
|
|
maximal conductance for currents
|
|
E : dict
|
|
Reversal potentials for currents
|
|
I_in : array
|
|
Current stimulus input to model
|
|
dt : float
|
|
fixed time step
|
|
currents_included : dict
|
|
Boolean to include current in simulation or not
|
|
stim_time :
|
|
length of stimulus ()
|
|
stim_num : int
|
|
number of different stimulus currents
|
|
C : float
|
|
membrane capacitance
|
|
tau_max_p : float
|
|
maximal adaptation (M-current) time constant
|
|
shift : dict
|
|
for each gating parameter
|
|
scale : dict
|
|
for each gating parameter
|
|
b_param : dict
|
|
Boltzmann parameter values for each gating parameter
|
|
slope_shift : dict
|
|
for each gating parameter
|
|
gating : array
|
|
array of gating values over time
|
|
current : array
|
|
array of current values over time
|
|
prominence: float
|
|
required spike prominence
|
|
desired_AUC_width: float
|
|
width of AUC from rheobase to rheobase + desired_AUC_width
|
|
folder : str
|
|
folder to save hdf5 in
|
|
high : float
|
|
upper current step magnitude
|
|
low : float
|
|
lowest current step magnitude
|
|
number_steps : int
|
|
number of current steps between low and high
|
|
initial_period: float
|
|
length of I = 0 before current step
|
|
sec : float
|
|
length of current step in seconds
|
|
lin_array : array
|
|
array of linear shifts from -10 tp 10
|
|
log_array : array
|
|
array of log2 values from -1 to 1
|
|
alt_types : array
|
|
array of arrays of strings of [variable, alteration type]
|
|
alt_ind : int
|
|
index for alt_type
|
|
alt :
|
|
index for lin_array of log_array
|
|
|
|
Returns
|
|
-------
|
|
saves hdf5 file to folder
|
|
"""
|
|
|
|
|
|
# setup for simulation
|
|
g_multi = copy.deepcopy(g)
|
|
E_multi = copy.deepcopy(E)
|
|
b_param_multi = copy.deepcopy(b_param)
|
|
shift_multi = copy.deepcopy(shift)
|
|
scale_multi = copy.deepcopy(scale)
|
|
slope_shift_multi = copy.deepcopy(slope_shift)
|
|
currents_included_multi = copy.deepcopy(currents_included)
|
|
gating2 = copy.deepcopy(gating)
|
|
current2 = copy.deepcopy(current)
|
|
|
|
g2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in g_multi.items():
|
|
g2[k] = v
|
|
b_param2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64[:], )
|
|
for k, v in b_param_multi.items():
|
|
b_param2[k] = np.array(v)
|
|
shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in shift_multi.items():
|
|
shift2[k] = v
|
|
scale2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in scale_multi.items():
|
|
scale2[k] = v
|
|
slope_shift2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in slope_shift_multi.items():
|
|
slope_shift2[k] = v
|
|
E2 = Dict.empty(key_type=types.unicode_type, value_type=types.float64, )
|
|
for k, v in E_multi.items():
|
|
E2[k] = v
|
|
currents_included2 = Dict.empty(key_type=types.unicode_type, value_type=types.boolean, )
|
|
for k, v in currents_included_multi.items():
|
|
currents_included2[k] = v
|
|
ind_dict = Dict.empty(key_type=types.unicode_type, value_type=types.int64, )
|
|
i = 0
|
|
for var in np.array(['m', 'h', 'n', 'q', 'r', 'p', 's', 'u', 's_mut', 'u_mut']):
|
|
ind_dict[var] = i
|
|
i += 1
|
|
i = 0
|
|
for var in np.array(['Na', 'Kd', 'M', 'Kv', 'Kv_mut', 'L', 'Leak']):
|
|
ind_dict[var] = i
|
|
i += 1
|
|
|
|
#### alteration #######################################################################
|
|
if alt_types[alt_ind, 1] == 'shift':
|
|
shift2[alt_types[alt_ind, 0]] = lin_array[alt]
|
|
fname = os.path.join(folder, "{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0], lin_array[alt]))
|
|
|
|
elif alt_types[alt_ind, 1] == 'slope':
|
|
b_param2[alt_types[alt_ind, 0]][1] = b_param2[alt_types[alt_ind, 0]][1] * log_array[alt]
|
|
fname = os.path.join(folder, "{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0],
|
|
np.int(np.around(log_array[alt], 2) * 100)))
|
|
if b_param2[alt_types[alt_ind, 0]][2] != 1:
|
|
# adjust slope_shift to account for slope not changing (rotating) around 0.5
|
|
V_test = np.arange(-150, 100, 0.001)
|
|
if b_param2[alt_types[alt_ind, 0]].shape[0] == 4:
|
|
steady_state = ((1 - b_param2[alt_types[alt_ind, 0]][3]) / (
|
|
1 + np.exp((V_test - b_param2[alt_types[alt_ind, 0]][0]) / b_param2[alt_types[alt_ind, 0]][1])) +
|
|
b_param2[alt_types[alt_ind, 0]][3]) ** b_param2[alt_types[alt_ind, 0]][2]
|
|
orig = ((1 - b_param[alt_types[alt_ind, 0]][3]) / (
|
|
1 + np.exp((V_test - b_param[alt_types[alt_ind, 0]][0]) / b_param[alt_types[alt_ind, 0]][1])) +
|
|
b_param[alt_types[alt_ind, 0]][3]) ** b_param[alt_types[alt_ind, 0]][2]
|
|
else:
|
|
steady_state = (1 / (1 + np.exp(
|
|
(V_test - b_param2[alt_types[alt_ind, 0]][0]) / b_param2[alt_types[alt_ind, 0]][1]))) ** \
|
|
b_param2[alt_types[alt_ind, 0]][2]
|
|
orig = (1 / (1 + np.exp(
|
|
(V_test - b_param[alt_types[alt_ind, 0]][0]) / b_param[alt_types[alt_ind, 0]][1]))) ** \
|
|
b_param[alt_types[alt_ind, 0]][2]
|
|
orig_V_half = V_test[(np.abs(orig - 0.5)).argmin()]
|
|
V_half_new = V_test[(np.abs(steady_state - 0.5)).argmin()]
|
|
slope_shift2[alt_types[alt_ind, 0]] = orig_V_half - V_half_new
|
|
|
|
elif alt_types[alt_ind, 1] == 'g':
|
|
g2[alt_types[alt_ind, 0]] = g2[alt_types[alt_ind, 0]] * log_array[alt]
|
|
fname = os.path.join(folder, "{}_{}_{}.hdf5".format(alt_types[alt_ind, 1], alt_types[alt_ind, 0],
|
|
np.int(np.around(log_array[alt], 2) * 100)))
|
|
###########################################################################
|
|
|
|
# initial simulation for current range from low to high
|
|
V_m = Pospischil(V_init * np.ones((stim_num)), V_T, g2, E2, I_in, dt, currents_included2, stim_time, stim_num, C,
|
|
tau_max_p, shift2, scale2, b_param2, slope_shift2, gating2, current2, ind_dict)
|
|
|
|
stim_start = np.int(initial_period * 1 / dt)
|
|
min_spike_height = V_m[0, stim_start] + prominence
|
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
|
|
|
# create hdf5 file and save data and metadata
|
|
with h5py.File(fname, "a") as f:
|
|
data = f.create_group("data")
|
|
data.create_dataset('V_m', data=V_m, dtype='float64', compression="gzip", compression_opts=9)
|
|
metadata = {'I_high': high, 'I_low': low, 'stim_num': number_steps, 'stim_time': stim_time, 'sec': sec,
|
|
'dt': dt, 'initial_period': initial_period, 'g': json.dumps(dict(g2)), 'E': json.dumps(dict(E2)),
|
|
'C': C, 'V_T': V_T, 'V_init': V_init, 'tau_max_p': tau_max_p, 'ind_dict': json.dumps(dict(ind_dict)),
|
|
'b_param': json.dumps(dict(b_param2), cls=NumpyEncoder), 'currents': json.dumps(dict(currents_included2)),
|
|
'shift': json.dumps(dict(shift2)), 'var_change': str(alt_types[alt_ind, 1]),
|
|
'var': str(alt_types[alt_ind, 0]), 'scale': json.dumps(dict(scale2)),
|
|
'slope_shift': json.dumps(dict(slope_shift2)), 'prominence': prominence,
|
|
'desired_AUC_width': desired_AUC_width, 'alteration_info': str(alt_types[alt_ind, :]),
|
|
'alt_index': str(alt),'min_spike_height': min_spike_height,
|
|
}
|
|
data.attrs.update(metadata)
|
|
if alt_types[alt_ind, 1] == 'shift':
|
|
meta2 = {'alteration': lin_array[alt]}
|
|
else:
|
|
meta2 = {'alteration': log_array[alt]}
|
|
data.attrs.update(meta2)
|
|
|
|
# firing frequency analysis and rheobase
|
|
I_mag = np.arange(low, high, (high - low) /stim_num)
|
|
with h5py.File(fname, "r+") as f:
|
|
analysis = f.create_group("analysis")
|
|
for i in np.array(['spike_times', 'spike_times_rheo', 'ISI', 'Freq']):
|
|
analysis.create_dataset(i, (I_mag.shape[0],), dtype=dtyp, compression="gzip", compression_opts=9)
|
|
analysis.create_dataset('F_inf', (I_mag.shape[0],), dtype='float64')
|
|
analysis.create_dataset('F_null', (I_mag.shape[0],), dtype='float64')
|
|
analysis.create_dataset('AP', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
|
analysis.create_dataset('AP_rheo', (I_mag.shape[0],), dtype='bool', compression="gzip", compression_opts=9)
|
|
for i in np.array(['rheobase']):
|
|
analysis.create_dataset(i, (1,), dtype='float64', compression="gzip", compression_opts=9)
|
|
try:
|
|
for stim in range(I_mag.shape[0]):
|
|
# spike detection
|
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m[stim, stim_start:], min_spike_height,
|
|
prominence=prominence, distance=1 * 1 / dt)
|
|
analysis['spike_times'][stim] = ap_all * dt
|
|
if analysis['spike_times'][stim].size == 0:
|
|
analysis['AP'][stim] = False
|
|
else:
|
|
analysis['AP'][stim] = True
|
|
analysis['ISI'][stim] = np.array([x - analysis['spike_times'][stim][i - 1] if i else None for i, x in
|
|
enumerate(analysis['spike_times'][stim])][1:]) # msec
|
|
analysis['Freq'][stim] = 1 / (analysis['ISI'][stim] / 1000)
|
|
|
|
if analysis['Freq'][stim].size != 0:
|
|
analysis['F_null'][stim] = analysis['Freq'][stim][0]
|
|
if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0: # if first spike is after 1 ms
|
|
half_second_spikes = np.logical_and(analysis['spike_times'][stim] < np.int(
|
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
|
|
analysis['spike_times'][stim] > np.int(
|
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][
|
|
0]] * dt)) # spikes in half second window
|
|
if np.sum(half_second_spikes == True) > 1:
|
|
half_second_spikes_1 = np.delete(half_second_spikes,
|
|
np.argwhere(half_second_spikes == True)[0][0])
|
|
analysis['F_inf'][stim] = np.mean(analysis['Freq'][stim][half_second_spikes_1])
|
|
else:
|
|
analysis['F_inf'][stim] = 0
|
|
else:
|
|
analysis['F_inf'][stim] = 0
|
|
else: # if no ISI detected
|
|
analysis['F_null'][stim] = 0
|
|
analysis['F_inf'][stim] = 0
|
|
|
|
except:
|
|
print(alt_types[alt_ind, 0], alt_types[alt_ind, 1], alt, 'Freq analysis unsuccessful')
|
|
|
|
# rheobase
|
|
# find where the first AP occurs
|
|
if np.any(analysis['AP'][:] == True):
|
|
# setup current range to be between the current step before and with first AP
|
|
I_first_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0]]
|
|
I_before_spike = I_mag[np.argwhere(analysis['AP'][:] == True)[0][0] - 1]
|
|
analysis.create_dataset('I_first_spike', data=I_first_spike, dtype='float64')
|
|
analysis.create_dataset('I_before_spike', data=I_before_spike, dtype='float64')
|
|
number_steps_rheo = 100
|
|
stim_time, I_in_rheo, stim_num_rheo, V_m_rheo = stimulus_init(I_before_spike, I_first_spike,
|
|
number_steps_rheo,
|
|
initial_period, dt, sec)
|
|
|
|
# simulate response to this current range and save
|
|
V_m_rheo = Pospischil(V_init * np.ones((stim_num_rheo)), V_T, g2, E2, I_in_rheo, dt, currents_included2,
|
|
stim_time, stim_num_rheo, C, tau_max_p, shift2, scale2, b_param2, slope_shift2,
|
|
np.zeros((len(b_param), stim_num_rheo)),
|
|
np.zeros((len(currents_included), stim_num_rheo)), ind_dict)
|
|
# run AP detection
|
|
try:
|
|
stim_start = np.int(initial_period * 1 / dt)
|
|
for stim in range(I_in_rheo.shape[1]):
|
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_rheo[stim, stim_start:], min_spike_height,
|
|
prominence=prominence, distance=1 * 1 / dt)
|
|
analysis['spike_times_rheo'][stim] = ap_all * dt
|
|
if analysis['spike_times_rheo'][stim].size == 0:
|
|
analysis['AP_rheo'][stim] = False
|
|
else:
|
|
analysis['AP_rheo'][stim] = True
|
|
# find rheobase as smallest current step to elicit AP and save
|
|
analysis['rheobase'][:] = I_in_rheo[-1, np.argwhere(analysis['AP_rheo'][:] == True)[0][0]] * 1000
|
|
except:
|
|
print(alt_types[alt_ind, 0], alt_types[alt_ind, 1], alt, 'RHEOBASE NO AP')
|
|
|
|
# AUC
|
|
# find rheobase of steady state firing (F_inf)
|
|
with h5py.File(fname, "a") as f:
|
|
F_inf = f['analysis']['F_inf'][:]
|
|
try:
|
|
# setup current range to be in current step between no and start of steady state firing
|
|
|
|
F_inf_start_ind = np.argwhere(F_inf != 0)[0][0]
|
|
dI = (high - low) / stim_num
|
|
I_start = I_mag[F_inf_start_ind - 1]
|
|
I_end = I_mag[F_inf_start_ind]
|
|
I_rel = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
|
I_rel = np.reshape(I_rel, (1, I_rel.shape[0]))
|
|
I_in = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel
|
|
I_in[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel
|
|
stim_num = I_in.shape[1]
|
|
|
|
# simulate response to this current step range
|
|
V_m_inf_rheo = Pospischil(V_init * np.ones(stim_num), V_T, g2, E2, I_in, dt, currents_included2, stim_time,
|
|
stim_num, C, tau_max_p, shift2, scale2, b_param2, slope_shift2,
|
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
|
ind_dict)
|
|
|
|
# save response, analyse and save
|
|
for i in np.array(['spike_times_Finf', 'ISI_Finf', 'Freq_Finf']):
|
|
f['analysis'].require_dataset(i, shape=(I_rel.shape[1],), dtype=dtyp, compression="gzip",
|
|
compression_opts=9)
|
|
f['analysis'].require_dataset('F_inf_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
|
f['analysis'].require_dataset('F_null_Finf', shape=(I_rel.shape[1],), dtype='float64')
|
|
for stim in range(I_rel.shape[1]):
|
|
# spike peak detection
|
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_inf_rheo[stim, stim_start:], min_spike_height,
|
|
prominence=prominence, distance=1 * 1 / dt)
|
|
f['analysis']['spike_times_Finf'][stim] = ap_all * dt
|
|
f['analysis']['ISI_Finf'][stim] = np.array(
|
|
[x - f['analysis']['spike_times_Finf'][stim][i - 1] if i else None for i, x in
|
|
enumerate(f['analysis']['spike_times_Finf'][stim])][1:]) # msec
|
|
f['analysis']['Freq_Finf'][stim] = 1 / (f['analysis']['ISI_Finf'][stim] / 1000)
|
|
if f['analysis']['Freq_Finf'][stim].size != 0:
|
|
if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0: # if first spike is after 1 ms
|
|
half_second_spikes = np.logical_and(f['analysis']['spike_times_Finf'][stim] < np.int(
|
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
|
|
f['analysis']['spike_times_Finf'][stim] > np.int(
|
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt)) # spikes in half second window
|
|
if np.sum(half_second_spikes == True) > 1:
|
|
half_second_spikes_1 = np.delete(half_second_spikes,
|
|
np.argwhere(half_second_spikes == True)[0][0]) # remove first spike because it has no ISI/freq
|
|
f['analysis']['F_inf_Finf'][stim] = np.mean(
|
|
f['analysis']['Freq_Finf'][stim][half_second_spikes_1])
|
|
else:
|
|
f['analysis']['F_inf_Finf'][stim] = 0
|
|
else:
|
|
f['analysis']['F_null_Finf'][stim] = 0
|
|
f['analysis']['F_inf_Finf'][stim] = 0
|
|
else:
|
|
f['analysis']['F_null_Finf'][stim] = 0
|
|
f['analysis']['F_inf_Finf'][stim] = 0
|
|
|
|
# find AUC relative to F_inf rheobase
|
|
# setup current inputs with current range around F_inf rheobase
|
|
I_first_inf = I_rel[0, np.argwhere(f['analysis']['F_inf_Finf'][:] != 0)[0][0]]
|
|
I_start = I_first_inf - 0.00005
|
|
I_end = I_first_inf + 0.2 * high
|
|
I_rel2 = np.arange(I_start, I_end + (I_end - I_start) / 100, (I_end - I_start) / 100)
|
|
I_rel2 = np.reshape(I_rel2, (1, I_rel2.shape[0]))
|
|
I_in2 = np.zeros((stim_time + np.int(initial_period * 1 / dt), 1)) @ I_rel2
|
|
I_in2[np.int(initial_period * 1 / dt):, :] = np.ones((stim_time, 1)) @ I_rel2
|
|
stim_num = I_in2.shape[1]
|
|
|
|
# simulate response to this current step range
|
|
V_m_AUC = Pospischil(V_init * np.ones(stim_num), V_T, g2, E2, I_in2, dt, currents_included2, stim_time,
|
|
stim_num, C, tau_max_p, shift2, scale2, b_param2, slope_shift2,
|
|
np.zeros((len(b_param), stim_num)), np.zeros((len(currents_included), stim_num)),
|
|
ind_dict)
|
|
|
|
# save data and analysis for this current step range including AUC
|
|
f['data'].require_dataset('V_m_AUC', shape=V_m_AUC.shape, dtype='float64')
|
|
f['data']['V_m_AUC'][:] = V_m_AUC
|
|
dtyp = h5py.special_dtype(vlen=np.dtype('float64'))
|
|
for i in np.array(['spike_times_Finf_AUC', 'ISI_Finf_AUC', 'Freq_Finf_AUC']): # analysis_names:
|
|
f['analysis'].require_dataset(i, shape=(I_rel2.shape[1],), dtype=dtyp, compression="gzip", compression_opts=9)
|
|
f['analysis'].require_dataset('F_inf_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
|
f['analysis'].require_dataset('F_null_Finf_AUC', shape=(I_rel2.shape[1],), dtype='float64')
|
|
for stim in range(I_rel2.shape[1]):
|
|
# spike peak detection
|
|
ap_all, ap_prop = scipy.signal.find_peaks(V_m_AUC[stim, stim_start:], min_spike_height,
|
|
prominence=prominence, distance=1 * 1 / dt)
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|
f['analysis']['spike_times_Finf_AUC'][stim] = ap_all * dt
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|
f['analysis']['ISI_Finf_AUC'][stim] = np.array(
|
|
[x - f['analysis']['spike_times_Finf_AUC'][stim][i - 1] if i else None for i, x in
|
|
enumerate(f['analysis']['spike_times_Finf_AUC'][stim])][1:]) # msec
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|
f['analysis']['Freq_Finf_AUC'][stim] = 1 / (f['analysis']['ISI_Finf_AUC'][stim] / 1000)
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|
if f['analysis']['Freq_Finf_AUC'][stim].size != 0:
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|
if np.argwhere(ap_all >= 1000 * 1 / dt).shape[0] != 0: # if first spike is after 1 ms
|
|
half_second_spikes = np.logical_and(f['analysis']['spike_times_Finf_AUC'][stim] < np.int(
|
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][0]] * dt + 500),
|
|
f['analysis']['spike_times_Finf_AUC'][stim] > np.int(
|
|
ap_all[np.argwhere(ap_all >= 1000 * 1 / dt)[0][
|
|
0]] * dt)) # spikes in half second window
|
|
if np.sum(half_second_spikes == True) > 1:
|
|
half_second_spikes_1 = np.delete(half_second_spikes,
|
|
np.argwhere(half_second_spikes == True)[0][
|
|
0]) # remove first spike because it has no ISI/freq
|
|
f['analysis']['F_inf_Finf_AUC'][stim] = np.mean(
|
|
f['analysis']['Freq_Finf_AUC'][stim][half_second_spikes_1])
|
|
else:
|
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
|
else:
|
|
f['analysis']['F_null_Finf_AUC'][stim] = 0
|
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
|
else:
|
|
f['analysis']['F_null_Finf_AUC'][stim] = 0
|
|
f['analysis']['F_inf_Finf_AUC'][stim] = 0
|
|
f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip",
|
|
compression_opts=9)
|
|
f['analysis']['AUC'][:] = np.trapz(f['analysis']['F_inf_Finf_AUC'][:], I_rel2[:] * 1000, dI * 1000)
|
|
except:
|
|
f['analysis'].require_dataset('AUC', shape=(1,), dtype='float64', compression="gzip",
|
|
compression_opts=9)
|
|
f['analysis']['AUC'][:] = 0
|
|
if f.__bool__():
|
|
f.close()
|
|
gc.collect()
|
|
|
|
|