"""
Test the effects of different syanptic inputs in the detailed PFC L5 neuron
to generate plateau potential
The orginal model is from
https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=117207&file=/acker_antic/Model/CA%20229.hoc#tabs-2
Modified by : Peng Penny Gao <penggao.1987@gmail.com>
Run simulation with NMDA.mod file - DMS model
"""
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 *
h.load_file('stdrun.hoc') # for initialization
##############################
# Function to determine the randomness of activation time of AMPA and NMDA
# Uniform random seen in Fig 2. E1
# Remove the seed to get true randomness
def random_2(low, high, size):
time_random = np.linspace(low, high, size)
np.random.seed(10)
np.random.shuffle(time_random)
return time_random
# Alpha random seen in Fig 2. E2
def random_beta(low, high, size):
time_random = np.random.beta(2, 8, size=size)
np.random.seed(10)
time_random = (high - low)*time_random + low
return time_random
################### Test the ratio of different repceptors
class Glu_Stim:
def __init__(self, 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]):
"""
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:
-----------
TTX: True or False.
True: setting all the sodium channel conductance to 0 to mimic
TTX application in experiments
False: default
Pool1_num: syanptic AMPA/NMDA numbers
Pool2_num: extrasyanptic NMDA numbers
Beta: parameter for NMDA Receptors
Cdur: parameter for NMDA 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
-----------
Outputs:
Figures: recording from soma and 3 different locations from basal dendrites
json: soma and dendritc voltage recording and parameters info
"""
Cell = de.CA229()
self.Cell = Cell
# Can adjust channel conductance ratio here:
# eg. Cell = CA229(KA_ratio = 0.5)
###########################################
timestr = time.strftime("%H%M")
data = time.strftime("%m_%d")
directory_root = 'Fig3/'
directory = directory_root + 'DMS/Plot/'
# directory = directory_root + 'DMS/Analysis/'
if (TTX == True):
Cell.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:
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 = []
self.SynAMPA = SynAMPA
self.nc_AMPA = nc_AMPA
self.SynNMDA = SynNMDA
self.nc_NMDA = nc_NMDA
###########################################
loc1 = list(np.linspace(Loc[0], Loc[1], Pool1_num))
###########################################
# Loc and time delay set up
delay1 = random_2(10, 50 + int(Syn_w1*50), Pool1_num)
# delay1 = random_beta(10, 50 + int(Syn_w1*50), Pool1_num)
ns = h.NetStim()
self.ns = ns
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[34](loc1[i])))
SynAMPA[-1].gmax = 0.05
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[34](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 = []
self.ExNMDA = ExNMDA
self.nc_ExNMDA = nc_ExNMDA
loc2 = list(np.linspace(Loc[0], Loc[1], Pool2_num))
# delay2 = list(np.linspace(5, 10, Pool2_num))
delay2 = random_2(15, 55 + int(Syn_w2*60), Pool2_num)
# delay2 = random_beta(15, 55 + int(Syn_w2*60), Pool2_num)
for i in range(Pool2_num):
###########################
# Adding extrasyanptic NMDA
ExNMDA.append(h.NMDA(Cell.basal[34](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()
cai_soma = h.Vector()
cai_dend = h.Vector()
v_vec_soma.record(Cell.soma[2](0.5)._ref_v)
v_vec_dend1.record(Cell.basal[34](0.8)._ref_v)
v_vec_dend2.record(Cell.basal[34](0.5)._ref_v)
v_vec_dend3.record(Cell.basal[34](0.3)._ref_v)
cai_soma.record(Cell.soma[2](0.5)._ref_cai)
cai_dend.record(Cell.basal[34](0.3)._ref_cai)
###########################################
### Run & Plot
###########################################
h.celsius = 32
h.v_init = -73.6927850677
h.init()
h.tstop = 1000
h.run()
# pdb.set_trace() #Debugging
# print v_vec_soma[-1]
# plt.clf()
# plt.close()
# 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[34](0.8)', color = 'red')
# plt.plot(t_vec, v_vec_dend2, label = 'Basal[34](0.5)', color = 'blue')
# plt.plot(t_vec, v_vec_dend3, label = 'Basal[34](0.3)', color = 'green')
# plt.ylim([-90, 40])
# plt.xlim([0, 800])
# 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)
#######################
# Plot the intracelluar calcium concentration
# plt.clf()
# plt.close()
# plt.figure(figsize = (16, 6), dpi = 100)
# plt.plot(t_vec, cai_soma, label = 'soma(0.5)', color = 'black')
# #plt.plot(t_vec, cai_dend, label = 'bdend[34](0.3)', color = 'red')
#
# # plt.ylim([-90, 60])
# plt.xlim([0, 800])
# plt.legend(loc = 'best')
# plt.ylabel('mM')
# plt.xlabel('Time (ms)')
# plt.title ("Calcium concentration")
# title1 = "Calcium_" + title
# ut.save(title1, directory, ext="png", close=True, verbose=True)
data = ut.Vividict()
data['SynAMPA']['num'] = Pool1_num
data['SynAMPA']['locs'] = loc1
data['SynAMPA']['weight'] = Syn_w1
data['SynNMDA']['num'] = Pool1_num
data['SynNMDA']['locs'] = loc1
data['SynNMDA']['weight'] = Syn_w1
data['SynNMDA']['Beta'] = Beta
data['SynNMDA']['Cdur'] = Cdur
data['ExNMDA']['num'] = Pool2_num
data['ExNMDA']['locs'] = loc2
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_34']['voltage_0.8'] = list(v_vec_dend1)
data['recording']['basal_34']['voltage_0.5'] = list(v_vec_dend2)
data['recording']['basal_34']['voltage_0.3'] = list(v_vec_dend3)
data['recording']['soma']['ica'] = list(cai_soma)
data['recording']['basal_34']['ica_0.3'] = list(cai_dend)
ut.savejson(data, title, directory, ext = "json", verbose = False)
######################################################
if __name__ == "__main__":
print("Running the model")
start_time = time.time()
loc = [0.25, 0.6]
# Plot weight for Fig2
weight = [0.1, 0.15, 0.20, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
# Analysis weight for Fig2
# weight = [0.1, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
# z = Glu_Stim(True, Pool_num, Pool_num, 0.02, 50 + int(100*1), 1, 1, loc)
for w in weight:
Pool_num = 8 + int(20*w)
gs = Glu_Stim(False, Pool_num, Pool_num, 0.02, 50 + int(100*w), w, w, loc)
# Glu_Stim(True, Pool_num, Pool_num, 0.02, 50 + int(100*w), w, w, loc)
print("Finished.")
print("--- %s seconds ---" % (time.time() - start_time))
