# Simulation of tripartite synapse model
# Tiina Manninen and Ausra Saudargiene
# Reference: Tiina Manninen, Ausra Saudargiene, and Marja-Leena Linne. Astrocyte-mediated spike-timing-dependent
# long-term depression modulates synaptic properties in the developing cortex. PLoS Comput Biol, 2020.
# -----------------------------------------------------------------------
from datetime import datetime
import os
from preneuron import Pre
from postneuron import Post
from astrocyte import Astro
from tqdm import tqdm
from scipy import io
from collections import defaultdict
def state_var_to_be_saved(pre, post, astro):
return{
"Ca_CaNHVA_pre": pre["Ca_CaNHVA_pre"],
"Ca_NMDAR_pre": pre["Ca_NMDAR_pre"],
"CaN_pre": pre["CaN_pre"],
"Glu_syncleft": post["Glu_syncleft"],
"Prel_pre": pre["Prel_pre"],
"RA2_O_pre": pre["RA2_O_pre"],
"Rrel_pre": pre["Rrel_pre"],
"V_pre": pre["V_pre"],
"AG_post": post["AG_post"],
"Ca_post": post["Ca_post"],
"Ca_ER_post": post["Ca_ER_post"],
"Ca_DAG_GaGTP_PLC_post": post["Ca_DAG_GaGTP_PLC_post"],
"Ca_DAG_PLC_post": post["Ca_DAG_PLC_post"],
"Ca_GaGTP_PLC_post": post["Ca_GaGTP_PLC_post"],
"Ca_PLC_post": post["Ca_PLC_post"],
"DAG_post": post["DAG_post"],
"GaGTP_PLC_post": post["GaGTP_PLC_post"],
"h_IP3R_post": post["h_IP3R_post"],
"IP3_post": post["IP3_post"],
"PLC_post": post["PLC_post"],
"V_dend_post": post["V_dend_post"],
"V_soma_post": post["V_soma_post"],
"Ca_astro": astro["Ca_astro"],
"Glu_extsyn": astro["Glu_extsyn"],
"h_astro": astro["h_astro"],
"IP3_astro": astro["IP3_astro"],
"Rrel_astro": astro["Rrel_astro"]
}
def main(T_shift):
# Start to count the time spent in simulation
time_start = datetime.now()
print("Simulation started at", time_start)
# Define the name of the result directory to be created
path_template = "./results_post_pre_pairing_100x/%sms/"
path = path_template % T_shift
try:
os.makedirs(path)
except OSError:
print("Creation of the directory %s failed" % path)
else:
print("Successfully created the directory %s " % path)
# --------------------
# External stimulation
# --------------------
stim_start = 20000 # ms; Stimulation start time
trainlengthtime = 500000 # ms; Stimulation lasting time
restlengthtime = 20000 # ms; Resting time after stimulation ends
no_trains = 1 # 1; Number of trains
pulserate = 0.2 # Hz, 1/s; Frequency of stimulus
pulselengthtime = 10 # ms; Length of the pulse
A_stim_post = 25 # uA/cm^2; External current amplitude to postsynaptic neuron per unit area
A_stim_pre = 10 # uA/cm^2; External current amplitude to presynaptic neuron per unit area
dt = 0.05 # ms; Simulation time step
T_end = stim_start + no_trains * (trainlengthtime + restlengthtime) # ms; Simulation end time
Nsteps = round(T_end / dt) # Number of simulation steps
t = [i * dt for i in range(Nsteps + 1)] # ms; Time vector
pulselength = round(pulselengthtime / dt)
no_pulses = round(pulserate * trainlengthtime * 1e-3)
pauselengthtime = (trainlengthtime - no_pulses * pulselengthtime) / no_pulses # ms
pauselength = round(pauselengthtime / dt)
restlength = round(restlengthtime / dt)
steps_T_shift = round(T_shift / dt)
stim_pause_post = ([A_stim_post] * pulselength + [0] * pauselength) * no_pulses
stim_pause_pre = ([A_stim_pre] * pulselength + [0] * pauselength) * no_pulses
# Postsynaptic stimulus
I_ext_post = [0] * (round(stim_start / dt) + 1) + (stim_pause_post + [0] * restlength) * no_trains
# Presynaptic stimulus
I_ext_pre = [0] * (round(stim_start / dt) + steps_T_shift + 1) + (stim_pause_pre + [0] * restlength) * no_trains
# ---------------------------------
# Presynaptic neuron initialization
# ---------------------------------
pre_params = Pre.get_parameters()
pre_init = Pre.get_initial_values(pre_params)
pre = Pre(pre_params, pre_init)
# ----------------------------------
# Postsynaptic neuron initialization
# ----------------------------------
post_params = Post.get_parameters()
post_init = Post.get_initial_values(post_params)
post = Post(post_params, post_init)
# ------------------------
# Astrocyte initialization
# ------------------------
astro_params = Astro.get_parameters()
astro_init = Astro.get_initial_values(astro_params)
astro = Astro(astro_params, astro_init)
# -----------------------------------------
# Initialization of Ca leak flux parameters
# -----------------------------------------
Ca_flux_post = post.calcium_other_fluxes()
Ca_par_post = post.calcium_leak_parameters(Ca_flux_post["J_CaL_post"],
Ca_flux_post["J_IP3R_post"],
Ca_flux_post["J_NMDAR_post"],
Ca_flux_post["J_PMCA_post"],
Ca_flux_post["J_SERCA_post"])
Ca_flux_astro = astro.calcium_other_fluxes()
Ca_par_astro = astro.calcium_leak_parameters(Ca_flux_astro["J_IP3R_astro"],
Ca_flux_astro["J_SERCA_astro"])
# ----------------------------------------------------------
# Variables to be saved in a dictionary and then into a file
# ----------------------------------------------------------
state_var = state_var_to_be_saved(pre.x, post.x, astro.x)
saved_state_var = {key: [values] for key, values in state_var.items()}
saved_other_var = defaultdict(list)
t_spike = 10 # ms
for i in tqdm(range(Nsteps)):
if i == stim_start * 1 / 2 / dt or i == stim_start * 3 / 4 / dt:
# ---------------------------------
# Adjusting Ca leak flux parameters
# ---------------------------------
Ca_flux_post = post.calcium_other_fluxes()
Ca_par_post = post.calcium_leak_parameters(Ca_flux_post["J_CaL_post"],
Ca_flux_post["J_IP3R_post"],
Ca_flux_post["J_NMDAR_post"],
Ca_flux_post["J_PMCA_post"],
Ca_flux_post["J_SERCA_post"])
Ca_flux_astro = astro.calcium_other_fluxes()
Ca_par_astro = astro.calcium_leak_parameters(Ca_flux_astro["J_IP3R_astro"],
Ca_flux_astro["J_SERCA_astro"])
# ---------------------------------------------
# Saving old values for certain state variables
# ---------------------------------------------
Ca_pre_old = pre.x["Ca_CaNHVA_pre"]
Prel_pre_old = pre.x["Prel_pre"]
Rrel_pre_old = pre.x["Rrel_pre"]
V_pre_old = pre.x["V_pre"]
Ca_astro_old = astro.x["Ca_astro"]
Rrel_astro_old = astro.x["Rrel_astro"]
# ----------------------------------------------
# Fraction of presynaptic Glu release inhibition
# ----------------------------------------------
f_pre = pre.x["X_ac_pre"] / pre_params["X_total_pre"]
# ----------------------
# Differential equations
# ----------------------
deriv_pre, other_var_pre = pre.derivative(
astro.x["Glu_extsyn"], post.x["Glu_syncleft"], I_ext_pre[i+1])
deriv_post, other_var_post = post.derivative(
pre.params["f_Glu_pre"], I_ext_post[i+1], Ca_par_post["r_leakCell_post"], Ca_par_post["r_leakER_post"])
deriv_ast, other_var_ast = astro.derivative(post.x["AG_post"], Ca_par_astro["r_leakER_astro"])
# ----------------------------------
# Solving the differential equations
# ----------------------------------
pre.solve_deriv(deriv_pre, dt)
post.solve_deriv(deriv_post, dt)
astro.solve_deriv(deriv_ast, dt)
# -----------------------------------------------------
# Updating those variables that include delta functions
# -----------------------------------------------------
# Counting the time from previous presynaptic spike
if (pre.x["V_pre"] >= 0) and (V_pre_old < 0):
t_spike = 0
else:
t_spike = t_spike + dt
# Glu release from presynaptic neuron
if (pre.x["Ca_CaNHVA_pre"] >= pre.params["C_thr_pre"]) and (t_spike < 10):
pre.solve_deltaf(Ca_pre_old, f_pre, Prel_pre_old, Rrel_pre_old)
post.solve_deltaf(pre, Rrel_pre_old)
t_spike = 10
# Glu release from astrocyte
if (astro.x["Ca_astro"] >= astro.params["C_thr_astro"]) and (Ca_astro_old < astro.params["C_thr_astro"]):
astro.solve_deltaf(Rrel_astro_old)
# -----------
# Saving data
# -----------
state_var = state_var_to_be_saved(pre.x, post.x, astro.x)
for key, values in state_var.items():
saved_state_var[key].append(values)
other_var = {"f_pre": f_pre,
"Glu_NMDAR_pre": other_var_pre["Glu_NMDAR_pre"],
"ICaNHVA_pre": other_var_pre["ICaNHVA_pre"],
"ICa_NMDAR_pre": other_var_pre["ICa_NMDAR_pre"],
"I_AMPAR_post": other_var_post["I_AMPAR_post"],
"ICaLHVA_dend_post": other_var_post["ICaLHVA_dend_post"],
"ICaLLVA_dend_post": other_var_post["ICaLLVA_dend_post"],
"ICa_NMDAR_post": other_var_post["ICa_NMDAR_post"],
"J_CaL_post": other_var_post["J_CaL_post"],
"J_IP3R_post": other_var_post["J_IP3R_post"],
"J_leakCell_post": other_var_post["J_leakCell_post"],
"J_leakER_post": other_var_post["J_leakER_post"],
"J_NMDAR_post": other_var_post["J_NMDAR_post"],
"J_PMCA_post": other_var_post["J_PMCA_post"],
"J_SERCA_post": other_var_post["J_SERCA_post"]}
for key, values in other_var.items():
saved_other_var[key].append(values)
# Saving dictionaries to mat files
io.savemat(os.path.join(path, "state_var_results.mat"), saved_state_var)
io.savemat(os.path.join(path, "other_var_results.mat"), saved_other_var)
io.savemat(os.path.join(path, "time_stimuli.mat"),
{**{"time": [tp / 1000 for tp in t]}, **{"I_ext_pre": [I_ext_pre]}, **{"I_ext_post": [I_ext_post]}})
io.savemat(os.path.join(path, "stimulation_parameters.mat"),
{**{"dt": dt}, **{"pulserate": pulserate}, **{"T_shift": T_shift}})
# Simulation time
time_end = datetime.now()
total_time = (time_end - time_start).seconds / 60. # min
print("\n")
print("Simulation finished at", time_end)
print("Total time = {0:.2f} minutes".format(total_time))
if __name__ == "__main__":
for T_shift in range(10, 210, 10): # ms; Temporal difference between post and pre activation
main(T_shift)
