{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2ee1d062",
"metadata": {},
"outputs": [],
"source": [
"# This code is written by Nooshin Abdollahi\n",
"# Information about this code:\n",
"# - Motor axons are not included\n",
"# - there are not transverse connections between Boundary and Boundary"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "af4c646e",
"metadata": {},
"outputs": [],
"source": [
"# show the time of execution\n",
"from datetime import datetime\n",
"start_time = datetime.now()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "493e7e8a",
"metadata": {},
"outputs": [],
"source": [
"from neuron import h\n",
"import netpyne \n",
"from netpyne import specs, sim \n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from typing import Tuple, List\n",
"import math\n",
"import sys\n",
"\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d05a8722",
"metadata": {},
"outputs": [],
"source": [
"# Import nesseccery files from Matlab\n",
"\n",
"R = np.loadtxt(\"R.txt\") # All axons with different radius\n",
"G = np.loadtxt(\"G.txt\") # Axon's groups\n",
"C = np.loadtxt(\"C.txt\") # Coordinates of each axon (x,y)\n",
"neighboringAxon = np.loadtxt(\"neighboringAxon.txt\")\n",
"dist = np.loadtxt(\"dist.txt\") \n",
"dist_edge = np.loadtxt(\"Distance_edge.txt\") \n",
"AVE_area_around_axon = np.loadtxt(\"Ave_area_around_axon.txt\")\n",
"\n",
"unique_radius = np.loadtxt(\"unique_radius.txt\") # including different types\n",
"Number_of_nodes = np.loadtxt(\"Number_of_nodes.txt\") # Number of nodes for the specified axon total length\n",
"\n",
"parameters = np.loadtxt(\"parameters.txt\") \n",
"\n",
"# importing all the connections\n",
"import scipy.io as io\n",
"\n",
"for i in range(1,2):\n",
" for j in range(1,2):\n",
" if j>=i:\n",
" l = [i, j]\n",
" z = ''.join([str(n) for n in l])\n",
" Input = io.loadmat('Connect_types_{}.mat'.format(z) , squeeze_me=True) \n",
" I = Input['SAVE']; \n",
" locals()[\"Connect_types_\"+str(z)]=[]\n",
" for v in range(len(I)):\n",
" D = I[v].strip() \n",
" locals()[\"Connect_types_\"+str(z)].append(D) \n",
"\n",
"\n",
"# Boundary connections\n",
"for i in range(1,2):\n",
" Input = io.loadmat('Boundary_to_{}.mat'.format(i) , squeeze_me=True) \n",
" I = Input['SAVE']; \n",
" locals()[\"Boundary_to_\"+str(i)]=[]\n",
" for v in range(len(I)):\n",
" D = I[v].strip() \n",
" locals()[\"Boundary_to_\"+str(i)].append(D) \n",
" \n",
"\n",
"\n",
"#\n",
"Boundary_coordinates = np.loadtxt(\"Boundary_coordinates.txt\")\n",
"Boundary_neighboring = np.loadtxt(\"Boundary_neighboring.txt\")\n",
"Boundary_dist = np.loadtxt(\"Boundary_dist.txt\") \n",
"\n",
"\n",
"############## importing files related to transverse resistance (Rg) and Areas\n",
"\n",
"for i in range(1,2):\n",
" for j in range(1,2):\n",
" if j>=i:\n",
" l = [i, j]\n",
" z = ''.join([str(n) for n in l])\n",
" Input = np.loadtxt('Rg_{}.txt'.format(z) ) \n",
" locals()[\"Rg_\"+str(z)]=Input\n",
" \n",
"\n",
"\n",
" \n",
"for i in range(1,2):\n",
" Input = np.loadtxt('Boundary_Rg_{}.txt'.format(i) ) \n",
" locals()[\"Boundary_Rg_\"+str(i)]=Input\n",
"\n",
" \n",
" \n",
" \n",
" \n",
"for i in range(1,2):\n",
" for j in range(1,2):\n",
" if j>i:\n",
" l = [i, j]\n",
" z = ''.join([str(n) for n in l])\n",
" Input = np.loadtxt('Areas_{}.txt'.format(z) ) \n",
" locals()[\"Areas_\"+str(z)]=Input\n",
" \n",
" \n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cf1c9f69",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t1 \n",
"\t1 \n"
]
}
],
"source": [
"# Network parameters\n",
"netParams = specs.NetParams()\n",
"\n",
"netParams.sizeX=3000\n",
"netParams.sizeY=3000\n",
"netParams.sizeZ=3000\n",
"\n",
"\n",
"################################# Importing Axons(including C fibers and the others) and Boundary ####################################\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='Boundary', \n",
" conds={'cellType': 'Boundary', 'cellModel': 'Boundary'},\n",
" fileName='Boundarycable.hoc', \n",
" cellName='Boundary', \n",
" importSynMechs=True) ;\n",
"\n",
"\n",
"\n",
"\n",
"# Myelinated axons have different types (i.e. diameters)\n",
"# How many types... do I have? print(len(unique_radius)-1), -1 because the first eleman is for C fiber\n",
"# each type is a specific diameter\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type1', \n",
" conds={'cellType': 'type1', 'cellModel': 'type1'},\n",
" fileName='type1.hoc', \n",
" cellName='type1', \n",
" importSynMechs=True) ;\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d5ef8f97",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n"
]
}
],
"source": [
"###################################### Locating each axon in specific (x,y) #################################################\n",
"\n",
"\n",
"\n",
"netParams.popParams[\"Axon0\"] = {\n",
" 'cellType': 'type1', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type1', \n",
" 'xRange':[C[0][0], C[0][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[0][1], C[0][1]]} \n",
"\n",
"netParams.popParams[\"Axon1\"] = {\n",
" 'cellType': 'type1', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type1', \n",
" 'xRange':[C[1][0], C[1][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[C[1][1], C[1][1]]}\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"########################################### Locating Boundary Cables ########################################################\n",
"\n",
"\n",
"\n",
" \n",
"netParams.popParams[\"Boundary0\"] = {\n",
" 'cellType': 'Boundary', \n",
" 'numCells':1 , \n",
" 'cellModel': 'Boundary', \n",
" 'xRange':[Boundary_coordinates[0][0], Boundary_coordinates[0][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[Boundary_coordinates[0][1], Boundary_coordinates[0][1]]} \n",
"\n",
"\n",
" \n",
" \n",
"netParams.popParams[\"Boundary1\"] = {\n",
" 'cellType': 'Boundary', \n",
" 'numCells':1 , \n",
" 'cellModel': 'Boundary', \n",
" 'xRange':[Boundary_coordinates[1][0], Boundary_coordinates[1][0]], \n",
" 'yRange':[0, 0], \n",
" 'zRange':[Boundary_coordinates[1][1], Boundary_coordinates[1][1]]} \n",
"\n",
" \n",
" \n",
"\n",
"# in Total, how many Cells does Netpyne generate? Length(R)+len(Boundary_coordinates)\n",
"print(len(R)+len(Boundary_coordinates))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4adc83be",
"metadata": {},
"outputs": [],
"source": [
"################################################### Stimulation ############################################################\n",
"# Which group of axons do you want to stimulate?\n",
"# Group1: motor axons Group2: C fibers Group3: Adelta Group4: Abeta\n",
"\n",
"\n",
"#netParams.stimSourceParams['Input1'] = {'type': 'IClamp', 'del': 1, 'dur': 0.1, 'amp': 0.4}\n",
"netParams.stimSourceParams['Input1'] = {'type': 'VClamp', 'dur': [1, 0.02, 0], 'amp':[-80, 0, 0]}\n",
"\n",
" \n",
"netParams.stimTargetParams['Input1->Stim_1'] = {'source': 'Input1', 'sec':'node_0', 'loc': 0.5, 'conds': {'pop':\"Axon0\"}} \n",
"netParams.stimTargetParams['Input1->Stim_2'] = {'source': 'Input1', 'sec':'node_0', 'loc': 0.5, 'conds': {'pop':\"Axon1\"}} \n",
"\n",
"\n",
"\n",
"\n",
"XG1 = 1e-9 # 1e-9: disconnect from ground 1e9: Connect to ground\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "90a2f08b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Start time: 2022-10-31 21:31:01.123866\n",
"\n",
"Creating network of 4 cell populations on 1 hosts...\n",
" Number of cells on node 0: 4 \n",
" Done; cell creation time = 0.34 s.\n",
"Making connections...\n",
" Number of connections on node 0: 0 \n",
" Done; cell connection time = 0.00 s.\n",
"Adding stims...\n",
" Number of stims on node 0: 2 \n",
" Done; cell stims creation time = 0.00 s.\n",
"Recording 6 traces of 2 types on node 0\n"
]
}
],
"source": [
"simConfig = specs.SimConfig()\n",
"simConfig.hParams = {'celsius': 37 }\n",
"\n",
"simConfig.dt = 0.005 # Internal integration timestep to use default is 0.025\n",
"simConfig.duration = 6\n",
"simConfig.recordStim = True\n",
"simConfig.recordStep = 0.005 # Step size in ms to save data (e.g. V traces, LFP, etc) default is 0.1\n",
"#simConfig.cache_efficient = True\n",
"#simConfig.cvode_active = True\n",
"# simConfig.cvode_atol=0.0001\n",
"# simConfig.cvode_rtol=0.0001\n",
"\n",
"\n",
"simConfig.recordTraces = {'V_node_0' :{'sec':'node_0','loc':0.5,'var':'v'}}\n",
"simConfig.analysis['plotTraces'] = {'include': ['allCells']} # ['Axon0','Axon1']\n",
"\n",
"simConfig.analysis['plot2Dnet'] = True\n",
"simConfig.analysis['plot2Dnet'] = {'include': ['allCells'], 'view': 'xz'}\n",
"\n",
"\n",
"\n",
"#simConfig.recordLFP = [[56.39,-4000,51.74]] # Determine the location of the LFP electrode\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"sim.create(netParams, simConfig)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "9045099d",
"metadata": {},
"source": [
"### xraxial and transverese conductances"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "41af5705",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.1\n",
"63978.96633503253\n",
"0.1\n",
"63978.96633503253\n"
]
}
],
"source": [
"# Since by default Netpyne does not insert the parameters of the extracellular mechanism, I insert them in this section\n",
"# this section includes \"longitudinal\" resistivities (i.e. xraxial)\n",
"\n",
"#Total_Length=10000\n",
"\n",
"number_boundary = 4000 #Total_Length/Section_Length \n",
"number_boundary = int(number_boundary)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"rhoa=0.7e6 \n",
"mycm=0.1 \n",
"mygm=0.001 \n",
"\n",
"space_p1=0.002 \n",
"space_p2=0.004\n",
"space_i=0.004\n",
"\n",
"\n",
"\n",
"\n",
"############################# For Boundary Cables #################################################\n",
"\n",
"# soma section is just for LFP recording, LFP in Netpyne does not work if at least one section is not called soma \n",
"\n",
"\n",
"for j in range(len(R),len(R)+len(Boundary_coordinates)):\n",
" \n",
" S = sim.net.cells[j].secs[\"soma\"][\"hObj\"] \n",
" for seg in S:\n",
" seg.xraxial[0] = 1e9\n",
" seg.xraxial[1] = 1e9\n",
" seg.xg[0] = 1e9\n",
" seg.xg[1] = 1e9\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\n",
"\n",
"\n",
" for i in range(number_boundary): \n",
" S = sim.net.cells[j].secs[\"section_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = 1e9\n",
" seg.xraxial[1] = 1e9\n",
" seg.xg[0] = 1e9\n",
" seg.xg[1] = 1e9\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\n",
" \n",
" \n",
"\n",
" \n",
"############################## For myelinated sensory axons ##################################### \n",
"\n",
"\n",
"rho2 = 1211 * 1e-6 # Mohm-cm\n",
"\n",
"\n",
"\n",
"\n",
"for j in range(len(R)):\n",
" if G[j]!=2: # if it is not a C fiber \n",
" x = np.where(unique_radius == R[j]) \n",
" x = int(x[0])\n",
" nodes = Number_of_nodes\n",
" nodes=int(nodes)\n",
" \n",
" \n",
" nl = parameters[4]\n",
" nodeD = parameters[1]\n",
" paraD1 = nodeD\n",
" axonD = parameters[0]\n",
" paraD2 = axonD\n",
" \n",
" Rpn0 = (rhoa*.01)/((math.pi)*((((nodeD/2)+space_p1)**2)-((nodeD/2)**2)))\n",
" Rpn1 = (rhoa*.01)/((math.pi)*((((paraD1/2)+space_p1)**2)-((paraD1/2)**2)))\n",
" Rpn2 = (rhoa*.01)/((math.pi)*((((paraD2/2)+space_p2)**2)-((paraD2/2)**2)))\n",
" Rpx = (rhoa*.01)/((math.pi)*((((axonD/2)+space_i)**2)-((axonD/2)**2)))\n",
" \n",
" \n",
" ################### xraxial[1]\n",
" \n",
" radi = R[j]\n",
" \n",
" AVE = (AVE_area_around_axon[j]+0) /2\n",
" \n",
" xr = rho2 / ((math.pi)*(((radi+AVE)**2) - (radi**2)) * 1e-8) # Mohm/cm\n",
" \n",
" xr = xr /1\n",
" \n",
" print(AVE_area_around_axon[j]+0)\n",
" print(xr)\n",
" \n",
" ##################\n",
" \n",
" \n",
" \n",
"\n",
" S = sim.net.cells[j].secs[\"soma\"][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn1\n",
" seg.xraxial[1] = xr \n",
" seg.xg[0] = mygm/(nl*2)\n",
" seg.xg[1] = XG1 # disconnect from ground\n",
" seg.xc[0] = mycm/(nl*2)\n",
" seg.xc[1] = 0\n",
"\n",
" \n",
" for i in range(nodes):\n",
" S = sim.net.cells[j].secs[\"node_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn0\n",
" seg.xraxial[1] = xr\n",
" seg.xg[0] = 1e6\n",
" seg.xg[1] = XG1\n",
" seg.xc[0] = 0\n",
" seg.xc[1] = 0\n",
"\n",
"\n",
" for i in range(2*nodes):\n",
" S = sim.net.cells[j].secs[\"MYSA_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn1\n",
" seg.xraxial[1] = xr\n",
" seg.xg[0] = mygm/(nl*2)\n",
" seg.xg[1] = XG1\n",
" seg.xc[0] = mycm/(nl*2)\n",
" seg.xc[1] = 0\n",
"\n",
"\n",
" for i in range(10*nodes):\n",
" S = sim.net.cells[j].secs[\"FLUT_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpn2\n",
" seg.xraxial[1] = xr\n",
" seg.xg[0] = mygm/(nl*2)\n",
" seg.xg[1] = XG1\n",
" seg.xc[0] = mycm/(nl*2)\n",
" seg.xc[1] = 0 \n",
"\n",
"\n",
" for i in range(40*nodes):\n",
" S = sim.net.cells[j].secs[\"STIN_%s\" %i][\"hObj\"]\n",
" for seg in S:\n",
" seg.xraxial[0] = Rpx\n",
" seg.xraxial[1] = xr\n",
" seg.xg[0] = mygm/(nl*2)\n",
" seg.xg[1] = XG1\n",
" seg.xc[0] = mycm/(nl*2)\n",
" seg.xc[1] = 0\n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "afaf323f",
"metadata": {},
"outputs": [],
"source": [
"\n",
"##############################This section is about transverse connections between axons #####################################\n",
"# *** If you do not want to include ephaptic interaction, do not run this section\n",
"# To model ephaptic effect, \"LinearMechanism\" in NEURON is used.\n",
"\n",
"\n",
"\n",
"rho = 1211 * 10000 # ohm-micron\n",
"\n",
"count = 0\n",
"\n",
"for i in range(len(R)): \n",
"\n",
" \n",
" for j in range(len(R)): \n",
" \n",
" if neighboringAxon[i][j]==1:\n",
" \n",
"\n",
" a1 = np.where(unique_radius == R[i]) # find type of R[i]\n",
" a1 = a1[0][0]+1\n",
" a2 = np.where(unique_radius == R[j]) # find type of R[j]\n",
" a2 = a2[0][0]+1\n",
"\n",
"\n",
" NSEG = 0\n",
"\n",
"\n",
"\n",
" if a1==a2:\n",
" SEC = locals()[\"Connect_types_\"+str(a1)+str(a1)]\n",
" RG = locals()[\"Rg_\"+str(a1)+str(a1)]\n",
" area = (math.pi)*(parameters[1])*(np.ones((len(RG),1))) # micron^2\n",
" area = area * 1e-8 #cm^2\n",
" b1=i\n",
" b2=j\n",
" if a1==0:\n",
" area = (math.pi)*0.8*10*(np.ones((len(RG),1))) # micron^2\n",
" area = area * 1e-8 #cm^2\n",
" \n",
" \n",
"\n",
" if a1<a2:\n",
" SEC = locals()[\"Connect_types_\"+str(a1)+str(a2)]\n",
" RG = locals()[\"Rg_\"+str(a1)+str(a2)]\n",
" b1=i\n",
" b2=j\n",
" if a1==0:\n",
" area = (math.pi)*(parameters[a2][1])*(np.ones((len(RG),1)))\n",
" area = area * 1e-8 #cm^2\n",
" b1=j\n",
" b2=i\n",
" \n",
" else:\n",
" area = locals()[\"Areas_\"+str(a1)+str(a2)]\n",
" area = area[ : , np.newaxis]\n",
" area = area * 1e-8\n",
" \n",
" \n",
"\n",
" if a1>a2:\n",
" SEC = locals()[\"Connect_types_\"+str(a2)+str(a1)]\n",
" RG = locals()[\"Rg_\"+str(a2)+str(a1)]\n",
" b1=j\n",
" b2=i\n",
" if a2==0:\n",
" area = (math.pi)*(parameters[a1][1])*(np.ones((len(RG),1)))\n",
" area = area * 1e-8 #cm^2\n",
" b1=i\n",
" b2=j\n",
" \n",
" else:\n",
" area = locals()[\"Areas_\"+str(a2)+str(a1)]\n",
" area = area[ : , np.newaxis]\n",
" area = area * 1e-8\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
" locals()[\"sl\"+str(count)] = h.SectionList()\n",
"\n",
" for z1 in range(int(len(SEC)/2)): \n",
"\n",
" S = sim.net.cells[b1].secs[SEC[z1]][\"hObj\"]\n",
" NSEG=NSEG+S.nseg\n",
" locals()[\"sl\"+str(count)].append(S)\n",
"\n",
" for z2 in range(int(len(SEC)/2),int(len(SEC))):\n",
"\n",
" S = sim.net.cells[b2].secs[SEC[z2]][\"hObj\"]\n",
" locals()[\"sl\"+str(count)].append(S) \n",
" \n",
" \n",
"\n",
" nsegs=int(NSEG)\n",
"\n",
" locals()[\"gmat\"+str(count)] =h.Matrix(2*nsegs, 2*nsegs)\n",
" locals()[\"cmat\"+str(count)] =h.Matrix(2*nsegs, 2*nsegs)\n",
" locals()[\"bvec\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"xl\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"layer\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"layer\"+str(count)].fill(2) # connect layer 2\n",
" locals()[\"e\"+str(count)] = h.Vector(2*nsegs)\n",
"\n",
" for z3 in range(2*nsegs):\n",
" locals()[\"xl\"+str(count)][z3] = 0.5\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" d = dist_edge[i][j] + 0 #dist[i][j]\n",
" rd = rho*d\n",
" s = ((unique_radius*2)+(unique_radius*2))/2\n",
" locals()[\"RG\"+str(count)] = np.array(RG)*s\n",
" locals()[\"Resistance\"+str(count)] = rd/locals()[\"RG\"+str(count)]\n",
" locals()[\"Conductance\"+str(count)]=[]\n",
" for z4 in range(len(locals()[\"Resistance\"+str(count)])):\n",
" locals()[\"Conductance\"+str(count)].append(1/(locals()[\"Resistance\"+str(count)][z4]*area[z4]))\n",
" \n",
"\n",
" \n",
" for z5 in range(0,nsegs,1):\n",
"\n",
" locals()[\"gmat\"+str(count)].setval(z5, z5, locals()[\"Conductance\"+str(count)][z5][0] )\n",
" locals()[\"gmat\"+str(count)].setval(z5, nsegs+z5, -locals()[\"Conductance\"+str(count)][z5][0])\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, z5, -locals()[\"Conductance\"+str(count)][z5][0])\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, nsegs+z5, locals()[\"Conductance\"+str(count)][z5][0])\n",
" \n",
" \n",
" locals()[\"GMAT\"+str(i)+str(j)] = locals()[\"gmat\"+str(count)]\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# geA= 1000\n",
" \n",
"# for z5 in range(0,nsegs,1):\n",
"# locals()[\"gmat\"+str(count)].setval(z5, z5, geA)\n",
"# locals()[\"gmat\"+str(count)].setval(z5, nsegs+z5, -geA)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z5, z5, -geA)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z5, nsegs+z5, geA)\n",
"\n",
"\n",
"\n",
"\n",
" locals()[\"lm\"+str(count)] = h.LinearMechanism(locals()[\"cmat\"+str(count)], locals()[\"gmat\"+str(count)], locals()[\"e\"+str(count)], locals()[\"bvec\"+str(count)], locals()[\"sl\"+str(count)], locals()[\"xl\"+str(count)], locals()[\"layer\"+str(count)])\n",
"\n",
" count=count+1\n",
" \n",
" SEC.clear\n",
" del RG\n",
" del area\n",
" \n",
" \n",
"\n",
" \n",
"#print(count) \n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b71ff07f",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT01.printf() "
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9f7204b0",
"metadata": {},
"outputs": [],
"source": [
" \n",
" \n",
" \n",
"############################### Transverse connections between Boundary cables and Axons ######################################\n",
"\n",
"\n",
"rho = 1.136e5 * 10000 * 4.7e-4 * 10000 # ohm-micron^2\n",
"\n",
"\n",
"\n",
"rows = len(Boundary_neighboring)\n",
"\n",
"for i in range(rows):\n",
" \n",
" for j in range(len(R)):\n",
" \n",
" if Boundary_neighboring[i][j]==1:\n",
" \n",
" NSEG = 0\n",
"\n",
" a2 = np.where(unique_radius == R[j]) # find type \n",
" a2 = a2[0][0]+1\n",
" \n",
" Boundary_RG = locals()[\"Boundary_Rg_\"+str(a2)]\n",
" area = (math.pi)*(parameters[1])*(np.ones((len(Boundary_RG),1)))\n",
" area = area * 1e-8 #cm^2\n",
" \n",
"\n",
" SEC = locals()[\"Boundary_to_\"+str(a2)]\n",
"\n",
"\n",
" locals()[\"sl\"+str(count)] = h.SectionList()\n",
"\n",
" for z1 in range(int(len(SEC)/2)): \n",
"\n",
" S = sim.net.cells[j].secs[SEC[z1]][\"hObj\"]\n",
" NSEG=NSEG+S.nseg\n",
" locals()[\"sl\"+str(count)].append(S)\n",
"\n",
" for z2 in range(int(len(SEC)/2),int(len(SEC))):\n",
"\n",
" S = sim.net.cells[len(R)+i].secs[SEC[z2]][\"hObj\"]\n",
" locals()[\"sl\"+str(count)].append(S) \n",
"\n",
"\n",
"\n",
"\n",
" nsegs=int(NSEG)\n",
"\n",
" locals()[\"gmat\"+str(count)] =h.Matrix(2*nsegs, 2*nsegs)\n",
" locals()[\"cmat\"+str(count)] =h.Matrix(2*nsegs, 2*nsegs)\n",
" locals()[\"bvec\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"xl\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"layer\"+str(count)] =h.Vector(2*nsegs)\n",
" locals()[\"layer\"+str(count)].fill(2) # connect layer 2\n",
" locals()[\"e\"+str(count)] = h.Vector(2*nsegs)\n",
"\n",
" for z3 in range(2*nsegs):\n",
" locals()[\"xl\"+str(count)][z3] = 0.5\n",
"\n",
"\n",
" \n",
" \n",
" rd = rho\n",
" s = (unique_radius*2)\n",
" locals()[\"Boundary_RG\"+str(count)] = np.array(Boundary_RG)*s\n",
" locals()[\"Resistance\"+str(count)] = rd/locals()[\"Boundary_RG\"+str(count)]\n",
" locals()[\"Conductance\"+str(count)]=[]\n",
" for z4 in range(len(locals()[\"Resistance\"+str(count)])):\n",
" locals()[\"Conductance\"+str(count)].append(1/(locals()[\"Resistance\"+str(count)][z4]*area[z4]))\n",
"\n",
" \n",
" for z5 in range(0,nsegs,1):\n",
"\n",
" locals()[\"gmat\"+str(count)].setval(z5, z5, locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" locals()[\"gmat\"+str(count)].setval(z5, nsegs+z5, - locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, z5, - locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" locals()[\"gmat\"+str(count)].setval(nsegs+z5, nsegs+z5, locals()[\"Conductance\"+str(count)][z5][0] * 1)\n",
" \n",
" \n",
" \n",
" locals()[\"GMAT_BOUNDARY\"+str(i)+str(j)] = locals()[\"gmat\"+str(count)]\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"\n",
"\n",
" \n",
"# geB= 1\n",
" \n",
"# for z6 in range(0,nsegs,1):\n",
"\n",
"# locals()[\"gmat\"+str(count)].setval(z6, z6, geB)\n",
"# locals()[\"gmat\"+str(count)].setval(z6, nsegs+z6, -geB)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z6, z6, -geB)\n",
"# locals()[\"gmat\"+str(count)].setval(nsegs+z6, nsegs+z6, geB)\n",
"\n",
"\n",
"\n",
"\n",
" locals()[\"lm\"+str(count)] = h.LinearMechanism(locals()[\"cmat\"+str(count)], locals()[\"gmat\"+str(count)], locals()[\"e\"+str(count)], locals()[\"bvec\"+str(count)], locals()[\"sl\"+str(count)], locals()[\"xl\"+str(count)], locals()[\"layer\"+str(count)])\n",
"\n",
" count=count+1\n",
" \n",
" \n",
" SEC.clear\n",
" del Boundary_RG\n",
" del area\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"\n",
"#print(count) \n",
" \n",
" \n",
" \n",
"# from IPython.display import clear_output\n",
"\n",
"# clear_output(wait=True)\n",
"\n",
"\n",
" \n",
"#gmat0.printf() \n",
"\n",
"# for sec in sl0:\n",
"# print(sec)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "2f2f0781",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(Boundary_neighboring)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7808a6c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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},
{
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},
{
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT_BOUNDARY11.printf() "
]
},
{
"cell_type": "markdown",
"id": "b2a6c256",
"metadata": {},
"source": [
"#### Recordings"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "d1494f97",
"metadata": {},
"outputs": [],
"source": [
"## Recording vext\n",
"\n",
"\n",
"# v1 = sim.net.cells[45].secs[\"node_0\"][\"hObj\"]\n",
"# ap1 = h.Vector()\n",
"# t = h.Vector()\n",
"# ap1.record(v1(0.5)._ref_v)\n",
"\n",
"# t.record(h._ref_t)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "5360fb79",
"metadata": {},
"outputs": [],
"source": [
"for i1 in range(36):\n",
"\n",
" locals()[\"Abeta0_imembrane\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
" locals()[\"Abeta0_imembrane_node\"+str(i1)] = h.Vector()\n",
" locals()[\"Abeta0_imembrane_node\"+str(i1)].record(locals()[\"Abeta0_imembrane\"+str(i1)](0.5)._ref_i_membrane)\n",
" \n",
" \n",
" \n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_icap\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_icap_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_icap_node\"+str(i1)].record(locals()[\"Abeta0_icap\"+str(i1)](0.5)._ref_i_cap) \n",
" \n",
"\n",
" \n",
" \n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_ik\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_ik_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_ik_node\"+str(i1)].record(locals()[\"Abeta0_ik\"+str(i1)](0.5)._ref_ik_axnode) \n",
" \n",
" \n",
" \n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_il\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_il_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_il_node\"+str(i1)].record(locals()[\"Abeta0_il\"+str(i1)](0.5)._ref_il_axnode) \n",
" \n",
" \n",
"\n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_ina\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_ina_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_ina_node\"+str(i1)].record(locals()[\"Abeta0_ina\"+str(i1)](0.5)._ref_ina_axnode) \n",
" \n",
" \n",
" \n",
" \n",
"# for i1 in range(12):\n",
"\n",
"# locals()[\"Abeta0_inap\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
"# locals()[\"Abeta0_inap_node\"+str(i1)] = h.Vector()\n",
"# locals()[\"Abeta0_inap_node\"+str(i1)].record(locals()[\"Abeta0_inap\"+str(i1)](0.5)._ref_inap_axnode) \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ca5603a0",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Vector[89]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Recording v and vext[0], Abeta\n",
"\n",
"###################################################### Abeta0\n",
"\n",
"\n",
"for i1 in range(36):\n",
"\n",
" locals()[\"Abeta0_v\"+str(i1)] = sim.net.cells[0].secs[\"node_%s\"%i1][\"hObj\"]\n",
" locals()[\"Abeta0_v_node\"+str(i1)] = h.Vector()\n",
" locals()[\"Abeta0_v_node\"+str(i1)].record(locals()[\"Abeta0_v\"+str(i1)](0.5)._ref_v)\n",
"\n",
"\n",
"\n",
"# for i2 in range(12):\n",
"\n",
"# locals()[\"Abeta0_vex\"+str(i2)] = sim.net.cells[0].secs[\"node_%s\"%i2][\"hObj\"]\n",
"# locals()[\"Abeta0_vext1_node\"+str(i2)] = h.Vector()\n",
"# locals()[\"Abeta0_vext1_node\"+str(i2)].record(locals()[\"Abeta0_vex\"+str(i2)](0.5)._ref_vext[1])\n",
"\n",
" \n",
" \n",
"# for i3 in range(0,24,2):\n",
" \n",
"# locals()[\"Abeta_vMext\"+str(i3)] = sim.net.cells[0].secs[\"MYSA_%s\"%i3][\"hObj\"]\n",
"# locals()[\"Abeta0_vext1_MYSA\"+str(i3)] = h.Vector()\n",
"# locals()[\"Abeta0_vext1_MYSA\"+str(i3)].record(locals()[\"Abeta_vMext\"+str(i3)](0.5)._ref_vext[1])\n",
"\n",
"\n",
" \n",
"# for i4 in range(12):\n",
"\n",
"# locals()[\"Abeta1_vext1\"+str(i4)] = sim.net.cells[1].secs[\"node_%s\"%i4][\"hObj\"]\n",
"# locals()[\"Abeta1_vext1_node\"+str(i4)] = h.Vector()\n",
"# locals()[\"Abeta1_vext1_node\"+str(i4)].record(locals()[\"Abeta1_vext1\"+str(i4)](0.5)._ref_vext[1]) \n",
" \n",
" \n",
" \n",
"# locals()[\"Abeta_vSext\"+str(220)] = sim.net.cells[0].secs[\"STIN_220\"][\"hObj\"]\n",
"# locals()[\"Abeta0_vext1_STIN\"+str(220)] = h.Vector()\n",
"# locals()[\"Abeta0_vext1_STIN\"+str(220)].record(locals()[\"Abeta_vSext\"+str(220)](0.5)._ref_vext[1]) \n",
" \n",
"# locals()[\"Abeta_v\"+str(220)] = sim.net.cells[0].secs[\"STIN_220\"][\"hObj\"]\n",
"# locals()[\"Abeta0_v_STIN\"+str(220)] = h.Vector()\n",
"# locals()[\"Abeta0_v_STIN\"+str(220)].record(locals()[\"Abeta_v\"+str(220)](0.5)._ref_v) \n",
" \n",
" \n",
" \n",
"t = h.Vector()\n",
"t.record(h._ref_t)"
]
},
{
"cell_type": "markdown",
"id": "d83f15db",
"metadata": {},
"source": [
"#### Simulate and Analyze"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "cd6d9f09",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Running simulation for 6.0 ms...\n",
" Done; run time = 88.45 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 0.53 s.\n",
"\n",
"Analyzing...\n",
" Cells: 4\n",
" Connections: 0 (0.00 per cell)\n",
" Spikes: 2 (83.33 Hz)\n",
" Simulated time: 0.0 s; 1 workers\n",
" Run time: 88.45 s\n",
" Done; saving time = 0.00 s.\n",
"Plotting recorded cell traces ... cell\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<Figure size 720x576 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting 2D representation of network cell locations and connections...\n"
]
},
{
"data": {
"image/png": 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POeWU0YMGDZo4YsSICZ/97GeHd3Z2RkTEX/3VX4344Ac/OGbLc5977rn6lNLhW2LkiCOOOOjSSy8dMWXKlPGNjY2TjzzyyHHLli2r27L9DTfcMHjEiBETBg4cOGnWrFnDtp7j0Ucf7Ttp0qTx/fr1mzRkyJDDzjnnnH3b2trSlsdTSodfc801Q/bbb79DR48ePeHss8/e9+Mf//h7tt7HscceO/aqq67aZ82aNTX/+q//Ouiaa655ecCAAV3Tp09fe9xxx71xyy237PVOfo/qdrwJAAAQEXHHy4+OWtb+et+eXGN478GtZ42ctvgPfV5LS0vN3XffPWjy5MlrIyLOP//8UWvWrKldtGjR/FdffbVu+vTpBw4fPnzjZZdd9lp39nffffcNfuihhxbuv//+G4499tgDv/zlLw/91re+9fJTTz3V5zOf+cx+995778KpU6euu+SSS0YuX768fsvz6urq4m//9m8XH3PMMesWLVpUf+KJJ4679tprh3zhC194dcs2Dz744MCf/exnv2hsbOx68sknGz760Y+Ovemmm5bU1tbGsmXL6p544ol+t91224vz58/vXVtbG4cddlj7lucedthhrY8//vg7Or/IkR8AANiNnXHGGWP79es3adCgQZMff/zx/ldcccXyjo6OeOihhwZfe+21Lw8aNKjroIMO2vDJT37yldmzZ3f7iMnpp5++8rDDDmtvamrKH/rQh16fP39+34iI2bNnDzr22GPfOPHEE9c2NDTk6667bmlKKW953tFHH9163HHHrevVq1ccdNBBG84999wVjz322O/EyuWXX/7K0KFDO5uamvK0adNam5qaOh944IH+ERG33nrroCOOOKJl1KhRHS0tLbVNTU2dWz93wIABnWvXrq19J79XjvwAAEA3vZMjMj3tu9/97gsnn3xyS0dHR9x5550Djz/++IOefPLJn2/cuDGNGzduw5btxowZs2H58uXdvhjCsGHD3jwhp2/fvl2tra01ERFLly7tNXLkyDf3279//66BAwd2bLn9zDPP9L700ktHzZ8/v7Gtra2ms7MzDj744Nat9z1mzJgNW98+7bTTVt5+++2DTznllDV33333XhdffPGrERH9+vXrXLdu3e8csFmzZs3vBVF3OfIDAAB7gLq6upg5c+bqmpqa/JOf/KSxrq4uL1y48M23o7344ov1Q4cO3RgR0djY2Ll+/fo3W2DJkiXdjqLhw4dvfPnll9/cb0tLS83q1avfPKhy4YUX7jdu3Li2hQsXzl+7du3TV1xxxctv3UdK6Xdun3feeSt/9KMfDXziiScaFi1a1OfMM89cFRExYcKE9o6OjjR//vzeW7Z95plnGsaPH7++u/NuTfwAAMAeoKurK+64446BLS0tdZMnT15/0kknrbr88stHrlq1qub555+vv+GGG4aedtppKyMipkyZsv7JJ59sWrhwYf3KlStrv/rVrw7b0f63OP3001c98sgjAx5++OGmtra29KlPfWpEzvnNmlm7dm1t//79OwcMGND19NNP97nlllv22dE+DzjggI0TJkxYN3PmzDEnnHDC6qamphyx6ajS9OnTV3/uc58bsWbNmpof/vCHjT/+8Y8HfuxjH1v5Tn6PxA8AAOzGTjvttHF9+/ad3K9fv8lf+tKXRl5//fW/bm5ubvv7v//73/Tt27dr//33n3DMMceMnzFjxuuXXnrpaxERp5xyypo/+7M/WzVlypSDJ0+e/N6TTjrpje6u19zc3Pb1r3/9N+eee+6YYcOGTRw0aFDH0KFD33wb27XXXrv43nvvHdzU1DT5/PPP3+/kk09+vTv7Peuss1YuXLiw4ZxzzvmdsPnOd77z0vr162uGDh06cebMmfv/zd/8zW+am5vbujvv1lLOecdbVUlzc3OeM2dOtccAAGAPllJ6KufcvK3H5s2b9+LEiRO7dXU03p0f/OAHTR/72MfGLFmyZH5t7Tu6nkFERMybN2/viRMnjt7WY478AAAAVdXe3p6+8Y1vDD3zzDNfezfhsyPiBwAAqJq5c+f2GThw4KRXX3211+c+97nlPbmWS10DAABVM2XKlLb169c/XYm1HPkBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgHdl5MiRE+6///5+1Z5jR8QPAADspkaOHDmhT58+U/r27Tu5f//+k6ZOnTr2hRde6FXtuXamr371q0MOPfTQ99bX10+ZMWPG6HezL/EDAAC7sbvuumtha2vr06+88sq8IUOGdFx00UX7Vnum7tq4ceMOtxk5cuTGWbNmLfvIRz7y2rtdT/wAAMAeoG/fvvnDH/7wqhdeeKEhImLlypW1p5xyyuhBgwZNHDFixITPfvazwzs7OyMi4q/+6q9GfPCDHxyz5bnPPfdcfUrp8C0xcsQRRxx06aWXjpgyZcr4xsbGyUceeeS4ZcuW1W3Z/oYbbhg8YsSICQMHDpw0a9asYVvP8eijj/adNGnS+H79+k0aMmTIYeecc86+bW1tacvjKaXDr7nmmiH77bffoaNHj55w9tln7/vxj3/8PVvv49hjjx171VVX7RMRMXPmzNVnn3326r322qvj3f4e1e14EwAAICJi9Q0fG9Xxm2f79uQadfse2jrwk7cs/kOf19LSUnP33XcPmjx58tqIiPPPP3/UmjVrahctWjT/1VdfrZs+ffqBw4cP33jZZZd16wjKfffdN/ihhx5auP/++2849thjD/zyl7889Fvf+tbLTz31VJ/PfOYz+917770Lp06duu6SSy4ZuXz58vo356+ri7/9279dfMwxx6xbtGhR/Yknnjju2muvHfKFL3zh1S3bPPjggwN/9rOf/aKxsbHrySefbPjoRz869qabblpSW1sby5Ytq3viiSf63XbbbS/+ob8HO+LIDwAA7MbOOOOMsf369Zs0aNCgyY8//nj/K664YnlHR0c89NBDg6+99tqXBw0a1HXQQQdt+OQnP/nK7Nmz9+rufk8//fSVhx12WHtTU1P+0Ic+9Pr8+fP7RkTMnj170LHHHvvGiSeeuLahoSFfd911S1NKecvzjj766NbjjjtuXa9eveKggw7acO6556547LHHfudiCJdffvkrQ4cO7WxqasrTpk1rbWpq6nzggQf6R0Tceuutg4444oiWUaNGvesjPW/lyA8AAHTTOzki09O++93vvnDyySe3dHR0xJ133jnw+OOPP+jJJ5/8+caNG9O4ceM2bNluzJgxG5YvX97tiyEMGzbszRNy+vbt29Xa2loTEbF06dJeI0eOfHO//fv37xo4cOCbofLMM8/0vvTSS0fNnz+/sa2traazszMOPvjg1q33PWbMmA1b3z7ttNNW3n777YNPOeWUNXffffdeF1988avRAxz5AQCAPUBdXV3MnDlzdU1NTf7JT37SWFdXlxcuXPjm29FefPHF+qFDh26MiGhsbOxcv379my2wZMmSbkfR8OHDN7788stv7relpaVm9erVbx5UufDCC/cbN25c28KFC+evXbv26SuuuOLlt+4jpfQ7t88777yVP/rRjwY+8cQTDYsWLepz5plnrur2N/4HED8AALAH6OrqijvuuGNgS0tL3eTJk9efdNJJqy6//PKRq1atqnn++efrb7jhhqGnnXbayoiIKVOmrH/yySebFi5cWL9y5crar371q8N2tP8tTj/99FWPPPLIgIcffripra0tfepTnxqRc36zZtauXVvbv3//zgEDBnQ9/fTTfW655ZZ9drTPAw44YOOECRPWzZw5c8wJJ5ywuqmp6c230W3cuDFaW1tTZ2dn6uzsTK2trak7V4nbFvEDAAC7sdNOO21c3759J/fr12/yl770pZHXX3/9r5ubm9v+/u///jd9+/bt2n///Sccc8wx42fMmPH6pZde+lpExCmnnLLmz/7sz1ZNmTLl4MmTJ7/3pJNOeqO76zU3N7d9/etf/8255547ZtiwYRMHDRrUMXTo0Dffxnbttdcuvvfeewc3NTVNPv/88/c7+eSTX+/Ofs8666yVCxcubDjnnHNWbn3/rFmzRjQ2Nk751re+Neyf//mfBzc2Nk6ZNWvWiO7Ou7WUc97xVlXS3Nyc58yZU+0xAADYg6WUnso5N2/rsXnz5r04ceLEd/35MuzYD37wg6aPfexjY5YsWTK/trb2He9n3rx5e0+cOHH0th5z5AcAAKiq9vb29I1vfGPomWee+dq7CZ8dET8AAEDVzJ07t8/AgQMnvfrqq70+97nPLe/JtVzqGgAAqJopU6a0rV+//ulKrOXIDwAAUATxAwAA29fZ1dWVdrwZu4LNr1XX9h4XPwAAsH2Pv/TSSwPb29t77cpXSWZT+KxYsWJARDy7vW2c8wMAANvR0dHx8dWrV3+ipaXl3Jzz4HDwYFfWFRHPdnR0nL+9DcQPAABsx+GHH74hIr65+YvdnHIFAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCJULH5SSn+eUpqTUmpPKf1DpdYFAACIiKir4FpLI+IrETE9IhoquC4AAEDl4ifnfF9EREqpOSLeU6l1AQAAInbBc35SShdsfnvcnBUrVlR7HAAAYA+xy8VPzvnmnHNzzrl5yJAh1R4HAADYQ+xy8QMAANATxA8AAFCEil3wIKVUt3m92oioTSn1iYiOnHNHpWYAAADKVckjP38dEesj4vKIOGvzr/+6gusDAAAFq+Slrr8YEV+s1HoAAABbc84PAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUoSLxk1LqnVL6TkrppZRSS0rp6ZTSiZVYGwAAIKJyR37qImJxRLw/IgZExOcj4p6U0ugKrQ8AABSurhKL5JzXRcQXt7rr+ymlX0fE4RHxYiVmAAAAylaVc35SSkMj4sCIWFCN9QEAgPJUPH5SSr0i4s6IuC3n/MttPH5BSmlOSmnOihUrKj0eAACwh6po/KSUaiLi9ojYEBF/vq1tcs4355ybc87NQ4YMqeR4AADAHqwi5/xERKSUUkR8JyKGRsRJOeeNlVobAACgYvETETdGxHsj4k9zzusruC4AAEDFPudnv4i4MCImRcQrKaW1m7/OrMT6AAAAlbrU9UsRkSqxFgAAwLZU5VLXAAAAlSZ+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCBWLn5TSHSmlZSmlNSml51NK51dqbQAAgEoe+bkmIkbnnPtHxAci4isppcMruD4AAFCwisVPznlBzrl9y83NXwdUan0AAKBsFT3nJ6X0rZRSa0T8MiKWRcS/bGObC1JKc1JKc1asWFHJ8QAAgD1YReMn53xxRPSLiKMj4r6IaN/GNjfnnJtzzs1Dhgyp5HgAAMAerOJXe8s5d+acH4+I90TEJyq9PgAAUKZqXuq6LpzzAwAAVEhF4ieltE9K6bSUUlNKqTalND0iTo+IRyqxPgAAQF2F1smx6S1uN8Wm4HopIv4y5/zPFVofAAAoXEXiJ+e8IiLeX4m1AAAAtqWa5/wAAABUjPgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCLU7WiDlNKUiPh/ImJiRAyMiNURMS8ifpBzntOTwwEAAOws242flNJ/jYivRkS/iPhJRPx7RLRsvv3eiLgzpbQ2Ij6Xc364ArMCAAC8Y2935OfCiPhEzvnJ7W2QUnpfRMyKCPEDAADs0rYbPznnGTt68uYw+vBOnQgAAKAH7PCcn62llPpHRNPW9+Wcl+7UiQAAAHpAt+InpfSnEXFzROwXEWmrh3JE1PbAXAAAADtVdy91/Z3YdPGDARHRa6uv+h6aCwAAYKfq7tve+kTErTnnzp4cBgAAoKd098jPf4+Iz6aU0g63BAAA2AV198jPvbHpctZXpJRe2/qBnPP+O30qAACAnay78fM/I+KxiPiniFjfc+MAAAD0jO7Gz5iImJxz7urJYQAAAHpKd8/5+eeIOLYnBwEAAOhJ3T3y0zsiHkgpPRYRy7d+IOd8zk6fCgAAYCfrbvws2PwFAACwW+pW/OScv9TTgwAAAPSkbsVPSmm75/vknB/ZeeMAAAD0jO6+7e07b7k9JCLqI2JJRPicHwAAYJfX3be9jdn6dkqpNiL+OiJaemIoAACAna27l7r+HTnnzoi4OiI+u3PHAQAA6BnvKH42Oz4ifOgpAACwW+juBQ8WR0Te6q6+EdEnIi7uiaEAAAB2tu5e8OCst9xeFxHP55zX7OR5AAAAekR3L3jwk54eBAAAoCdt95yflNJ1KaVhb/fklNKwlNJ1O38sAACAnevtjvw8FxE/Syn9IiJ+svl2S0T0i4gDI2JqRBwUEV/p4RkBAADete3GT875f6SUbomID0bEiRFxckQMjIhVEfFMRNwUEQ/mnDt6fkwAAIB3523P+ck5b4yI/7n5CwAAYLf1bj7nBwAAYLchfgAAgCKIHwAAoAjiBwAAKEK34iel9EhK6fxt3P/Qzh8JAABg53vbq71t5b9ExNCU0qSIuDTn3Ln5/qN7ZCoAAICdrLtve9sQEX8cEaMj4scppcGb7089MRQAAMDO1u1zfnLOLRHx3yLi/0TEnJTSYRGRe2owAACAnam7b3tLERE55xwRV6SU5kXEjyOiT08NBgAAsDN1N37O2/pGzvmulNJzEfGBnT8SAADAztet+Mk537ON+56OiKd3+kQAAAA9wOf8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFqHj8pJTGpZTaUkp3VHptAACgXNU48nNDRDxZhXUBAICCVTR+UkqnRcTqiPhflVwXAACgYvGTUuofEVdFxKd2sN0FKaU5KaU5K1asqMxwAADAHq+SR36+HBHfyTkvfruNcs4355ybc87NQ4YMqdBoAADAnq6uEouklCZFxJ9GxORKrAcAAPBWFYmfiJgaEaMj4jcppYiIpoioTSkdnHOeUqEZAACAglUqfm6OiLu2uv3p2BRDn6jQ+gAAQOEqEj8559aIaN1yO6W0NiLacs6uaAAAAFREpY78/I6c8xersS4AAFCuanzIKQAAQMWJHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAgVi5+U0r+llNpSSms3fz1XqbUBAAAqfeTnz3POTZu/Dqrw2gAAQMG87Q0AAChCpePnmpTSaymlf08pTa3w2gAAQMEqGT+zImL/iBgZETdHxIMppQPeulFK6YKU0pyU0pwVK1ZUcDwAAGBPVrH4yTn/R865JefcnnO+LSL+PSJO2sZ2N+ecm3POzUOGDKnUeAAAwB6umuf85IhIVVwfAAAoSEXiJ6U0MKU0PaXUJ6VUl1I6MyKOiYiHK7E+AABAXYXW6RURX4mI8RHRGRG/jIiTc84+6wcAAKiIisRPznlFRLyvEmsBAABsi8/5AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCBWNn5TSaSmlX6SU1qWUfpVSOrqS6wMAAOWqq9RCKaXjI+LrEfHRiPhZRAyv1NoAAAAVi5+I+FJEXJVz/j+bb79cwbUBAIDCVeRtbyml2ohojoghKaUXUkpLUkp/l1Jq2Ma2F6SU5qSU5qxYsaIS4wEAAAWo1Dk/QyOiV0R8OCKOjohJETE5Iv76rRvmnG/OOTfnnJuHDBlSofEAAIA9XaXiZ/3mf16fc16Wc34tIq6LiJMqtD4AAFC4isRPznlVRCyJiFyJ9QAAAN6qkpe6vjUiLkkp7ZNSGhQRfxkR36/g+gAAQMEqebW3L0fE3hHxfES0RcQ9EXF1BdcHAAAKVrH4yTlvjIiLN38BAABUVCXf9gYAAFA14gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gcAACiC+AEAAIogfgAAgCKIHwAAoAjiBwAAKIL4AQAAiiB+AACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAIddUeYFeSc45XN6yOtq6NsU/9gGio7V3tkQAA9ni5Y2N0/ObZiJSibtQhkep6VXsk9lDiZ7Pn170cs5f+W7yxsTVqUk105s44fMC4OHX4UVFf4z9AAICdLecc6x7877H2n74ckTsjckTU1kW/j34x+p50SaSUqj0ie5iKxE9Kae1b7mqIiG/lnC+pxPo78qvWZXHjS/8SG3PHpjvypn/MeWNhvNq+Oi4bc7L/+AAAdrK191wVa//52oj21t+5v+XOKyK3r4umD11RpcnYU1XknJ+cc9OWr4gYGhHrI+KfKrF2d9y37N9/Gz5b6cid8XL7a/H8uperMBUAwJ6ra90bsfb+r/1e+ERE5PbWaPmnL0fX+rf+/Tm8O9W44MGHI+LViHisCmv/ntbO9ljStnK7j7d3dcScNxZWcCIAgD1f+7wfRqrd/qkFqbYuNsz/XxWciBJUI35mRsQ/5pzzth5MKV2QUpqTUpqzYsWKHh+mI3fu8C1t7V0be3wOAICS5A1tEdv+4+Bvt9nYVqFpKEVF4yeltG9EvD8ibtveNjnnm3POzTnn5iFDhvT4TE21DdG3tn67j/eu6RXvbRrV43MAAJSk/qA/idz5+6cdbJE7NkavcX9cwYkoQaWP/JwTEY/nnH9d4XW3qyalOGHv5qhP2772Q12qjcMHjK3wVAAAe7a64WOj/uBjIuq28dEivXpH78P+NOr22a/yg7FHq0b8bPeoT7UcPfiQOGrwIVGXaqMubfot6V3TK/rVNsRfjv6gS10DAPSAQZ+6J3odcHik3n0jUs2mr959o37cH8XAy75b7fHYA6XtnHqz8xdK6b9ExI8iYljOuaU7z2lubs5z5szp2cG2smrj2pj7xq+irbM93tMwJA7tt1/UpmqcFgUAUIacc2x84clof/pfI2pqovekE6J+bHNFZ0gpPZVzruyiVEUlP+R0ZkTc193wqYZBvZriuL0nVnsMAIBipJSiftwRUT/uiGqPQgEqFj855wsrtRYAAMBbeU8XAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARRA/AABAEcQPAABQBPEDAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBHEDwAAUATxAwAAFEH8AAAARUg552rPsF0ppRUR8VIVlt47Il6rwrpsn9dk1+L12PV4TXY9XpNdi9dj17MrvSb75ZyHVHsIet4uHT/VklKak3NurvYc/JbXZNfi9dj1eE12PV6TXYvXY9fjNaEavO0NAAAogvgBAACKIH627eZqD8Dv8ZrsWrweux6vya7Ha7Jr8XrserwmVJxzfgAAgCI48gMAABRB/AAAAEUQPwAAQBHEz1ZSSoNTSt9LKa1LKb2UUjqj2jOVLKXUO6X0nc2vRUtK6emU0onVnouIlNK4lFJbSumOas9CRErptJTSLzb/v+tXKaWjqz1TyVJKo1NK/5JSWpVSeiWl9Hcppbpqz1WKlNKfp5TmpJTaU0r/8JbHjksp/TKl1JpSejSltF+VxizK9l6TlNIfp5R+lFJ6PaW0IqX0Tyml4VUclQKIn991Q0RsiIihEXFmRNyYUjqkuiMVrS4iFkfE+yNiQER8PiLuSSmNruZQRMSm/1aerPYQRKSUjo+Ir0fE/xsR/SLimIhYVNWh+FZEvBoRwyNiUmz6f9jF1RyoMEsj4isRccvWd6aU9o6I+2LTz5LBETEnIu6u+HRl2uZrEhGDYtMV30ZHxH4R0RIRt1Z0Morjb6I2Syk1RsSMiDg057w2Ih5PKT0QEWdHxOVVHa5QOed1EfHFre76fkrp1xFxeES8WI2Z2HSUISJWR8T/joix1Z2GiPhSRFyVc/4/m2+/XM1hiIiIMRHxdznntoh4JaX0rxHhL9IqJOd8X0RESqk5It6z1UMfiogFOed/2vz4FyPitZTS+JzzLys+aEG295rknH+w9XYppb+LiJ9UdjpK48jPbx0YEZ055+e3um9e+IG1y0gpDY1Nr9OCas9SqpRS/4i4KiI+Ve1ZiEgp1UZEc0QMSSm9kFJasvktVg3Vnq1w34yI01JKfVNKIyPixIj41yrPxKaf5/O23Nj8F2y/Cj/ndyXHhJ/x9DDx81tNEfHGW+57Iza9jYQqSyn1iog7I+I2f0NXVV+OiO/knBdXexAiYtNbdHtFxIcj4ujY9BaryRHx11WciU1/c31IRKyJiCWx6e1V91dzICLCz/ldWkrpsIj4QkR8ptqzsGcTP7+1NiL6v+W+/rHp/adUUUqpJiJuj03nY/15lccpVkppUkT8aUT89yqPwm+t3/zP63POy3LOr0XEdRFxUhVnKtrm/189HJvOLWmMiL1j03kNX6/mXESEn/O7rJTS2Ij4QURcmnN+rNrzsGcTP7/1fETUpZTGbXXfxHD4tapSSikivhOb/oZ7Rs55Y5VHKtnU2HRS6m9SSq9ExKcjYkZKaW41hypZznlVbDqykKs9C28aHBGjYtM5P+0555Wx6QRuQVp9C2LTz/WIePNc3wPCz/mq2nzFvR9HxJdzzrdXex72fOJns83v/b0vIq5KKTWmlI6MiA/GpiMOVM+NEfHeiPhvOef1O9qYHnVzbPqDwqTNXzdFxEMRMb16IxGb/mB9SUppn5TSoIj4y4j4fnVHKtfmo2+/johPpJTqUkoDI2JmbHWuCT1r8+97n4iojYjalFKfzZca/15EHJpSmrH58S9ExDPeSt3ztveabD4n7pGIuCHnfFN1p6QUKWd/YbhFSmlwbLoM4/ERsTIiLs85f7e6U5Vr898GvRgR7RHRsdVDF+ac76zKULxp85WSxuacz6r2LCXbfD7cNyPijIhoi4h7IuKzm680RhVsfovoN2LTUYbOiHg0Ij6Zc361imMVY/P/m658y91fyjl/MaX0pxHxd7Hpssr/ERHn5pxfrOyE5dneaxKbjlp/MSLWbf1AzrmpIoNRJPEDAAAUwdveAACAIogfAACgCOIHAAAogvgBAACKIH4AAIAiiB8AAKAI4gdgN5RSuial9Jc9tO+fpZQO6Yl9A0A1+ZwfgN1MSmlIRPxnbPqQ2fU9sP9TI+KjOecZO3vfAFBNjvwA7H7OjYh/6Ynw2eyBiJiWUhreQ/sHgKoQPwBVllI6IKX0ekppyubbI1JKr6WUpm7nKSdGxE+2ev65KaXH37LPnFIau/nX/5BS+lZK6QcppbUppX9PKQ1LKX0jpbQqpfTLlNLkLc/NObdFxFMR8V937ncKANUlfgCqLOf8q4iYFRF3ppT6RsStEfEPOed/285TJkTEc3/gMqdGxF9HxN4R0R4RT0TE3M23/2dEXPeW7X8RERP/wDUAYJcmfgB2ATnnb0fEwoj4j4gYHhH/39tsPjAiWv7AJb6Xc35q81Gd70VEW875H3POnRFxd0RMfsv2LZvXAYA9hvgB2HV8OyIOjYjrc87tb7Pdqojo9wfue/lWv16/jdtNb9m+X0Ss/gPXAIBdmvgB2AWklJoi4hsR8Z2I+GJKafDbbP5MRBy41e11EdF3q30N2wkjvTci5u2E/QDALkP8AOwavhkRT+Wcz4+IhyLiprfZ9l8i4v1b3Z4XEYeklCallPpExBffzSAppd4RcXhE/Ojd7AcAdjXiB6DKUkofjIgTIuKizXf9VURMSSmduZ2n/GNEnJRSaoiIyDk/HxFXRcSPY9N5Q49v53nd9YGI+Lec89J3uR8A2KX4kFOA3VBK6asR8WrO+Rs9sO//iIjzcs7P7ux9A0A1iR8AAKAI3vYGAAAUQfwAAABFED8AAEARxA8AAFAE8QMAABRB/AAAAEUQPwAAQBH+L+97ZFZ36sBkAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Done; plotting time = 0.51 s\n",
"\n",
"Total time = 90.54 s\n",
"\n",
"End time: 2022-10-31 21:32:31.660741\n"
]
}
],
"source": [
"sim.simulate()\n",
"sim.analyze()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "ceb34061",
"metadata": {},
"outputs": [],
"source": [
"# plotting\n",
"\n",
"#sim.analysis.plotLFP( plots = ['timeSeries', 'locations'] , electrodes=[ 'all'], lineWidth=1000 , fontSize=14, saveFig=True)\n",
"\n",
"# from matplotlib import pyplot\n",
"# %matplotlib inline\n",
"# pyplot.plot(t, ap1 )\n",
"# #pyplot.xlim((0, 10))\n",
"# pyplot.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ddb4904a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Duration: 0:01:34.957720\n"
]
}
],
"source": [
"# show the execution time\n",
"\n",
"end_time = datetime.now()\n",
"print('Duration: {}'.format(end_time - start_time))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb4751f0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"id": "d18ce34b",
"metadata": {},
"outputs": [],
"source": [
"# Longitudinal Current: picoamp\n",
"\n",
"\n",
"\n",
"# xraxia = xr*1e6 #ohm/cm\n",
"# xraxia = xraxia*2*1e-4 # ohm, length between node to MYSA is 2 micron\n",
"\n",
"\n",
"# v_diff_00 = (Abeta0_vext1_node0-Abeta0_vext1_MYSA0)/1000 #volt\n",
"# Longi_Current_node0_MYSA0 = v_diff_00/xraxia #amp\n",
"# Longi_Current_node0_MYSA0 = Longi_Current_node0_MYSA0*1e12 #picoamp\n",
"\n",
"# v_diff_12 = (Abeta0_vext1_node1-Abeta0_vext1_MYSA2)/1000 #volt\n",
"# Longi_Current_node1_MYSA2 = v_diff_12/xraxia \n",
"# Longi_Current_node1_MYSA2 = Longi_Current_node1_MYSA2*1e12 \n",
"\n",
"# v_diff_24 = (Abeta0_vext1_node2-Abeta0_vext1_MYSA4)/1000 #volt\n",
"# Longi_Current_node2_MYSA4 = v_diff_24/xraxia \n",
"# Longi_Current_node2_MYSA4 = Longi_Current_node2_MYSA4*1e12 \n",
"\n",
"\n",
"\n",
"\n",
"# v_diff_36 = (Abeta0_vext1_node3-Abeta0_vext1_MYSA6)\n",
"\n",
"\n",
"\n",
"\n",
"# # v_diff_36 = (Abeta0_vext1_node3-Abeta0_vext1_MYSA6)/1000 #volt\n",
"# # Longi_Current_node3_MYSA6 = v_diff_36/xraxia \n",
"# # Longi_Current_node3_MYSA6 = Longi_Current_node3_MYSA6*1e12 \n",
"\n",
"# v_diff_48 = (Abeta0_vext1_node4-Abeta0_vext1_MYSA8)/1000 #volt\n",
"# Longi_Current_node4_MYSA8 = v_diff_48/xraxia \n",
"# Longi_Current_node4_MYSA8 = Longi_Current_node4_MYSA8*1e12 \n",
"\n",
"# v_diff_510 = (Abeta0_vext1_node5-Abeta0_vext1_MYSA10)/1000 #volt\n",
"# Longi_Current_node5_MYSA10 = (v_diff_510/xraxia)*1e12 \n",
"\n",
"# v_diff_612 = (Abeta0_vext1_node6-Abeta0_vext1_MYSA12)/1000 #volt\n",
"# Longi_Current_node6_MYSA12 = (v_diff_612/xraxia)*1e12 \n",
"\n",
"# v_diff_714 = (Abeta0_vext1_node7-Abeta0_vext1_MYSA14)/1000 #volt\n",
"# Longi_Current_node7_MYSA14 = (v_diff_714/xraxia)*1e12 \n",
"\n",
"# v_diff_816 = (Abeta0_vext1_node8-Abeta0_vext1_MYSA16)/1000 #volt\n",
"# Longi_Current_node8_MYSA16 = (v_diff_816/xraxia)*1e12 \n",
"\n",
"# v_diff_918 = (Abeta0_vext1_node9-Abeta0_vext1_MYSA18)/1000 #volt\n",
"# Longi_Current_node9_MYSA18 = (v_diff_918/xraxia)*1e12 \n",
"\n",
"# v_diff_1020 = (Abeta0_vext1_node10-Abeta0_vext1_MYSA20)/1000 #volt\n",
"# Longi_Current_node10_MYSA20 = (v_diff_1020/xraxia)*1e12 \n",
"\n",
"# v_diff_1122 = (Abeta0_vext1_node11-Abeta0_vext1_MYSA22)/1000 #volt\n",
"# Longi_Current_node11_MYSA22 = (v_diff_1122/xraxia)*1e12 \n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "d833f599",
"metadata": {},
"outputs": [],
"source": [
"# Transverse current: Picoamp/micron^2\n",
"\n",
"\n",
"# v_diff00 = (Abeta0_vext1_node0 - Abeta1_vext1_node0)/1000 #volt\n",
"# Trans_Current_node0_node0 = (v_diff00 * 6.9e+04 )*1e12/1e8 #volt*S/cm2 = Amp/cm2 = PicoAMP/cm2 = PicoAMP/micron^2\n",
"\n",
"# v_diff11 = (Abeta0_vext1_node1 - Abeta1_vext1_node1)/1000 #volt\n",
"# Trans_Current_node1_node1 = v_diff11 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff22 = (Abeta0_vext1_node2 - Abeta1_vext1_node2)/1000 #volt\n",
"# Trans_Current_node2_node2 = v_diff22 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff33 = (Abeta0_vext1_node3 - Abeta1_vext1_node3)/1000 #volt\n",
"# Trans_Current_node3_node3 = v_diff33 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff44 = (Abeta0_vext1_node4 - Abeta1_vext1_node4)/1000 #volt\n",
"# Trans_Current_node4_node4 = v_diff44 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff55 = (Abeta0_vext1_node5 - Abeta1_vext1_node5)/1000 #volt\n",
"# Trans_Current_node5_node5 = v_diff55 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff66 = (Abeta0_vext1_node6 - Abeta1_vext1_node6)/1000 #volt\n",
"# Trans_Current_node6_node6 = v_diff66 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff77 = (Abeta0_vext1_node7 - Abeta1_vext1_node7)/1000 #volt\n",
"# Trans_Current_node7_node7 = v_diff77 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff88 = (Abeta0_vext1_node8 - Abeta1_vext1_node8)/1000 #volt\n",
"# Trans_Current_node8_node8 = v_diff88 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff99 = (Abeta0_vext1_node9 - Abeta1_vext1_node9)/1000 #volt\n",
"# Trans_Current_node9_node9 = v_diff99 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff1010 = (Abeta0_vext1_node10 - Abeta1_vext1_node10)/1000 #volt\n",
"# Trans_Current_node10_node10 = v_diff1010 * 6.9e+04 *1e12/1e8 \n",
"\n",
"# v_diff1111 = (Abeta0_vext1_node11 - Abeta1_vext1_node11)/1000 #volt\n",
"# Trans_Current_node11_node11 = v_diff1111 * 6.9e+04 *1e12/1e8 \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "cbe681f7",
"metadata": {},
"outputs": [],
"source": [
"# import csv\n",
"\n",
"# with open('v_diff66_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , v_diff66 ))\n",
" \n",
" \n",
" \n",
"import csv\n",
"\n",
"# with open('LongVoltageDifference_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , v_diff_36 ))\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"id": "8f3b15f1",
"metadata": {},
"source": [
"#### saving the data"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "890baeb5",
"metadata": {},
"outputs": [],
"source": [
"## saving the data\n",
"\n",
"\n",
"import csv\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# #################################### Connected to ground \n",
"\n",
"# with open('ConnectGround_v_Abeta0_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 )) \n",
"\n",
"\n",
"\n",
"# with open('ConnectGround_LongiCurrent_Abeta0_NodetoMYSA_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Longi_Current_node0_MYSA0 , Longi_Current_node1_MYSA2 , Longi_Current_node2_MYSA4 , Longi_Current_node3_MYSA6 , Longi_Current_node4_MYSA8 , Longi_Current_node5_MYSA10 , Longi_Current_node6_MYSA12 , Longi_Current_node7_MYSA14 , Longi_Current_node8_MYSA16 , Longi_Current_node9_MYSA18 , Longi_Current_node10_MYSA20 , Longi_Current_node11_MYSA22 ))\n",
" \n",
" \n",
"\n",
"# with open('ConnectGround_TransCurrent_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Trans_Current_node0_node0 , Trans_Current_node1_node1 , Trans_Current_node2_node2 , Trans_Current_node3_node3 , Trans_Current_node4_node4 , Trans_Current_node5_node5 , Trans_Current_node6_node6 , Trans_Current_node7_node7 , Trans_Current_node8_node8 , Trans_Current_node9_node9 , Trans_Current_node10_node10 , Trans_Current_node11_node11 ))\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" \n",
"# ##################################### Not connected to ground, Stimulate only one fiber \n",
"\n",
" \n",
"with open('radius6_v_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
" csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 , Abeta0_v_node12 , Abeta0_v_node13 , Abeta0_v_node14 , Abeta0_v_node15 , Abeta0_v_node16 , Abeta0_v_node17 , Abeta0_v_node18 , Abeta0_v_node19 , Abeta0_v_node20 , Abeta0_v_node21 , Abeta0_v_node22 , Abeta0_v_node23 , Abeta0_v_node24 , Abeta0_v_node25 , Abeta0_v_node26 , Abeta0_v_node27 , Abeta0_v_node28 , Abeta0_v_node29 , Abeta0_v_node30 , Abeta0_v_node31 , Abeta0_v_node32 , Abeta0_v_node33 , Abeta0_v_node34 , Abeta0_v_node35 )) \n",
"\n",
"\n",
" \n",
" \n",
"with open('radius6_imembrane_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
" csv.writer(f).writerows(zip( t , Abeta0_imembrane_node0 , Abeta0_imembrane_node1 , Abeta0_imembrane_node2 , Abeta0_imembrane_node3 , Abeta0_imembrane_node4 , Abeta0_imembrane_node5 , Abeta0_imembrane_node6 , Abeta0_imembrane_node7 , Abeta0_imembrane_node8 , Abeta0_imembrane_node9 , Abeta0_imembrane_node10 , Abeta0_imembrane_node11 , Abeta0_imembrane_node12 , Abeta0_imembrane_node13 , Abeta0_imembrane_node14 , Abeta0_imembrane_node15 , Abeta0_imembrane_node16 , Abeta0_imembrane_node17 , Abeta0_imembrane_node18 , Abeta0_imembrane_node19 , Abeta0_imembrane_node20 , Abeta0_imembrane_node21 , Abeta0_imembrane_node22 , Abeta0_imembrane_node23 , Abeta0_imembrane_node24 , Abeta0_imembrane_node25 , Abeta0_imembrane_node26 , Abeta0_imembrane_node27 , Abeta0_imembrane_node28 , Abeta0_imembrane_node29 , Abeta0_imembrane_node30 , Abeta0_imembrane_node31 , Abeta0_imembrane_node32 , Abeta0_imembrane_node33 , Abeta0_imembrane_node34 , Abeta0_imembrane_node35 )) \n",
" \n",
" \n",
" \n",
" \n",
"# with open('i_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 )) \n",
"\n",
"\n",
"# with open('LongiCurrent_Abeta0_NodetoMYSA_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Longi_Current_node0_MYSA0 , Longi_Current_node1_MYSA2 , Longi_Current_node2_MYSA4 , Longi_Current_node3_MYSA6 , Longi_Current_node4_MYSA8 , Longi_Current_node5_MYSA10 , Longi_Current_node6_MYSA12 , Longi_Current_node7_MYSA14 , Longi_Current_node8_MYSA16 , Longi_Current_node9_MYSA18 , Longi_Current_node10_MYSA20 , Longi_Current_node11_MYSA22 ))\n",
" \n",
" \n",
"\n",
"# with open('TransCurrent_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Trans_Current_node0_node0 , Trans_Current_node1_node1 , Trans_Current_node2_node2 , Trans_Current_node3_node3 , Trans_Current_node4_node4 , Trans_Current_node5_node5 , Trans_Current_node6_node6 , Trans_Current_node7_node7 , Trans_Current_node8_node8 , Trans_Current_node9_node9 , Trans_Current_node10_node10 , Trans_Current_node11_node11 ))\n",
" \n",
" \n",
" \n",
"# with open('v_Abeta1_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta1_v_node0 , Abeta1_v_node1 , Abeta1_v_node2 , Abeta1_v_node3 , Abeta1_v_node4 , Abeta1_v_node5 , Abeta1_v_node6 , Abeta1_v_node7 , Abeta1_v_node8 , Abeta1_v_node9 , Abeta1_v_node10 , Abeta1_v_node11 )) \n",
" \n",
" \n",
" \n",
" \n",
" \n",
"# ##################################### Not connected to ground, Stimulate BOTH fibers \n",
"\n",
"\n",
"# with open('v_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_node0 , Abeta0_v_node1 , Abeta0_v_node2 , Abeta0_v_node3 , Abeta0_v_node4 , Abeta0_v_node5 , Abeta0_v_node6 , Abeta0_v_node7 , Abeta0_v_node8 , Abeta0_v_node9 , Abeta0_v_node10 , Abeta0_v_node11 )) \n",
"\n",
"\n",
"\n",
"# with open('LongiCurrent_Abeta0_NodetoMYSA_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Longi_Current_node0_MYSA0 , Longi_Current_node1_MYSA2 , Longi_Current_node2_MYSA4 , Longi_Current_node3_MYSA6 , Longi_Current_node4_MYSA8 , Longi_Current_node5_MYSA10 , Longi_Current_node6_MYSA12 , Longi_Current_node7_MYSA14 , Longi_Current_node8_MYSA16 , Longi_Current_node9_MYSA18 , Longi_Current_node10_MYSA20 , Longi_Current_node11_MYSA22 ))\n",
" \n",
" \n",
"\n",
"# with open('TransCurrent_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Trans_Current_node0_node0 , Trans_Current_node1_node1 , Trans_Current_node2_node2 , Trans_Current_node3_node3 , Trans_Current_node4_node4 , Trans_Current_node5_node5 , Trans_Current_node6_node6 , Trans_Current_node7_node7 , Trans_Current_node8_node8 , Trans_Current_node9_node9 , Trans_Current_node10_node10 , Trans_Current_node11_node11 ))\n",
" \n",
" \n",
" \n",
"\n",
"# with open('Connectground_vext1_Abeta0_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_vext1_node0 , Abeta0_vext1_node1 , Abeta0_vext1_node2 , Abeta0_vext1_node3 , Abeta0_vext1_node4 , Abeta0_vext1_node5 , Abeta0_vext1_node6 , Abeta0_vext1_node7 , Abeta0_vext1_node8 , Abeta0_vext1_node9 , Abeta0_vext1_node10 , Abeta0_vext1_node11 )) \n",
" \n",
" \n",
"# with open('vext1_Abeta1_stimulateonlyAbeta0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta1_vext1_node0 , Abeta1_vext1_node1 , Abeta1_vext1_node2 , Abeta1_vext1_node3 , Abeta1_vext1_node4 , Abeta1_vext1_node5 , Abeta1_vext1_node6 , Abeta1_vext1_node7 , Abeta1_vext1_node8 , Abeta1_vext1_node9 , Abeta1_vext1_node10 , Abeta1_vext1_node11 )) \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "7a4d2e6a",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# with open('STIN220_vext1_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_vext1_STIN220))\n",
" \n",
" \n",
"# with open('STIN220_v_Abeta0_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_v_STIN220)) \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a594bc51",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 26,
"id": "8e386b67",
"metadata": {},
"outputs": [],
"source": [
" \n",
"# with open('ConnectedGround_icap_Abeta0_stimulateonlyAbata0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_icap_node0 , Abeta0_icap_node1 , Abeta0_icap_node2 , Abeta0_icap_node3 , Abeta0_icap_node4 , Abeta0_icap_node5 , Abeta0_icap_node6 , Abeta0_icap_node7 , Abeta0_icap_node8 , Abeta0_icap_node9 , Abeta0_icap_node10 , Abeta0_icap_node11 )) \n",
"\n",
" \n",
" \n",
"# with open('ConnectedGround_ik_Abeta0_stimulateonlyAbata0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_ik_node0 , Abeta0_ik_node1 , Abeta0_ik_node2 , Abeta0_ik_node3 , Abeta0_ik_node4 , Abeta0_ik_node5 , Abeta0_ik_node6 , Abeta0_ik_node7 , Abeta0_ik_node8 , Abeta0_ik_node9 , Abeta0_ik_node10 , Abeta0_ik_node11 )) \n",
"\n",
"\n",
" \n",
"# with open('ConnectedGround_il_Abeta0_stimulateonlyAbata0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_il_node0 , Abeta0_il_node1 , Abeta0_il_node2 , Abeta0_il_node3 , Abeta0_il_node4 , Abeta0_il_node5 , Abeta0_il_node6 , Abeta0_il_node7 , Abeta0_il_node8 , Abeta0_il_node9 , Abeta0_il_node10 , Abeta0_il_node11 )) \n",
"\n",
"\n",
" \n",
"# with open('ConnectedGround_ina_Abeta0_stimulateonlyAbata0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_ina_node0 , Abeta0_ina_node1 , Abeta0_ina_node2 , Abeta0_ina_node3 , Abeta0_ina_node4 , Abeta0_ina_node5 , Abeta0_ina_node6 , Abeta0_ina_node7 , Abeta0_ina_node8 , Abeta0_ina_node9 , Abeta0_ina_node10 , Abeta0_ina_node11 )) \n",
"\n",
"\n",
" \n",
"# with open('ConnectedGround_inap_Abeta0_stimulateonlyAbata0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_inap_node0 , Abeta0_inap_node1 , Abeta0_inap_node2 , Abeta0_inap_node3 , Abeta0_inap_node4 , Abeta0_inap_node5 , Abeta0_inap_node6 , Abeta0_inap_node7 , Abeta0_inap_node8 , Abeta0_inap_node9 , Abeta0_inap_node10 , Abeta0_inap_node11 )) \n",
"\n",
" \n",
" \n",
"# with open('ConnectedGround_imembrane_Abeta0_stimulateonlyAbata0_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , Abeta0_imembrane_node0 , Abeta0_imembrane_node1 , Abeta0_imembrane_node2 , Abeta0_imembrane_node3 , Abeta0_imembrane_node4 , Abeta0_imembrane_node5 , Abeta0_imembrane_node6 , Abeta0_imembrane_node7 , Abeta0_imembrane_node8 , Abeta0_imembrane_node9 , Abeta0_imembrane_node10 , Abeta0_imembrane_node11 )) \n",
" \n",
" \n"
]
}
],
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