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DG_80.py
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DG_80.py
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#==============================================================================
# Network of Dentate gyrus based on Chavlis et al, Hippocampus 2017
# This is a control model with 80% overlap in the input pattern!
#==============================================================================
###############################################################################
import os
from brian import *
from brian.library.ionic_currents import *
from brian.library.IF import *
import numpy as np
import time
import random as pyrandom
# Active pattern of neurons
overlap = '80'
trial = 2
trial_i = [1]
Trial = trial_i[0]
maindir=os.getcwd()
# Initial pattern
scale_fac = 4
N_input = 100 * scale_fac
N_granule = 500 * scale_fac
N_basket = 25 * scale_fac
N_mossy = 20 * scale_fac
N_hipp = 10 * scale_fac
d_input = 0.10 # active input density
reinit(states = True)
clear(erase = True, all = True)
# Active pattern of neurons
path = 'input_patterns/scale_'+str(scale_fac)+'/d_input_0.1/'
active_pattern = list(np.load(path+'active_pattern_'+str(Trial)+'.npy'))
n = int( round((1 - int(overlap)*0.01)*d_input*N_input) )
removal = pyrandom.sample(active_pattern, n)
extra = pyrandom.sample([x for x in xrange(N_input) if x not in active_pattern], n)
active = [x for x in active_pattern if x not in removal]
active += extra
active = list(sort(active))
inactive = [x for x in xrange(N_input) if x not in active]
print "\nBuilding the Network... "
# CONNECTIVITY PARAMETERS
# General parameters
E_nmda = 0 * mV # NMDA reversal potential
E_ampa = 0 * mV # AMPA reversal potential
E_gaba = -86 * mV # GABA reversal potential
gamma = 0.072 * mV**-1 # Mg Concentration factor
alpha_nmda = 0.5 * ms**-1 # NMDA scale factor
alpha_ampa = 1 * ms**-1 # AMPA scale factor
alpha_gaba = 1 * ms**-1 # GABA scale factor
# EC CELLS ---> GRANULE CELLS
g_ampa_eg = 0.8066 * nS
g_nmda_eg = 1.0800 * g_ampa_eg
# EC CELLS ---> HIPP CELLS
g_ampa_eh = 0.24 * nS
g_nmda_eh = 1.15 * g_ampa_eh
# SOURCE: GRANULE CELLS
# GRANULE CELLS ---> BASKET CELLS
g_ampa_gb = 0.21 * nS
g_nmda_gb = 1.50 * g_ampa_gb
# GRANULE CELLS ---> MOSSY CELLS
g_ampa_gm = 0.50 * nS
g_nmda_gm = 1.05 * g_ampa_gm
# SOURCE: MOSSY CELLS
# MOSSY CELLS ---> GRANULE CELLS
g_ampa_mg = 0.1066 * nS
g_nmda_mg = 1.0800 * g_ampa_mg
# MOSSY CELLS ---> BASKET CELLS
g_ampa_mb = 0.35 * nS
g_nmda_mb = 1.10 * g_ampa_mb
# SOURCE: BASKET CELLS
# BASKET CELLS ---> GRANULE CELLS
g_gaba_bg = 14.0 * nS
# SOURCE: HIPP CELLS
# HIPP CELLS ---> GRANULE CELLS
g_gaba_hg = 0.12 * nS
#=======================================================================================================================
#=======================================================================================================================
# INPUT CELLS (ENTORHINAL CORTEX)
from poisson_input import *
rate = 45*Hz
simtime = 1000*ms
t1 = 300 * ms
t2 = 10 * ms
spiketimes = poisson_input(active, N_input, rate, simtime, t1, t2)
Input_ec = SpikeGeneratorGroup(N_input, spiketimes)
#=======================================================================================================================
# GRANULE CELLS
# Parameters
gl_g = 2.57 * nS # leakage conductance
El_g = -87.00 * mV # reversal-resting potential
Cm_g = 0.08 * nF # membrane capacitance
v_th_g = -56.00 * mV # threshold potential
v_reset_g = -74.00 * mV # reset potential
# AMPA/NMDA/GABA Kinetics
t_nmda_decay_g = 50.0 * ms # NMDA decay time constant
t_nmda_rise_g = 0.33 * ms # NMDA rise time constant
t_ampa_decay_g = 2.5 * ms # AMPA decay time constant
t_ampa_rise_g = 0.1 * ms # AMPA rise time constant
t_gaba_decay_g = 6.8 * ms # GABA decay time constant
t_gaba_rise_g = 0.9 * ms # GABA rise time constant
# AMPA/NMDA/GABA Model Parameters
gamma_g = 0.04 * mV**-1 # the steepness of Mg sensitivity of Mg unblock
Mg = 2.0 # [mM]--mili Molar - the extracellular Magnesium concentration
eta = 0.2 # [mM**-1] -1- mili Molar **(-1) - Magnesium sensitivity of unblock
alpha_nmda_g = 2.0 * ms**-1
alpha_ampa = 1.0 * ms**-1
alpha_gaba = 1.0 * ms**-1
# NOISE
g_ampa_gn = 0.008 * nS
g_nmda_gn = 0.008 * nS
# AHP patrameters
tau_ahp = 45.0 * ms
g_ahp = 0.2 * nS
# Synaptic current equations @ SOMA
eq_soma = Equations('''
I_syn_g = I_nmda_eg + I_ampa_eg + I_nmda_mg + I_ampa_mg + I_nmda_gn + I_ampa_gn + I_gaba_bg +I_gaba_hg + I_Sahp : amp
I_nmda_eg = g_nmda_eg*(vm - E_nmda)*s_nmda_eg/(1.0 + eta*Mg*exp(-gamma*vm)) : amp
I_ampa_eg = g_ampa_eg*(vm - E_ampa)*s_ampa_eg : amp
s_nmda_eg : 1
s_ampa_eg : 1
I_nmda_mg = g_nmda_mg*(vm - E_nmda)*s_nmda_mg/(1.0 + eta*Mg*exp(-gamma*vm)) : amp
I_ampa_mg = g_ampa_mg*(vm - E_ampa)*s_ampa_mg : amp
s_nmda_mg : 1
s_ampa_mg : 1
I_gaba_bg = g_gaba_bg*(vm - E_gaba)*s_gaba_bg : amp
s_gaba_bg : 1
I_gaba_hg = g_gaba_hg*(vm - E_gaba)*s_gaba_hg : amp
s_gaba_hg : 1
I_nmda_gn = g_nmda_gn*(vm - E_nmda)*s_nmda_gn*(1.0 + eta*Mg*exp(-gamma*vm)) : amp
I_ampa_gn = g_ampa_gn*(vm - E_ampa)*s_ampa_gn : amp
s_nmda_gn : 1
s_ampa_gn : 1
I_Sahp : amp
dI_Sahp/dt = (g_ahp*(vm-El_g)-I_Sahp)/tau_ahp : amp
''')
# Soma equation
granule_eqs = MembraneEquation(Cm_g)
granule_eqs += leak_current(gl_g, El_g)
granule_eqs += IonicCurrent('I = I_syn_g : amp')
granule_eqs += eq_soma
granule = NeuronGroup(N_granule, model = granule_eqs, threshold = 'vm > v_th_g',
reset = 'vm = v_reset_g; I_Sahp += 0.0450*nA',
refractory = 20 * ms, compile = True, freeze = True)
# Initialization of membrane potential
granule.vm = El_g
#Clustering of granule cells
counter = 20
N_cl = len(granule)/counter
granule_cl = {}
for gran in xrange(N_cl):
granule_cl[gran] = granule.subgroup(counter)
#=======================================================================================================================
#=======================================================================================================================
# BASKET CELLS
# Parameters
gl_b = 18.054 * nS # leakage conductance
El_b = -52 * mV # reversal-resting potential
Cm_b = 0.1793 * nF # membrane capacitance
v_th_b = -39 * mV # threshold potential
v_reset_b = -45 * mV # reset potential
DeltaT_b = 2 * mV # slope factor
# Synaptic Parameters
gamma = 0.072 * mV**-1 # Mg Concentration factor
alpha_nmda = 0.5 * ms**-1 # NMDA scale factor
alpha_ampa = 1 * ms**-1 # AMPA scale factor
alpha_gaba = 1 * ms**-1 # GABA scale factor
#AMPA/NMDA Kinetics
t_nmda_decay_b = 130.0 * ms # NMDA decay time constant
t_nmda_rise_b = 10.0 * ms # NMDA rise time constant
t_ampa_decay_b = 4.2 * ms # AMPA decay time constant
t_ampa_rise_b = 1.2 * ms # AMPA rise time constant
# NOISE
g_nmda_bn = 2.5 * nS # NMDA maximum conductance
g_ampa_bn = 3.5 * nS # AMPA maximum conductance
t_nmda_decay_bn = 130 * ms # NMDA decay time constant
t_nmda_rise_bn = 10 * ms # NMDA rise time constant
t_ampa_decay_bn = 4.2 * ms # AMPA decay time constant
t_ampa_rise_bn = 1.2 * ms # AMPA rise time constant
# Synaptic current equations
eq_soma_b = Equations('''
I_syn_b = I_nmda_gb + I_ampa_gb + I_nmda_mb + I_ampa_mb + I_nmda_bn + I_ampa_bn : amp
I_nmda_gb = g_nmda_gb*(vm - E_nmda)*s_nmda_gb*(1.0/(1 + exp(-gamma*vm)*(1.0/3.57))) : amp
I_ampa_gb = g_ampa_gb*(vm - E_ampa)*s_ampa_gb : amp
s_nmda_gb : 1
s_ampa_gb : 1
I_nmda_mb = g_nmda_mb*(vm - E_nmda)*s_nmda_mb*(1.0/(1 + exp(-gamma*vm)*(1.0/3.57))) : amp
I_ampa_mb = g_ampa_mb*(vm - E_ampa)*s_ampa_mb : amp
s_nmda_mb : 1
s_ampa_mb : 1
I_nmda_bn = g_nmda_bn*(vm - E_nmda)*s_nmda_bn*(1.0/(1 + exp(-gamma*vm)*(1.0/3.57))) : amp
I_ampa_bn = g_ampa_bn*(vm - E_ampa)*s_ampa_bn : amp
s_nmda_bn : 1
s_ampa_bn : 1
''')
# Brette-Gerstner
basket_eqs = Brette_Gerstner(Cm_b, gl_b, El_b, v_th_b, DeltaT_b, tauw = 100 * ms, a = .1 * nS)
basket_eqs += IonicCurrent('I = I_syn_b : amp')
basket_eqs += eq_soma_b
basket = NeuronGroup(N_basket, model = basket_eqs, threshold = 'vm > v_th_b',
reset = AdaptiveReset(Vr=v_reset_b, b = 0.0205*nA),
refractory = 2 * ms, compile = True)
# Initialization of membrane potential
basket.vm = El_b
basket_cl = {}
for bb in xrange(N_cl):
basket_cl[bb] = basket.subgroup(1)
#=======================================================================================================================
#=======================================================================================================================
# MOSSY CELLS
# Parameters
gl_m = 4.53 * nS # leakage conductance
El_m = -64 * mV # reversal-resting potential
Cm_m = 0.2521 * nfarad # membrane capacitance
v_th_m = -42 * mV # threshold potential
v_reset_m = -49 * mV # reset potential
DeltaT_m = 2 * mV # slope factor
# Synaptic Parameters
gamma = 0.072 * mV**-1 # Mg Concentration factor
alpha_nmda = 0.5 * ms**-1 # NMDA scale factor
alpha_ampa = 1 * ms**-1 # AMPA scale factor
alpha_gaba = 1 * ms**-1 # GABA scale factor
#AMPA/NMDA Kinetics
t_nmda_decay_m = 100 * ms # NMDA decay time constant
t_nmda_rise_m = 4 * ms # NMDA rise time constant
t_ampa_decay_m = 6.2 * ms # AMPA decay time constant
t_ampa_rise_m = 0.5 * ms # AMPA rise time constant
# Noise model Parameters
g_nmda_mn = 4.465 * nS # NMDA maximum conductance
g_ampa_mn = 4.7 * nS # AMPA maximum conductance
t_nmda_decay_mn = 100 * ms # NMDA decay time constant
t_nmda_rise_mn = 4 * ms # NMDA rise time constant
t_ampa_decay_mn = 6.2 * ms # AMPA decay time constant
t_ampa_rise_mn = 0.5 * ms # AMPA rise time constant
# Synaptic current equations
eq_soma_m = Equations('''
I_syn_m = I_ampa_gm + I_nmda_gm + I_ampa_mn + I_nmda_mn : amp
I_nmda_gm = g_nmda_gm*(vm - E_nmda)*s_nmda_gm*(1.0/(1 + exp(-gamma*vm)*(1.0/3.57))) : amp
I_ampa_gm = g_ampa_gm*(vm - E_ampa)*s_ampa_gm : amp
s_nmda_gm : 1
s_ampa_gm : 1
I_nmda_mn = g_nmda_mn*(vm - E_nmda)*s_nmda_mn*(1.0/(1 + exp(-gamma*vm)*(1.0/3.57))) : amp
I_ampa_mn = g_ampa_mn*(vm - E_ampa)*s_ampa_mn : amp
s_nmda_mn : 1
s_ampa_mn : 1
''')
# Brette-Gerstner
mossy_eqs = Brette_Gerstner(Cm_m, gl_m, El_m, v_th_m, DeltaT_m, tauw = 180 * ms, a = 1 * nS)
mossy_eqs += IonicCurrent('I = I_syn_m : amp')
mossy_eqs += eq_soma_m
mossy = NeuronGroup(N_mossy, model = mossy_eqs, threshold = 'vm > v_th_m',
reset = AdaptiveReset(Vr=v_reset_m, b = 0.0829*nA),
refractory = 2 * ms, compile = True)
# Initialization of membrane potential
mossy.vm = El_m
#=======================================================================================================================
#=======================================================================================================================
# HIPP CELLS
# Parameters
gl_h = 1.930 * nS # leakage conductance
El_h = -59 * mV # reversal-resting potential
Cm_h = 0.0584 * nF # membrane capacitance
v_th_h = -50 * mV # threshold potential
v_reset_h = -56 * mV # reset potential
DeltaT_h = 2 * mV # slope factor
# Synaptic Parameters
gamma = 0.072 * mV**-1 # Mg Concentration factor
alpha_nmda = 0.5 * ms**-1 # NMDA scale factor
alpha_ampa = 1 * ms**-1 # AMPA scale factor
alpha_gaba = 1 * ms**-1 # GABA scale factor
#AMPA/NMDA Kinetics
t_nmda_decay_h = 110 * ms # NMDA decay time constant
t_nmda_rise_h = 4.8 * ms # NMDA rise time constant
t_ampa_decay_h = 11.0 * ms # AMPA decay time constant
t_ampa_rise_h = 2.0 * ms # AMPA rise time constant
# NOISE
g_nmda_hn = 0.2 * nS # NMDA maximum conductance
g_ampa_hn = 0.2 * nS # AMPA maximum conductance
t_nmda_decay_hn = 100 * ms # NMDA decay time constant
t_nmda_rise_hn = 5.0 * ms # NMDA rise time constant
t_ampa_decay_hn = 11.0 * ms # AMPA decay time constant
t_ampa_rise_hn = 2.0 * ms # AMPA rise time constant
# Synaptic current equations
eq_soma_h = Equations('''
I_syn_h = I_nmda_eh + I_ampa_eh + I_nmda_hn + I_ampa_hn : amp
I_nmda_eh = g_nmda_eh*(vm - E_nmda)*s_nmda_eh*1./(1 + exp(-gamma*vm)/3.57) : amp
I_ampa_eh = g_ampa_eh*(vm - E_ampa)*s_ampa_eh : amp
s_nmda_eh : 1
s_ampa_eh : 1
I_nmda_hn = g_nmda_hn*(vm - E_nmda)*s_nmda_hn*(1.0/(1 + exp(-gamma*vm)*(1.0/3.57))) : amp
I_ampa_hn = g_ampa_hn*(vm - E_ampa)*s_ampa_hn : amp
s_nmda_hn : 1
s_ampa_hn : 1
''')
# Brette-Gerstner
hipp_eqs = Brette_Gerstner(Cm_h, gl_h, El_h, v_th_h, DeltaT_h, tauw = 93 * ms, a = .82 * nS)
hipp_eqs += IonicCurrent('I = I_syn_h : amp')
hipp_eqs += eq_soma_h
hipp = NeuronGroup(N_hipp, model = hipp_eqs, threshold = EmpiricalThreshold(threshold = v_th_h,refractory = 3*ms),
reset = AdaptiveReset(Vr=v_reset_h, b = 0.015*nA), compile = True, freeze = True)
# Initialization of membrane potential
hipp.vm = El_h
#=======================================================================================================================
#=======================================================================================================================
# *************************************** C O N N E C T I O N S ********************************************
#=======================================================================================================================
os.chdir('ConnectivityMatrices')
os.chdir('scale_'+str(scale_fac))
# EC CELLS ----> GRANULE CELLS
a = 3.5
# Synapses at 1st dendrite
nmda_eqs_eg = '''
dj_eg/dt = -j_eg / t_nmda_decay_g + alpha_nmda_g * x_eg * (1 - j_eg) : 1
dx_eg/dt = -x_eg / t_nmda_rise_g : 1
wNMDA_eg : 1
'''
synNMDA_eg = Synapses(Input_ec, granule, model = nmda_eqs_eg, pre = 'x_eg += wNMDA_eg', implicit=True, freeze=True)
granule.s_nmda_eg = synNMDA_eg.j_eg
synNMDA_eg.load_connectivity('syn_eg.txt')
synNMDA_eg.wNMDA_eg[:, :] = 1.0 * a
synNMDA_eg.delay[:, :] = 3 * ms
ampa_eqs_eg = '''
dy_eg/dt = -y_eg / t_ampa_decay_g + alpha_ampa * h_eg * (1 - y_eg) : 1
dh_eg/dt = -h_eg / t_ampa_rise_g : 1
wAMPA_eg : 1
'''
synAMPA_eg = Synapses(Input_ec, granule, model = ampa_eqs_eg, pre = 'h_eg += wAMPA_eg', implicit=True, freeze=True)
granule.s_ampa_eg = synAMPA_eg.y_eg
synAMPA_eg.load_connectivity('syn_eg.txt')
synAMPA_eg.wAMPA_eg[:, :] = 1.0 * a
synAMPA_eg.delay[:, :] = 3 * ms
# EC CELLS ---> HIPP CELLS
# The NMDA/AMPA synapses @ hipp cell
nmda_eqs_eh = '''
dj_eh/dt = -j_eh / t_nmda_decay_h + alpha_nmda * x_eh * (1 - j_eh) : 1
dx_eh/dt = -x_eh / t_nmda_rise_h : 1
wNMDA_eh : 1
'''
synNMDA_eh = Synapses(Input_ec, hipp, model = nmda_eqs_eh, pre = 'x_eh += wNMDA_eh', implicit=True, freeze=True)
hipp.s_nmda_eh = synNMDA_eh.j_eh
synNMDA_eh.load_connectivity('syn_eh.txt')
synNMDA_eh.wNMDA_eh[:, :] = 1.0
synNMDA_eh.delay[:, :] = 3.0 * ms
ampa_eqs_eh = '''
dy_eh/dt = -y_eh / t_ampa_decay_h + h_eh*alpha_ampa*(1 - y_eh) : 1
dh_eh/dt = -h_eh / t_ampa_rise_h : 1
wAMPA_eh : 1
'''
synAMPA_eh = Synapses(Input_ec, hipp, model = ampa_eqs_eh, pre = 'h_eh += wAMPA_eh', implicit=True, freeze=True)
hipp.s_ampa_eh = synAMPA_eh.y_eh
synAMPA_eh.load_connectivity('syn_eh.txt')
synAMPA_eh.wAMPA_eh[:, :] = 1.0
synAMPA_eh.delay[:, :] = 3.0 * ms
# GRANULE CELLS ---> MOSSY CELLS
# The NMDA/AMPA synapses @ mossy cell
nmda_eqs_gm = '''
dj_gm/dt = -j_gm / t_nmda_decay_m + alpha_nmda * x_gm * (1 - j_gm) : 1
dx_gm/dt = -x_gm / t_nmda_rise_m : 1
wNMDA_gm : 1
'''
synNMDA_gm = Synapses(granule, mossy, model = nmda_eqs_gm, pre = 'x_gm += wNMDA_gm', implicit=True, freeze=True)
mossy.s_nmda_gm = synNMDA_gm.j_gm
synNMDA_gm.load_connectivity('syn_gm.txt')
synNMDA_gm.wNMDA_gm[:, :] = 1.0
synNMDA_gm.delay[:, :] = 1.5 * ms
ampa_eqs_gm = '''
dy_gm/dt = -y_gm / t_ampa_decay_m + h_gm*alpha_ampa*(1 - y_gm) : 1
dh_gm/dt = -h_gm / t_ampa_rise_m : 1
wAMPA_gm : 1
'''
synAMPA_gm = Synapses(granule, mossy, model = ampa_eqs_gm, pre = 'h_gm += wAMPA_gm', implicit=True, freeze=True)
mossy.s_ampa_gm = synAMPA_gm.y_gm
synAMPA_gm.load_connectivity('syn_gm.txt')
synAMPA_gm.wAMPA_gm[:, :] = 1.0
synAMPA_gm.delay[:, :] = 1.5 * ms
# GRANULE CELLS ---> BASKET CELLS
# The NMDA/AMPA synapses @ basket cell
synNMDA_gb = {}
synAMPA_gb = {}
for gtob in xrange(N_cl):
nmda_eqs_gb = '''
dj_gb/dt = -j_gb / t_nmda_decay_b + alpha_nmda * x_gb * (1 - j_gb) : 1
dx_gb/dt = -x_gb / t_nmda_rise_b : 1
wNMDA_gb : 1
'''
synNMDA_gb[gtob] = Synapses(granule_cl[gtob], basket_cl[gtob], model = nmda_eqs_gb, pre = 'x_gb += wNMDA_gb', implicit=True, freeze=True)
basket_cl[gtob].s_nmda_gb = synNMDA_gb[gtob].j_gb
synNMDA_gb[gtob].connect_random(granule_cl[gtob], basket_cl[gtob], sparseness = 1.0)
synNMDA_gb[gtob].wNMDA_gb[:, :] = 1.0
synNMDA_gb[gtob].delay[:, :] = 0.8 * ms
ampa_eqs_gb = '''
dy_gb/dt = -y_gb / t_ampa_decay_b + h_gb*alpha_ampa*(1 - y_gb) : 1
dh_gb/dt = -h_gb / t_ampa_rise_b : 1
wAMPA_gb : 1
'''
synAMPA_gb[gtob] = Synapses(granule_cl[gtob], basket_cl[gtob], model = ampa_eqs_gb, pre = 'h_gb += wAMPA_gb', implicit=True, freeze=True)
basket_cl[gtob].s_ampa_gb = synAMPA_gb[gtob].y_gb
synAMPA_gb[gtob].connect_random(granule_cl[gtob], basket_cl[gtob], sparseness = 1.0)
synAMPA_gb[gtob].wAMPA_gb[:, :] = 1.0
synAMPA_gb[gtob].delay[:, :] = 0.8 * ms
# MOSSY CELLS ---> GRANULE CELLS
# The NMDA/AMPA synapses @ granule proximal dendrite (dendrite 2)
# 1st branch
nmda_eqs_mg = '''
dj_mg/dt = -j_mg / t_nmda_decay_g + alpha_nmda_g * x_mg * (1 - j_mg) : 1
dx_mg/dt = -x_mg / t_nmda_rise_g : 1
wNMDA_mg : 1
'''
synNMDA_mg = Synapses(mossy, granule, model = nmda_eqs_mg, pre = 'x_mg += wNMDA_mg', implicit=True, freeze=True)
granule.s_nmda_mg = synNMDA_mg.j_mg
synNMDA_mg.load_connectivity('syn_mg.txt')
synNMDA_mg.wNMDA_mg[:, :] = 1.0
synNMDA_mg.delay[:, :] = 3.0 * ms
ampa_eqs_mg = '''
dy_mg/dt = -y_mg / t_ampa_decay_g + h_mg * alpha_ampa * (1 - y_mg) : 1
dh_mg/dt = -h_mg / t_ampa_rise_g : 1
wAMPA_mg : 1
'''
synAMPA_mg = Synapses(mossy, granule, model = ampa_eqs_mg, pre = 'h_mg += wAMPA_mg', implicit=True, freeze=True)
granule.s_ampa_mg = synAMPA_mg.y_mg
synAMPA_mg.load_connectivity('syn_mg.txt')
synAMPA_mg.wAMPA_mg[:, :] = 1.0
synAMPA_mg.delay[:, :] = 3.0 * ms
# MOSSY CELL ---> BASKET CELLS
# The NMDA/AMPA synapses @ basket cell
nmda_eqs_mb = '''
dj_mb/dt = -j_mb / t_nmda_decay_b + alpha_nmda * x_mb * (1 - j_mb) : 1
dx_mb/dt = -x_mb / t_nmda_rise_b : 1
wNMDA_mb : 1
'''
synNMDA_mb = Synapses(mossy, basket, model = nmda_eqs_mb, pre = 'x_mb += wNMDA_mb', implicit=True, freeze=True)
basket.s_nmda_mb = synNMDA_mb.j_mb
synNMDA_mb.connect_random(mossy, basket, sparseness = 1.0)
synNMDA_mb.wNMDA_mb[:, :] = 1.0
synNMDA_mb.delay[:, :] = 3.0 * ms
ampa_eqs_mb = '''
dy_mb/dt = -y_mb / t_ampa_decay_b + h_mb*alpha_ampa*(1 - y_mb) : 1
dh_mb/dt = -h_mb / t_ampa_rise_b : 1
wAMPA_mb : 1
'''
synAMPA_mb = Synapses(mossy, basket, model = ampa_eqs_mb, pre = 'h_mb += wAMPA_mb', implicit=True, freeze=True)
basket.s_ampa_mb = synAMPA_mb.y_mb
synAMPA_mb.connect_random(mossy, basket, sparseness = 1.0)
synAMPA_mb.wAMPA_mb[:, :] = 1.0
synAMPA_mb.delay[:, :] = 3.0 * ms
# BASKET CELLS ----> GRANULE CELLS (INHIBITION @ soma)
# Synapses @ granule cell (soma)
syn_bg = {}
for btog in xrange(N_cl):
gaba_eqs_bg = '''
dz_bg/dt = -z_bg / t_gaba_decay_g + r_bg*alpha_gaba*(1 - z_bg) : 1
dr_bg/dt = -r_bg / t_gaba_rise_g : 1
w_bg : 1
'''
syn_bg[btog] = Synapses(basket_cl[btog], granule_cl[btog], model = gaba_eqs_bg, pre = 'r_bg += w_bg', implicit=True, freeze=True)
granule_cl[btog].s_gaba_bg = syn_bg[btog].z_bg
syn_bg[btog].connect_random(basket_cl[btog], granule_cl[btog], sparseness = 1.0)
syn_bg[btog].w_bg[:, :] = 1.0
syn_bg[btog].delay[:, :] = 0.85 * ms
# HIPP CELLS ----> GRANULE CELLS (INHIBITION @ distal dendrite)
# Synapses at granule cell distal dendrite (0)
# Synapses @ 1st branch
gaba_eqs_hg = '''
dz_hg/dt = -z_hg / t_gaba_decay_g + alpha_gaba * r_hg * (1 - z_hg) : 1
dr_hg/dt = -r_hg / t_gaba_rise_g : 1
w_hg : 1
'''
syn_hg = Synapses(hipp, granule, model = gaba_eqs_hg, pre = 'r_hg += w_hg', implicit=True, freeze=True)
granule.s_gaba_hg = syn_hg.z_hg
syn_hg.load_connectivity('syn_hg.txt')
syn_hg.w_hg[:, :] = 1.0
syn_hg.delay[:, :] = 1.6 * ms
############################################# N O I S E ################################################################
# GRANULE CELLS
noise_g = PoissonGroup(40, 2.2*Hz)
# DISTAL
# Synapses at dend00
nmda_eqs_gn = '''
dj_gn/dt = -j_gn / t_nmda_decay_g + alpha_nmda_g * x_gn * (1 - j_gn) : 1
dx_gn/dt = -x_gn / t_nmda_rise_g : 1
dy_gn/dt = -y_gn / t_ampa_decay_g + alpha_ampa * h_gn * (1 - y_gn) : 1
dh_gn/dt = -h_gn / t_ampa_rise_g : 1
w_gn : 1
'''
syn_gn = Synapses(noise_g, granule, model = nmda_eqs_gn,
pre = 'x_gn = w_gn; h_gn = w_gn', implicit=True, freeze=True)
granule.s_nmda_gn = syn_gn.j_gn
granule.s_ampa_gn = syn_gn.y_gn
syn_gn.connect_random(noise_g, granule, sparseness = 1.0)
syn_gn.w_gn[:, :] = 1.0
syn_gn.delay[:, :] = '10 * rand() * ms'
# BASKET CELLS
noise_b = PoissonGroup(20*N_basket, 3*Hz)
noise_b_cl = {}
for no in xrange(N_basket):
noise_b_cl[no] = noise_b.subgroup(20)
# Synapses at basket cell (noise role)
syn_bn = {}
for cell0 in xrange(N_basket):
nmda_eqs_bn = '''
dj_bn/dt = -j_bn / t_nmda_decay_bn + alpha_nmda * x_bn * (1 - j_bn) : 1
dx_bn/dt = -x_bn / t_nmda_rise_bn : 1
dy_bn/dt = -y_bn / t_ampa_decay_bn + alpha_ampa * h_bn * (1 - y_bn) : 1
dh_bn/dt = -h_bn / t_ampa_rise_bn : 1
w_bn : 1
'''
syn_bn[cell0] = Synapses(noise_b_cl[cell0], basket_cl[cell0], model = nmda_eqs_bn,
pre = 'x_bn = w_bn; h_bn = w_bn')
basket_cl[cell0].s_nmda_bn = syn_bn[cell0].j_bn
basket_cl[cell0].s_ampa_bn = syn_bn[cell0].y_bn
syn_bn[cell0].connect_random(noise_b_cl[cell0], basket_cl[cell0], sparseness = 1.0)
syn_bn[cell0].w_bn[:, :] = 1.0
syn_bn[cell0].delay[:, :] = '10 * rand() * ms'
# MOSSY CELLS
noise = PoissonGroup(30*N_mossy, 3.8*Hz)
noise_cl = {}
mossy_cl = {}
for no in xrange(N_mossy):
noise_cl[no] = noise.subgroup(20)
mossy_cl[no] = mossy[no]
# Synapses at mossy cell (noise role)
syn_mn = {}
for kk in xrange(N_mossy):
nmda_eqs_mn = '''
dj_mn/dt = -j_mn / t_nmda_decay_mn + alpha_nmda * x_mn * (1 - j_mn) : 1
dx_mn/dt = -x_mn / t_nmda_rise_mn : 1
dy_mn/dt = -y_mn / t_ampa_decay_mn + alpha_ampa * h_mn * (1 - y_mn) : 1
dh_mn/dt = -h_mn / t_ampa_rise_mn : 1
w_mn : 1
'''
syn_mn[kk] = Synapses(noise_cl[kk], mossy_cl[kk], model = nmda_eqs_mn,
pre = 'x_mn = w_mn; h_mn = w_mn')
mossy_cl[kk].s_nmda_mn = syn_mn[kk].j_mn
mossy_cl[kk].s_ampa_mn = syn_mn[kk].y_mn
syn_mn[kk].connect_random(noise_cl[kk], mossy_cl[kk], sparseness = 1.0)
syn_mn[kk].w_mn[:, :] = 1.0
syn_mn[kk].delay[:, :] = '10 * rand() * ms'
# HIPP Cells
noise_h = PoissonGroup(20*N_hipp, 3*Hz)
noise_h_cl = {}
hipp_cl = {}
for no in xrange(N_hipp):
noise_h_cl[no] = noise_h.subgroup(20)
hipp_cl[no] = hipp[no]
# Synapses at hipp cell (noise role)
syn_hn = {}
for cell2 in xrange(N_hipp):
nmda_eqs_hn = '''
dj_hn/dt = -j_hn / t_nmda_decay_hn + alpha_nmda * x_hn * (1 - j_hn) : 1
dx_hn/dt = -x_hn / t_nmda_rise_hn : 1
dy_hn/dt = -y_hn / t_ampa_decay_hn + alpha_ampa * h_hn * (1 - y_hn) : 1
dh_hn/dt = -h_hn / t_ampa_rise_hn : 1
w_hn : 1
'''
syn_hn[cell2] = Synapses(noise_h_cl[cell2], hipp_cl[cell2], model = nmda_eqs_hn,
pre = 'x_hn = w_hn; h_hn = w_hn')
hipp_cl[cell2].s_nmda_hn = syn_hn[cell2].j_hn
hipp_cl[cell2].s_ampa_hn = syn_hn[cell2].y_hn
syn_hn[cell2].connect_random(noise_h_cl[cell2], hipp_cl[cell2], sparseness = 1.0)
syn_hn[cell2].w_hn[:, :] = 1.0
syn_hn[cell2].delay[:, :] = '10 * rand() * ms'
#=======================================================================================================================
#=======================================================================================================================
# MONITORING
I_S = SpikeMonitor(Input_ec)
G_S = SpikeMonitor(granule)
#=======================================================================================================================
#=======================================================================================================================
# *************************************** S I M U L A T I O N S ********************************************
#=======================================================================================================================
#Simulation run
start_timestamp = time.time()
run(t1+simtime+t2, report='text', report_period = 10 *second)
sim_duration = time.time() - start_timestamp
print "\nDuration of simulation: " + str(sim_duration)
if not os.path.exists(maindir+'/results/'):
os.makedirs(maindir+'/results/')
if not os.path.exists(maindir+'/results/Control'):
os.makedirs(maindir+'/results/Control')
os.chdir(maindir+'/results/Control')
output_pattern = []
for spikes in xrange(N_granule):
output_pattern.append(len(G_S[spikes]))
np.save('output_pattern0d_'+overlap+'_'+str(trial_i[0]) + '_'+str(trial), output_pattern)
input_pattern = []
for spikes_i in xrange(len(Input_ec)):
input_pattern.append(len(I_S[spikes_i]))
np.save('input_pattern0d_'+overlap+'_'+str(trial_i[0]) + '_'+str(trial), input_pattern)