CRH modulates excitatory transmission and network physiology in hippocampus (Gunn et al. 2017)

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Accession:225906
This model simulates the effects of CRH on sharp waves in a rat CA1/CA3 model. It uses the frequency of the sharp waves as an output of the network.
References:
1 . Taxidis J, Coombes S, Mason R, Owen MR (2012) Modeling sharp wave-ripple complexes through a CA3-CA1 network model with chemical synapses. Hippocampus 22:995-1017 [PubMed]
2 . Gunn BG, Cox CD, Chen Y, Frotscher M, Gall CM, Baram TZ, Lynch G (2017) The Endogenous Stress Hormone CRH Modulates Excitatory Transmission and Network Physiology in Hippocampus. Cereb Cortex 27:4182-4198 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Abstract Wang-Buzsaki neuron; Pinsky-Rinzel CA1/3 pyramidal cell ;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: Brian; Python;
Model Concept(s): Brain Rhythms;
Implementer(s): Cox, Conor [cdcox1 at gmail.com];
Search NeuronDB for information about:  Gaba; Glutamate;
# -*- coding: utf-8 -*-
"""
Created on Tue May 10 11:07:00 2016

@author: Conor D. cox
@author: Conor D. Cox
Notes:
    this requires brian2 and python 2.7, though it might run on python 3 the print functions are all wrong.
    WARNING WARNING WARNING
    This requires a number of GB of ram equal to the number of seconds you are simulating. If your computer doesn't have that
    it will crash this can be ameliorated by changing the recording function down around  436ish. You can turn
    those off and it will require less RAM.
    The output of this, other than dozens of figures is the output array. You can save it as you see fit
    This code representes figure 7
    email me for any problems:
    cdcox1@gmail.com
"""

# -*- coding: utf-8 -*-
from __future__ import division
import numpy as np
from brian2 import *
import copy
import gc as gc11
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

def cluster_algorithim(nnzoom,i,innnnn):
    '''this takes the signal from a region and performs DBSCAN clustering on it
    it requires you to scale your time input to a reasonable spatial input, 
    in this case time was scaled by a factor of 10. Eps represents approximate
    cluster size.'''
    X=np.copy(nnzoom)
    X[:,0]=X[:,0]/10
    db = DBSCAN(eps=10, min_samples=5).fit(X)
    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
    core_samples_mask[db.core_sample_indices_] = True
    labels = db.labels_
    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
    # Black removed and is used for noise instead.
    unique_labels = set(labels)
    colors = plt.cm.prism(np.linspace(0, 1, len(unique_labels)))
    for k, col in zip(unique_labels, colors):
        if k == -1:
            # Black used for noise.
            col = 'k'
    
        class_member_mask = (labels == k)
    
        xy = X[class_member_mask & core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
                 markeredgecolor='k', markersize=14)
    
        xy = X[class_member_mask & ~core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
                 markeredgecolor='k', markersize=6)
    plt.plot(X[:,0],X[:,1],'+k')
    plt.title('Estimated number of clusters: %d' % n_clusters_)
    plt.show()
    plt.savefig('nnncca3clustconnect'+str(i)+str(innnnn)+'.png')
    med_clus_size=median(np.histogram(labels,bins=len(unique_labels)+1,range=(-1,len(unique_labels)))[0][1:-1])
    num_clust=len(unique_labels)
    return med_clus_size,num_clust
    
def make_targetting_array():
    targetting_count_array=[]
    j=1
    for i in range(1100):
        if i%11!=0:
            targetting_count_array.append(j)
            j+=1
        else:
            targetting_count_array.append(0)
    targetting_count_array=np.array(targetting_count_array)
    return targetting_count_array
  
  

def randomize_and_edge_check(real_i,i,targetting_count_array,number_of_neurons):
    check=np.random.normal(scale=1000)
    target=np.round(check/10)
    temp_tar=real_i+target
    if ((i+target)<0) or ((temp_tar)>=len(targetting_count_array)):
        checker=999999999
        return checker,check,12345678
    checker=targetting_count_array[temp_tar]
    if checker>number_of_neurons:
        checker=999999999
        return checker,check,12345678
    return checker,check,temp_tar
    
def randomize_and_edge_checkint(real_i,i,targetting_count_array):
    check=np.random.normal(scale=100)
    target=np.round(check/10)
    temp_tar=real_i+target
    if ((real_i+target)<0) or ((temp_tar)>=len(targetting_count_array)):
        checker=999999999
        return checker,check,12345678
    checker=targetting_count_array[temp_tar]
    if checker>number_of_ca1_neurons:
        checker=999999999
        return checker,check,12345678
    return checker,check,temp_tar

  
def excite_to_excite_wire_it_up(number_of_neurons,exkij,exkijsd,inkj,inkijsd):
    connectivity=[]
    connectivityin=[]
    delayex=[]
    delayin=[]
    targetting_count_array=make_targetting_array()
    for i in range(1,number_of_neurons+1):
        in_count=0
        numb_in_connect=np.int(np.round(np.random.normal(loc=inkj,scale=inkijsd)))
        if exkij!=0:
            numb_connect=np.int(np.round(np.random.normal(loc=exkij,scale=exkijsd)))
        else:
            numb_connect=0
        real_i=np.where(targetting_count_array==i)[0][0]
        for j in range(1,numb_connect):  
            checker,check,temp_tar=randomize_and_edge_check(real_i,i,targetting_count_array,number_of_neurons)
            if checker==999999999:
                continue
            while temp_tar%11==0 or checker==i:
                if in_count<numb_in_connect and checker!=i and temp_tar!=0:
                    connectivityin.append([i,np.int(temp_tar/11)])
                    delayin.append(np.abs(np.round(check*um/speed,decimals=4)))
                    in_count+=1
                checker,check,temp_tar=randomize_and_edge_check(real_i,i,targetting_count_array,number_of_neurons)
                if checker==999999999:
                    continue
            if checker==999999999:
                continue
            connectivity.append([i,checker])
            delayex.append(np.abs(np.round(check*um/speed,decimals=4)))
        while in_count<numb_in_connect:
            checker,check,temp_tar=randomize_and_edge_check(real_i,i,targetting_count_array,number_of_neurons)
            if checker==999999999:
                continue
            if temp_tar%11==0 and temp_tar!=0:
                connectivityin.append([i,np.int(temp_tar/11)])
                delayin.append(np.abs(np.round(check*um/speed,decimals=4)))
                in_count+=1
    connectivity=np.array(connectivity)
    connectivityin=np.array(connectivityin)
    return connectivity,connectivityin,delayex,delayin
    
def ca1in_wire_it_up(number_of_ca1_neurons,exkij,exkijsd,inkj,inkijsd):
    speed2=100*um/ms
    connectivity=[]
    connectivityin=[]
    delayex=[]
    delayin=[]
    targetting_count_array=make_targetting_array()
    for i in range(1,int(number_of_ca1_neurons/10)+1):
        in_count=0
        numb_in_in_connect=np.int(np.round(np.random.normal(loc=inkj,scale=inkijsd)))
        numb_2_ex_connect=np.int(np.round(np.random.normal(loc=exkij,scale=exkijsd)))
        real_i=np.where(targetting_count_array==i)[0][0]
        for j in range(1,numb_2_ex_connect):  
            checker,check,temp_tar=randomize_and_edge_checkint(i*11,i,targetting_count_array)
            if checker==999999999:
                continue
            while temp_tar%11==0 or checker==i:
                if in_count<numb_in_in_connect and checker!=i and temp_tar!=0:
                    connectivityin.append([i,np.int(temp_tar/11)])
                    delayin.append(np.abs(np.round(check*um/speed2,decimals=4)))
                    in_count+=1
                checker,check,temp_tar=randomize_and_edge_checkint(i*11,i,targetting_count_array)
                if checker==999999999:
                    continue
            if checker==999999999:
                continue
            connectivity.append([i,checker])
            delayex.append(np.abs(np.round(check*um/speed2,decimals=4)))
        while in_count<numb_in_in_connect:
            checker,check,temp_tar=randomize_and_edge_checkint(i*11,i,targetting_count_array)
            if checker==999999999:
                continue
            if temp_tar%11==0 and temp_tar!=0:
                connectivityin.append([i,np.int(temp_tar/11)])
                delayin.append(np.abs(np.round(check*um/speed2,decimals=4)))
                in_count+=1
    connectivity=np.array(connectivity)
    connectivityin=np.array(connectivityin)
    return connectivity,connectivityin,delayex,delayin

def in2ex_wire_it_up(number_of_ca3_neurons,ca3_inpykij,ca3_inpykij_sd):
    #wires up CA3 in2ex
    targetting_count_array=make_targetting_array()
    connectivity=[]
    delay=[]
    for i in range(1,101):
        numb_connect=np.int(np.round(np.random.normal(loc=ca3_inpykij,scale=ca3_inpykij_sd)))
        cell_pos=i*11
        for j in range(1,numb_connect):  
            step=np.random.uniform(-1.5*100,1.5*100)
            cell_step=step/10
            target_cell=np.int(np.round(cell_pos+cell_step))

            if target_cell<0 or (target_cell>=len(targetting_count_array)):
                continue
            real_target=targetting_count_array[target_cell]
            while real_target==0:
                step=np.random.uniform(-1.5*100,1.5*100)
                cell_step=step/10
                target_cell=np.int(np.round(cell_pos+cell_step))

                if target_cell<0 or (target_cell>=len(targetting_count_array)):
                    continue
                real_target=targetting_count_array[target_cell] 
            connectivity.append([i,real_target])
            delay.append((np.abs(np.round(step*um/speed,decimals=4))))
    connectivity=np.array(connectivity)
    return connectivity,delay
    
def randomize_and_edge_checkSC(real_i,i,targetting_count_array,number_of_neurons):
    check=np.random.normal(scale=1200)
    target=np.round(check/10)
    temp_tar=real_i+target
    if ((i+target)<0) or ((temp_tar)>=len(targetting_count_array)):
        checker=999999999
        return checker,check,12345678
    checker=targetting_count_array[temp_tar]
    if checker>number_of_neurons:
        checker=999999999
        return checker,check,12345678
    return checker,check,temp_tar
    
def schafer_collaterals(number_of_neurons,exkij,exkijsd,inkj,inkijsd):
    connectivity=[]
    connectivityin=[]
    delayex=[]
    delayin=[]
    targetting_count_array=make_targetting_array()
    for i in range(1,number_of_neurons+1):
        if exkij!=0:
            numb_connect=np.int(np.round(np.random.normal(loc=exkij,scale=exkijsd)))
        else:
            numb_connect=0
        real_i=np.where(targetting_count_array==i)[0][0]
        for j in range(1,numb_connect):  
            checker,check,temp_tar=randomize_and_edge_checkSC(real_i,i,targetting_count_array,number_of_neurons)
            if checker==999999999:
                continue
            if temp_tar%11==0 and temp_tar!=0:
                dupe_rate=13
                for k in range(dupe_rate):
                    connectivityin.append([i,np.int(temp_tar/11)])
                    delayin.append(np.abs(np.round(check*um/speed,decimals=4)))
            else:
                dupe_rate=np.abs(np.round(13+np.random.normal(scale=13)))
                for k in range(int(dupe_rate)):
                    connectivity.append([i,checker])
                    delayex.append(np.abs(np.round(check*um/speed,decimals=4)))
    connectivity=np.array(connectivity)
    connectivityin=np.array(connectivityin)
    delayex=delayex+1000*um/speed
    delayin=delayin+1000*um/speed
    return connectivity,connectivityin,delayex,delayin    
    
def generate_dentate_gyrus(number_of_cells,frequency_of_spiking,length_of_simulation):
    frequency=frequency_of_spiking/1000
    firing_array=np.random.rand(number_of_cells,np.int(length_of_simulation/ms))
    firing_array=firing_array<frequency
    indicies,times=np.where(firing_array)
    dentate = SpikeGeneratorGroup(number_of_cells, indicies, times*ms)
    return dentate
    
def main(output_array,innnnn,clust_array):    
    Pinksy_rinzel_eqs='''    
    dVs/dt=(-gLs*(Vs-VL)-gNa*(Minfs**2)*hs*(Vs-VNa)-gKdr*ns*(Vs-VK)+(gc/pp)*(Vd-Vs)+(Ip0-Iinssyn)/pp)/Cm : volt
    dVd/dt=(-gLd*(Vd-VL)-ICad-gKahp*qd*(Vd-VK)-gKC*cd*chid*(Vd-VK)+(gc*(Vs-Vd))/(1.0-pp)-Isyn/(1.0-pp))/Cm : volt
    dCad/dt=  -0.13*ICad/uamp/ms*scaler-0.075*Cad/ms : 1
    dhs/dt=  alphahs-(alphahs+betahs)*hs : 1
    dns/dt=  alphans-(alphans+betans)*ns : 1
    dsd/dt=  alphasd-(alphasd+betasd)*sd : 1
    dcd/dt=  alphacd-(alphacd+betacd)*cd : 1
    dqd/dt=  alphaqd-(alphaqd+betaqd)*qd : 1
    ICad=     gCa*sd*sd*(Vd-VCa) : amp
    alphams=  0.32*(-46.9-Vs/mV)/(exp((-46.9-Vs/mV)/4.0)-1.0)/ms : Hz
    betams=   0.28*(Vs/mV+19.9)/(exp((Vs/mV+19.9)/5.0)-1.0)/ms : Hz
    Minfs=    alphams/(alphams+betams) : 1
    alphans=  0.016*(-24.9-Vs/mV)/(exp((-24.9-Vs/mV)/5.0)-1.0)/ms : Hz
    betans=   0.25*exp(-1.0-0.025*Vs/mV)/ms : Hz
    alphahs=  0.128*exp((-43.0-Vs/mV)/18.0)/ms : Hz
    betahs=   4.0/(1.0+exp((-20.0-Vs/mV)/5.0))/ms : Hz
    alphasd=  1.6/(1.0+exp(-0.072*(Vd/mV-5.0)))/ms : Hz
    betasd=   0.02*(Vd/mV+8.9)/(exp((Vd/mV+8.9)/5.0)-1.0)/ms : Hz
    alphacd=((Vd/mV<=-10)*exp((Vd/mV+50.0)/11-(Vd/mV+53.5)/27)/18.975+(Vd/mV>-10)*2.0*exp((-53.5-Vd/mV)/27.0))/ms  : Hz
    betacd=   ((Vd/mV<=-10)*(2.0*exp((-53.5-Vd/mV)/27.0)-alphacd*ms)+(Vd/mV>-10)*0)/ms : Hz
    alphaqd=  clip(0.00002*Cad,0,0.01)/ms : Hz
    betaqd=   0.001/ms : Hz
    chid=     clip(Cad/250.0,0,1.0) : 1
    INMDA=gNMDA*Si*(1+0.28*exp(-0.062*(Vs/mV)))**(-1)*(Vd-Vsyn) : amp
    Hxs=0<=((Vs/mV+50)*1) : 1
    Hxw=0<=((Vs/mV+40)*1) : 1
    dSi/dt=(Hxs-Si/150)/second : 1
    Isyn=Issyn+INMDA+Iapp : amp
    Issyn=gbarsyn*clip(ssyn,0,7000)*(Vd-Vsyn): amp
    dssyn/dt=-ssyn/tausyn: 1
    dinssyn/dt=-inssyn/tauisyn: 1
    Iinssyn=gbarsyn*clip(inssyn,0,7000)*(Vs-VK): amp
    dIapp/dt=0*amp/ms : amp
    '''

    CA1_pyramidal = NeuronGroup(number_of_ca1_neurons+1, Pinksy_rinzel_eqs,threshold='Vs>-20*mV',refractory='Vs>-60*mV',method='euler')
    CA1_pyramidal.Vs='(randn()*60*.05-60)*mV'
    CA1_pyramidal.Vd='(randn()*65*.05-65)*mV'
    CA1_pyramidal.hs=.999
    CA1_pyramidal.ns=.0001
    CA1_pyramidal.sd=.009
    CA1_pyramidal.cd=.007
    CA1_pyramidal.qd=.01
    CA1_pyramidal.Cad=.20
    CA1_pyramidal.Si=0
    CA1_pyramidal.ssyn=0
    CA1_pyramidal.inssyn=0
    
    buzsaki_eqs = '''
    dv/dt = (-gNai*m**3*h*(v-VNa)-gKi*n**4*(v-VK)-gL*(v-VL2)-Issyn-Iinssyn-Iapp2)/Cmi : volt
    m = alpha_m/(alpha_m+beta_m) : 1
    alpha_m = -0.1/mV*(v+35*mV)/(exp(-0.1/mV*(v+35*mV))-1)/ms : Hz
    beta_m = 4*exp(-(v+60*mV)/(18*mV))/ms : Hz
    dh/dt = 5*(alpha_h*(1-h)-beta_h*h) : 1
    alpha_h = 0.07*exp(-(v+58*mV)/(20*mV))/ms : Hz
    beta_h = 1./(exp(-0.1/mV*(v+28*mV))+1)/ms : Hz
    dn/dt = 5*(alpha_n*(1-n)-beta_n*n) : 1
    alpha_n = -0.01/mV*(v+34*mV)/(exp(-0.1/mV*(v+34*mV))-1)/ms : Hz
    beta_n = 0.125*exp(-(v+44*mV)/(80*mV))/ms : Hz
    Issyn=gbarsyn*(clip(ssyn,0,7000))*(v-Vsyn): amp
    dssyn/dt=-ssyn/tausyn: 1
    dinssyn/dt=-inssyn/taguisyn: 1
    Iinssyn=gbarsyn*clip(inssyn,0,7000)*(v-VK):amp
    '''
    
    Ca1_interneuron=NeuronGroup(np.round(number_of_ca1_neurons/10+1),buzsaki_eqs,threshold='v>-20*mV',refractory='v>-60*mV',method='euler')
    Ca1_interneuron.v='(randn()*60*.05-60)*mV'
    Ca1_interneuron.h=.999
    Ca1_interneuron.n=.0001
    Ca1_interneuron.ssyn=0
    
    ca1_pypy_kij=0
    ca1_pypy_kij_sd=ca1_pypy_kij/20
    ca1_pyin_kij=20
    ca1_pyin_kij_sd=ca1_pyin_kij/20
    connectivity2ex,connectivity2in,delayex,delayin=excite_to_excite_wire_it_up(number_of_ca1_neurons,ca1_pypy_kij,ca1_pypy_kij_sd,ca1_pyin_kij,ca1_pyin_kij_sd)
    ca1_inpykij=400
    ca1_inpykij_sd=ca1_inpykij/20
    ca1in2inkij=100
    ca1in2iniij_sd=ca1in2inkij/20
    connectivityin2ex,connectivityin2in,delayinex,delayinin=ca1in_wire_it_up(number_of_ca1_neurons,ca1_inpykij,ca1_inpykij_sd,ca1in2inkij,ca1in2iniij_sd)
    
    Ca1_in_in=Synapses(Ca1_interneuron,Ca1_interneuron,pre='inssyn+=5')
    Ca1_in_in.connect(i=list(connectivityin2in[0:,0]),j=list(connectivityin2in[0:,1]))
    Ca1_in_in.delay=delayinin[0:]
    
    Ca1_py_in=Synapses(CA1_pyramidal,Ca1_interneuron,pre='ssyn+=2.5')
    
    Ca1_py_in.connect(i=list(connectivity2in[0:,0]),j=list(connectivity2in[0:,1]))
    Ca1_py_in.delay=delayin[0:]
    
    Ca1_in_py=Synapses(Ca1_interneuron,CA1_pyramidal,pre='inssyn+=15')
    Ca1_in_py.connect(i=list(connectivityin2ex[0:,0]),j=list(connectivityin2ex[0:,1]))
    Ca1_in_py.delay=delayinex[0:]
    
    number_of_ca3_neurons=1000
    
    CA3_pyramidal = NeuronGroup(number_of_ca3_neurons+1, Pinksy_rinzel_eqs,threshold='Vs>-20*mV',refractory='Vs>-60*mV',method='euler')
    CA3_pyramidal.Vs='(randn()*60*.05-60)*mV'
    CA3_pyramidal.Vd='(randn()*65*.05-65)*mV'
    CA3_pyramidal.hs=.999
    CA3_pyramidal.ns=.0001
    CA3_pyramidal.sd=.009
    CA3_pyramidal.cd=.007
    CA3_pyramidal.qd=.01
    CA3_pyramidal.Cad=.20
    CA3_pyramidal.Si=0
    CA3_pyramidal.ssyn=0
    CA3_pyramidal.inssyn=0
    CA3_pyramidal.Iapp=0*nA

    Ca3_interneuron=NeuronGroup(np.round(number_of_ca3_neurons/10+1),buzsaki_eqs,threshold='v>-20*mV',refractory='v>-60*mV',method='euler')
    Ca3_interneuron.v=-60*mV
    Ca3_interneuron.h=.999
    Ca3_interneuron.n=.0001
    Ca3_interneuron.ssyn=0
    
    ca3_pypy_kij=55
    ca3_pypy_kij_sd=ca3_pypy_kij/20
    ca3_pyin_kij=5
    ca3_pyin_kij_sd=ca3_pyin_kij/20
    connectivity2ex,connectivity2in,delayex,delayin=excite_to_excite_wire_it_up(number_of_ca3_neurons,ca3_pypy_kij,ca3_pypy_kij_sd,ca3_pyin_kij,ca3_pyin_kij_sd)
    ca3_inpykij=68
    ca3_inpykij_sd=ca3_inpykij/20
    connectivityin2ex,delayinin=in2ex_wire_it_up(number_of_ca3_neurons,ca3_inpykij,ca3_inpykij_sd)
    

    Ca3_py_in=Synapses(CA3_pyramidal,Ca3_interneuron,pre='ssyn+=3')
    
    Ca3_py_in.connect(i=list(connectivity2in[0:,0]),j=list(connectivity2in[0:,1]))
    Ca3_py_in.delay=delayin[0:]
    print 'ca3'
    
    Ca3_in_py=Synapses(Ca3_interneuron,CA3_pyramidal,pre='inssyn+=50')
    Ca3_in_py.connect(i=list(connectivityin2ex[0:,0]),j=list(connectivityin2ex[0:,1]))
    
    exkijsc=130
    exkijsdsc=exkijsc/20
    scconnectivityex,scconnectivityin,scdelayex,scdelayin=schafer_collaterals(number_of_ca1_neurons,exkijsc,exkijsdsc,0,0)
    SC_py_py=Synapses(CA3_pyramidal,CA1_pyramidal,pre='ssyn+=15')
    SC_py_py.connect(i=list(scconnectivityex[0:,0]),j=list(scconnectivityex[0:,1]))
    SC_py_py.delay=scdelayex[0:]
    SC_py_in=Synapses(CA3_pyramidal,Ca1_interneuron,pre='ssyn+=15')
    SC_py_in.connect(i=list(scconnectivityin[0:,0]),j=list(scconnectivityin[0:,1]))
    SC_py_in.delay=scdelayin[0:]
    strength_of_input=1
    Ca3str=str(np.round(strength_of_input*15,1))
    Ca3_py_py=Synapses(CA3_pyramidal,CA3_pyramidal,pre='ssyn+='+Ca3str)
    Ca3_py_py.connect(i=list(connectivity2ex[0:,0]),j=list(connectivity2ex[0:,1]))
    Ca3_py_py.delay=delayex[0:]    
    simulation_len=10000*ms
    
    net=Network(Ca3_interneuron,CA1_pyramidal,Ca1_interneuron,CA3_pyramidal,Ca1_py_in,Ca1_in_in,Ca1_in_py,Ca3_py_in,Ca3_in_py,SC_py_py,SC_py_in)
    net.add(SpikeMonitor(CA1_pyramidal))
    net.add(SpikeMonitor(CA3_pyramidal))
    net.add(SpikeMonitor(Ca1_interneuron))
    net.add(SpikeMonitor(Ca3_interneuron))
    net.add(StateMonitor(CA1_pyramidal, True ,record=True,dt=.5*ms))
    net.add(StateMonitor(Ca1_interneuron,True,record=True,dt=.5*ms))     
    net.add(StateMonitor(CA3_pyramidal, True ,record=True,dt=.5*ms))
    net.add(StateMonitor(Ca3_interneuron,True,record=True,dt=.5*ms))
    out_spikes_ca1=[]
    out_spikes_ca3=[]
    med_clust=[]
    clust_size=[]
    rcParams['figure.figsize'] = 5, 5
    #for i in np.arange(6,7,2)*.01:
    for i in [15,16,17,18,19,20,21,22,23,24]:
        i=i*.01
        print str(i)+'nA'
        net['neurongroup_2'].Iapp=-1*i*nA
        net.store()
        net.store()
        print len(net.objects)
        net.run(simulation_len, report='text')
        plt.cla()
        plt.clf()
        plot(net['spikemonitor'].t/ms,net['spikemonitor'].i,'.k')
        plt.axis('off')
        print(max(net['spikemonitor'].t/ms))
        plt.show()
        plt.axis((0,simulation_len/ms,0,1000))
        plt.savefig('nnnlowstartdgcellsconnect'+str(i)+str(innnnn)+'.png')
        plt.cla()
        plt.clf()
        plt.clf()
        plot(net['spikemonitor_1'].t/ms,net['spikemonitor_1'].i,'.k')
        plt.axis('off')
        print(max(net['spikemonitor_1'].t/ms))
        plt.axis((0,simulation_len/ms,0,1000))
        plt.show()
        plt.savefig('nnnca3lowstartdgcellsconnect'+str(i)+str(innnnn)+'.png')
        plt.cla()
        plt.clf()
        plt.plot(net['statemonitor'][0].t,(np.sum(net['statemonitor'].Vd[500:550],0)+np.sum(net['statemonitor_1'].v[50:55],0))/55/mV)
        plt.axis((0,5,-70,-10))
        plt.savefig('nnnlowstartrecordingdgcellsconnect'+str(i)+str(innnnn)+'.eps')
        plt.cla()
        plt.clf()
        #plt.plot(net['statemonitor'][0].t,(np.sum(net['statemonitor_2'].Vd[500:550],0)+np.sum(net['statemonitor_3'].v[50:55],0))/55/mV)
        #plt.axis((0,5,-70,-10))
        #plt.savefig('nnnlowstartrecordinca3cellsconnect'+str(i)+str(innnnn)+'.eps')
        spikes_ca1=np.where(np.histogram(net['spikemonitor'].t,bins=200)[0]>500)[0]
        filt_spikes=[]
        for sn,spike in enumerate(spikes_ca1):
            if spikes_ca1[sn]-spikes_ca1[sn-1]<3:
                continue
            else:
                filt_spikes.append(spike)
        spikes_ca3=np.where(np.histogram(net['spikemonitor_1'].t,bins=200)[0]>100)[0]
        filt_spikes_ca3=[]
        for sn,spike in enumerate(spikes_ca3):
            if spikes_ca3[sn]-spikes_ca3[sn-1]<3:
                continue
            else:
                filt_spikes_ca3.append(spike)
        out_spikes_ca1.append(len(filt_spikes))
        out_spikes_ca3.append(len(filt_spikes_ca3))
        nnzoom=np.array([net['spikemonitor_1'].t/ms,net['spikemonitor_1'].i])
        nnzoom=nnzoom.T
        med_clus_size,num_clust=cluster_algorithim(nnzoom,i,innnnn)
        net.restore()
        med_clust.append(med_clus_size)
        clust_size.append(num_clust)
    output_array.append([out_spikes_ca1,out_spikes_ca3])
    clust_array.append([med_clust,clust_size])
    del net
    return output_array,clust_array
    
speed=500*um/ms
number_of_ca1_neurons=1000
kij={'ca3pypy':55,'ca3pyin':5}
defaultclock.dt = 0.05*ms
'''base values requireed for PR cells'''
Isyn=0*uamp
areapyr=50000*um**2
test=areapyr
Ip0=-0.3*nA
gLs=0.1  *msiemens*cm**-2*test
gLd=0.1  *msiemens*cm**-2*test
gNa=30  *msiemens*cm**-2*test
gKdr=15  *msiemens*cm**-2*test
gCa=7  *msiemens*cm**-2*test
gKahp=0.8*msiemens*cm**-2*test  
gKC=15  *msiemens*cm**-2*test
VNa=60*mV
VCa=80*mV
VK=-75*mV
VL=-60*mV
Vsyn=0*mV 
vtsyn=0*mV 
gc=1.75*usiemens
pp=0.5
Cm=3  *uF*cm**-2*test
gNMDA=0*msiemens*cm**-2*test
gampa=0*msiemens*cm**-2*test
gbarsyn=1*nS
tausyn= 2*ms
tauisyn=7*ms
scaler=(10**8*um**2/(test))
'''base values required for buzsaki cells'''
interneuron_size=20000*um**2
Cmi = 1*uF/cm**2*interneuron_size
gL = 0.1*msiemens/cm**2*interneuron_size
VL2 = -65*mV
gNai = 35*msiemens/cm**2*interneuron_size
gKi = 9*msiemens/cm**2*interneuron_size
Esyn=-75*mV
taguisyn=2*ms
Iapp2=0*nA
'''setting up empty data arrays for data storage'''
output_array=[]
clust_array=[]
'''this allows the simulaiton to be run multiple times to capture a range of randomized connectivity values'''
for i in range(10):
    gc11.collect()
    output_array,clust_array=main(output_array,i,clust_array)

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