"""
Simulation code for Fig 5. - Model 1
Test the effects of different syanptic input locations
for the generation of plateau potential
Author: Peng Penny Gao
<penggao.1987@gmail.com>
"""
import CA229 as de # from CA229 import *
import matplotlib.pyplot as plt
from neuron import h
import numpy as np
import utils as ut #from utils import *
import json
import itertools
import time
import pdb # For python debugging
#from random import *
import math
import pandas as pd
h.load_file('stdrun.hoc') # for initialization
def random_2(low, high, size):
time_random = np.linspace(low, high, size)
np.random.shuffle(time_random)
return time_random
################### Test the ratio of different repceptors
def Glu_Stim(Bnum = 34, TTX = False, Pool1_num = 9, Pool2_num = 9,
Beta = 0.067, Cdur = 1, Syn_w1 = 0.01, Syn_w2 = 0.01, Loc = [0.2, 0.6], DenLoc = 0.5):
"""
Model the Glumate Stimulation.
Model the Receptors in 2 pools:
Pool 1: AMPA + NMDA (same synaptic weight, represent spine conductance)
Pool 2: NMDA only (represent the extrasyanptic NMDARs)
Parameters:
-----------
Bnum: the number of basal branch to explore
TTX: True or False.
True: setting all the sodium channel conductance to 0.
False: default
Pool1_num: syanptic AMPA/NMDA numbers
Pool2_num: extrasyanptic NMDA numbers
Beta: parameter of NMDA Receptors
Cdur: parameter ofNMDA Receptors
Syn_w1: the syanptic weight of AMPA/NMDA receptors in pool1
Syn_w2: the syanptic weight of AMPA/NMDA receptors in pool2
Loc: the stimulation location
DenLoc: the targeted recording location on dendrite
-----------
Outputs:
Figures: recording from soma and 3 different locations from basal dendrites
json: soma and dendritc voltage recording and parameters info
"""
Cell = de.CA229()
timestr = time.strftime("%Y%m%d-%H%M")
data = time.strftime("%m_%d")
directory_root = "Fig5/DMS/"
L1 = "{:.2f}".format(Loc[0])
L2 = "{:.2f}".format(Loc[1])
if (TTX == True):
Cell.TTX()
directory = directory_root + "B" + str(Bnum) + "/Loc" + L1 + "_" + L2 + "/TTX/"
title = "TTX_Pool1_"+ \
str(Pool1_num) + "_Pool2_" + str(Pool2_num) + "_NMDA_Beta_" + \
str(Beta) + "_NMDA_Cdur_" + str(Cdur) + "_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
else:
directory = directory_root + "B" + str(Bnum) + "/Loc" + L1 + "_" + L2 + "/N/"
title = "Pool1_"+ \
str(Pool1_num) + "_Pool2_" + str(Pool2_num) + "_NMDA_Beta_" + \
str(Beta) + "_NMDA_Cdur_" + str(Cdur) + "_Pool1_W_" + str(Syn_w1) + \
"_Pool2_W_" + str(Syn_w2) + "_"+ timestr
###########################################
# Adding Pool 1
###########################################
##### AMPA
SynAMPA = []
nc_AMPA = []
SynNMDA = []
nc_NMDA = []
loc1 = list(np.linspace(Loc[0], Loc[1], Pool1_num))
###########################################
delay1 = random_2(10, 20 + int(Syn_w1*50), Pool1_num)
ns = h.NetStim()
ns.interval = 20
ns.number = 1
ns.start = 190
ns.noise = 0
for i in range(Pool1_num):
###########################
# Adding AMPA
SynAMPA.append(h.AMPA(Cell.basal[Bnum](loc1[i])))
SynAMPA[-1].gmax = 0.05
#SynAMPA1[-1].Beta = 0.28
nc_AMPA.append(h.NetCon(ns, SynAMPA[i]))
nc_AMPA[-1].delay = delay1[i]
nc_AMPA[-1].weight[0] = Syn_w1
###########################
#Adding NMDA
SynNMDA.append(h.NMDA(Cell.basal[Bnum](loc1[i])))
SynNMDA[-1].gmax = 0.005
SynNMDA[-1].Beta = Beta
SynNMDA[-1].Cdur = Cdur
nc_NMDA.append(h.NetCon(ns, SynNMDA[i]))
nc_NMDA[-1].delay = delay1[i]
nc_NMDA[-1].weight[0] = Syn_w1
###########################################
# Adding Pool 2
###########################################
ExNMDA = []
nc_ExNMDA = []
loc2 = list(np.linspace(Loc[0], Loc[1], Pool2_num))
delay2 = random_2(15, 25 + int(Syn_w2*60), Pool2_num)
for i in range(Pool2_num):
###########################
# Adding extrasyanptic NMDA
ExNMDA.append(h.NMDA(Cell.basal[Bnum](loc2[i])))
ExNMDA[-1].gmax = 0.005
ExNMDA[-1].Beta = Beta
ExNMDA[-1].Cdur = Cdur
nc_ExNMDA.append(h.NetCon(ns, ExNMDA[i]))
nc_ExNMDA[-1].delay = delay2[i]
nc_ExNMDA[-1].weight[0] = Syn_w2
###########################################
### Recording
###########################################
t_vec = h.Vector()
t_vec.record(h._ref_t)
v_vec_soma = h.Vector()
v_vec_dend1 = h.Vector()
v_vec_dend2 = h.Vector()
v_vec_dend3 = h.Vector()
v_vec_dend = h.Vector()
v_vec_soma.record(Cell.soma[2](0.5)._ref_v)
v_vec_dend1.record(Cell.basal[Bnum](0.8)._ref_v)
v_vec_dend2.record(Cell.basal[Bnum](0.5)._ref_v)
v_vec_dend3.record(Cell.basal[Bnum](0.3)._ref_v)
v_vec_dend.record(Cell.basal[Bnum](DenLoc)._ref_v)
###########################################
### Run & Plot
###########################################
h.celsius = 32
h.v_init = -73.6927850677
h.init()
h.tstop = 1000
h.run()
# pdb.set_trace() #Debugging
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, v_vec_soma, label = 'soma(0.5)', color = 'black')
# plt.plot(t_vec, v_vec_dend1, label = 'bdend['+str(Bnum)+'](0.8)', color = 'red')
# plt.plot(t_vec, v_vec_dend2, label = 'Basal['+str(Bnum)+'](0.5)', color = 'blue')
# plt.plot(t_vec, v_vec_dend3, label = 'Basal['+str(Bnum)+'](0.3)', color = 'green')
# plt.ylim([-90, 40])
# plt.xlim([0, 700])
# plt.legend(loc = 'best')
# plt.ylabel('mV')
# plt.xlabel('Time (ms)')
# plt.title ("Glumate Receptor Activated Plateau Potential")
#
# save(title, directory, ext="png", close=True, verbose=True)
data = ut.Vividict()
data['TTX'] = TTX
data['SynAMPA']['num'] = Pool1_num
data['SynAMPA']['locs'] = Loc
data['SynAMPA']['weight'] = Syn_w1
data['SynNMDA']['num'] = Pool1_num
data['SynNMDA']['locs'] = Loc
data['SynNMDA']['weight'] = Syn_w1
data['SynNMDA']['Beta'] = Beta
data['SynNMDA']['Cdur'] = Cdur
data['ExNMDA']['num'] = Pool2_num
data['ExNMDA']['locs'] = Loc
data['ExNMDA']['weight'] = Syn_w2
data['ExNMDA']['Beta'] = Beta
data['ExNMDA']['Cdur'] = Cdur
data['recording']['time'] = list(t_vec)
data['recording']['soma']['voltage'] = list(v_vec_soma)
data['recording']['basal']['voltage_0.8'] = list(v_vec_dend1)
data['recording']['basal']['voltage_0.5'] = list(v_vec_dend2)
data['recording']['basal']['voltage_0.3'] = list(v_vec_dend3)
data['recording']['basal']['voltage_input'] = list(v_vec_dend)
ut.savejson(data, title, directory, ext = "json", verbose = False)
######################################################
if __name__ == "__main__":
print("Running the model")
start_time = time.time()
Pool_num = 12
weight = [0.7, 0.9] # 0.7 for Fig 5. B1, 0.9 for Fig D1
# weight = [1.2, 1.5] # For generating multiple APs - Fig 5. D3
# weight = [0.1, 0.3, 0.5, 0.7] # For the demo traces
basal_num = [15, 34, 14, 22, 25, 31]
with open('data.json', 'r') as fp:
data = json.load(fp)
with open('dend_measure_data.json', 'r') as fp1:
Ndata = json.load(fp1)
for b in basal_num:
loc = data[str(b)]
DenLoc = Ndata[str(b)]
for l1, l2 in zip(loc, DenLoc):
for w in weight:
Glu_Stim(b, False, Pool_num, Pool_num, 0.02, 10, w, w, l1, l2)
Glu_Stim(b, True, Pool_num, Pool_num, 0.02, 10, w, w, l1, l2)
print("Finished.")
print("--- %s seconds ---" % (time.time() - start_time))