{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from neuron import h,gui\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"N_sections = 11\n",
"h.celsius=22\n",
"nlist=[]\n",
"gcci = [h.Section() for i in range(N_sections)]\n",
"nlist.append(gcci)\n",
"gccd = [h.Section() for i in range(N_sections)]\n",
"nlist.append(gccd)\n",
"gci = [h.Section() for i in range(N_sections)]\n",
"nlist.append(gci)\n",
"gcd = [h.Section() for i in range(N_sections)]\n",
"nlist.append(gcd)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in range(N_sections-1):\n",
" gcci[i+1].connect(gcci[i],1,0)\n",
" gccd[i+1].connect(gccd[i],1,0)\n",
"\n",
"for i in range(N_sections-1):\n",
" gci[i+1].connect(gci[i],1,0)\n",
"for i in range(N_sections-1):\n",
" gcd[i+1].connect(gcd[i],1,0) "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in nlist:\n",
" i[0].diam=10\n",
" i[0].L=10\n",
" i[0].nseg=3\n",
" \n",
"for i in range(1,N_sections):\n",
" for j in nlist:\n",
" j[i].nseg=3\n",
" j[i].L=20\n",
" j[i].Ra=100\n",
" gcd[i].diam=2-(i-1)*0.088 #(2-1.2)/9\n",
" gci[i].diam=2.7-(i-1)*0.166 #(2.7-1.2)/9 \n",
" \n",
" gccd[i].diam=2-(i-1)*0.088 #(2-1.2)/9\n",
" gcci[i].diam=2.7-(i-1)*0.166 #(2.7-1.2)/9"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"for i in range(N_sections):\n",
" for j in nlist:\n",
" j[i].insert('pas')\n",
" j[i].g_pas=0.0002\n",
" j[i].insert('kfast')\n",
" j[i].insert('nat')\n",
" j[i].insert('nadp')\n",
" j[i].TotalPump_nadp=0\n",
" \n",
" \n",
"gcd[0].gbar_nat=4.1\n",
"gcd[0].gbar_kfast=4.1/80\n",
"\n",
"gcd[1].gbar_nat=4.1\n",
"gcd[1].gbar_kfast=4.1/80\n",
"\n",
"gci[0].gbar_nat=2.0\n",
"gci[0].gbar_kfast=2.0/80\n",
"\n",
"gci[1].gbar_nat=2.0\n",
"gci[1].gbar_kfast=2.0/80\n",
"\n",
"gcina=[2,2]\n",
"gcdna=[4.1,4.1]\n",
"\n",
"for i in range(2,N_sections): \n",
" gci[i].gbar_nat=(gci[i-1].gbar_nat)+0.05*(gci[i-1].gbar_nat)\n",
" gci[i].gbar_kfast=(gci[i-1].gbar_kfast)+0.05*(gci[i-1].gbar_kfast)\n",
" gcina.append(round(gci[i].gbar_nat,5))\n",
" \n",
" \n",
" gcd[i].gbar_nat=(gcd[i-1].gbar_nat)-0.12*(gcd[i-1].gbar_nat)\n",
" gcd[i].gbar_kfast=(gcd[i-1].gbar_kfast)-0.12*(gcd[i-1].gbar_kfast)\n",
" gcdna.append(round(gcd[i].gbar_nat,5))\n",
" \n",
"avnared=2.2*((2.7+2.8+2.2+4.2)/4) #mM\n",
"avnablu=2.2*((6.5+6.3+7.2+11)/4) #mM\n",
"\n",
"avnaredn=2.2*((2.7+2.8)/2) #mM\n",
"avnablun=2.2*((6.5+6.3+7.2)/3) #mM"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'hoc.HocObject' object has no attribute 'Ipulse1'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-1b9652c07819>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpulscd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIpulse1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgccd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mpulscd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mton\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpulscd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoff\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mpulscd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpulscd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mamp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;31m#na\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'hoc.HocObject' object has no attribute 'Ipulse1'"
]
}
],
"source": [
"pulscd=h.Ipulse1(0.5,sec=gccd[0])\n",
"pulscd.ton=5\n",
"pulscd.toff=15\n",
"pulscd.num=20\n",
"pulscd.amp=1#na\n",
"\n",
"pulsci=h.Ipulse1(0.5,sec=gcci[0])\n",
"pulsci.ton=5\n",
"pulsci.toff=15\n",
"pulsci.num=20\n",
"pulsci.amp=1#na\n",
"\n",
"pulsd=h.Ipulse1(0.5,sec=gcd[0])\n",
"pulsd.ton=5\n",
"pulsd.toff=15\n",
"pulsd.num=20\n",
"pulsd.amp=1 #na\n",
"\n",
"pulsi=h.Ipulse1(0.5,sec=gci[0])\n",
"pulsi.ton=5\n",
"pulsi.toff=15\n",
"pulsi.num=20\n",
"pulsi.amp=1 #na\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"time_h = h.Vector()\n",
"time_h.record(h._ref_t)\n",
"\n",
"inareci=[h.Vector() for i in range(N_sections)]\n",
"inarecd=[h.Vector() for i in range(N_sections)]\n",
"\n",
"vrec_0=h.Vector()\n",
"vrec_1=h.Vector()\n",
"vrec_2=h.Vector()\n",
"vrec_3=h.Vector()\n",
"vrec_4=h.Vector()\n",
"vrec_5=h.Vector()\n",
"\n",
"vrec_p1=h.Vector()\n",
"vrec_p2=h.Vector()\n",
"vrec_p3=h.Vector()\n",
"\n",
"vrec_0.record(gcd[0](0.5)._ref_v)\n",
"vrec_1.record(gci[0](0.5)._ref_v)\n",
"\n",
"vrec_p1.record(gccd[0](0.5)._ref_v)\n",
"\n",
"vrec_2.record(gcd[1](0.5)._ref_v)\n",
"vrec_3.record(gci[1](0.5)._ref_v)\n",
"\n",
"vrec_p2.record(gccd[1](0.5)._ref_v)\n",
"\n",
"vrec_4.record(gcd[10](0.5)._ref_v)\n",
"vrec_5.record(gci[10](0.5)._ref_v)\n",
"\n",
"vrec_p3.record(gccd[10](0.5)._ref_v)\n",
"\n",
"for i in range(N_sections):\n",
" inarecd[i].record(gcd[i](0.5)._ref_ina)\n",
" inareci[i].record(gci[i](0.5)._ref_ina)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"h.tstop =100\n",
"h.run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"plt.suptitle('Vm',fontsize=14)\n",
"\n",
"plt.subplot(321).set_title('decreasing pattern')\n",
"plt.plot(time_h,vrec_0)\n",
"plt.legend(['Soma'])\n",
"\n",
"\n",
"plt.subplot(322).set_title('increasing pattern')\n",
"plt.plot(time_h,vrec_1)\n",
"\n",
"plt.subplot(323)\n",
"plt.plot(time_h,vrec_2)\n",
"plt.legend(['Dend(0)'])\n",
"\n",
"plt.subplot(324)\n",
"plt.plot(time_h,vrec_3)\n",
"\n",
"plt.subplot(325)\n",
"plt.plot(time_h,vrec_4)\n",
"plt.legend(['Dend(1)'])\n",
"\n",
"plt.subplot(326)\n",
"plt.plot(time_h,vrec_5)\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"connai=[]\n",
"for i in range(N_sections):\n",
" connai.append(round(gci[i](0.5).nai,3))\n",
"\n",
"connad=[]\n",
"for i in range(N_sections):\n",
" connad.append(round(gcd[i](0.5).nai,3)) \n",
" \n",
"connacd=[]\n",
"for i in range(N_sections):\n",
" connacd.append(round(gccd[i](0.5).nai,3))\n",
" \n",
"connaci=[]\n",
"for i in range(N_sections):\n",
" connaci.append(round(gcci[i](0.5).nai,3))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.subplot(2,2,1).set_title('[Na] in decreasing density')\n",
"plt.plot(connad,'b')\n",
"\n",
"plt.subplot(2,2,2).set_title('gnabar in decreasing pattern')\n",
"plt.plot(gcdna,'b')\n",
"\n",
"plt.subplot(2,2,3).set_title('[Na] in increasing density')\n",
"plt.plot(connai,'r')\n",
"\n",
"plt.subplot(2,2,4).set_title('gnabar in increasing pattern')\n",
"plt.plot(gcina,'r')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'inareci' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-a5f5685522c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN_sections\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0minareci\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minareci\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0minarecd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minarecd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mInabari\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'inareci' is not defined"
]
}
],
"source": [
"for i in range(N_sections):\n",
" inareci[i]=np.asarray(inareci[i])\n",
" inarecd[i]=np.asarray(inarecd[i])\n",
"\n",
"Inabari=[]\n",
"Inabard=[]\n",
"for i in range(N_sections):\n",
" Inabarii=-1*round(np.mean(inareci[i]),3)\n",
" Inabari.append(Inabarii)\n",
" Inabardd=-1*round(np.mean(inarecd[i]),3)\n",
" Inabard.append(Inabardd)\n",
" \n",
"plt.subplot(211).set_title('Ina,Increasing, mA/cm2')\n",
"plt.plot(Inabari,'r')\n",
"\n",
"plt.subplot(212).set_title('Ina,Decreasing, mA/cm2')\n",
"plt.plot(Inabard,'b')\n",
"\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
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}