{
"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(2,3):\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(1,3):\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(1,3):\n",
" for j in range(2,3):\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(1,3):\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(2,3):\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",
"\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",
"\n",
"\n",
"netParams.importCellParams(\n",
" cellInstance=True,\n",
" label='type2', \n",
" conds={'cellType': 'type2', 'cellModel': 'type2'},\n",
" fileName='type2.hoc', \n",
" cellName='type2', \n",
" importSynMechs=True) ;\n",
"\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': 'type2', \n",
" 'numCells':1 , \n",
" 'cellModel': 'type2', \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 21:36:28.187301\n",
"\n",
"Creating network of 4 cell populations on 1 hosts...\n",
" Number of cells on node 0: 4 \n",
" Done; cell creation time = 0.41 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",
"63978.8601460533\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",
"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",
"\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[0]\n",
" nodes=int(nodes)\n",
" \n",
" \n",
" nl = parameters[x][4]\n",
" nodeD = parameters[x][1]\n",
" paraD1 = nodeD\n",
" axonD = parameters[x][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": "004941aa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[36. 36.]\n"
]
}
],
"source": [
"print(Number_of_nodes)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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 = ((6*2)+(6*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": 12,
"id": "b71ff07f",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"\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[0][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 = (6*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": 14,
"id": "2f2f0781",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(Boundary_neighboring)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7808a6c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
},
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"GMAT_BOUNDARY00.printf() "
]
},
{
"cell_type": "markdown",
"id": "b2a6c256",
"metadata": {},
"source": [
"#### Recordings"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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": 17,
"id": "25ca22ac",
"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": 18,
"id": "ca5603a0",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Vector[89]"
]
},
"execution_count": 18,
"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_vext\"+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_vext\"+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": 19,
"id": "cd6d9f09",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Running simulation for 6.0 ms...\n",
" Done; run time = 90.65 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 0.50 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: 90.65 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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\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 = 0.50 s\n",
"\n",
"Total time = 93.39 s\n",
"\n",
"End time: 2022-10-31 21:38:01.580352\n"
]
}
],
"source": [
"sim.simulate()\n",
"sim.analyze()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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": 21,
"id": "ddb4904a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Duration: 0:01:37.226554\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": 22,
"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",
"# 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": 23,
"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 * 3.45e+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 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff22 = (Abeta0_vext1_node2 - Abeta1_vext1_node2)/1000 #volt\n",
"# Trans_Current_node2_node2 = v_diff22 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff33 = (Abeta0_vext1_node3 - Abeta1_vext1_node3)/1000 #volt\n",
"# Trans_Current_node3_node3 = v_diff33 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff44 = (Abeta0_vext1_node4 - Abeta1_vext1_node4)/1000 #volt\n",
"# Trans_Current_node4_node4 = v_diff44 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff55 = (Abeta0_vext1_node5 - Abeta1_vext1_node5)/1000 #volt\n",
"# Trans_Current_node5_node5 = v_diff55 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff66 = (Abeta0_vext1_node6 - Abeta1_vext1_node6)/1000 #volt\n",
"# Trans_Current_node6_node6 = v_diff66 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff77 = (Abeta0_vext1_node7 - Abeta1_vext1_node7)/1000 #volt\n",
"# Trans_Current_node7_node7 = v_diff77 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff88 = (Abeta0_vext1_node8 - Abeta1_vext1_node8)/1000 #volt\n",
"# Trans_Current_node8_node8 = v_diff88 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff99 = (Abeta0_vext1_node9 - Abeta1_vext1_node9)/1000 #volt\n",
"# Trans_Current_node9_node9 = v_diff99 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff1010 = (Abeta0_vext1_node10 - Abeta1_vext1_node10)/1000 #volt\n",
"# Trans_Current_node10_node10 = v_diff1010 * 3.45e+04 *1e12/1e8 \n",
"\n",
"# v_diff1111 = (Abeta0_vext1_node11 - Abeta1_vext1_node11)/1000 #volt\n",
"# Trans_Current_node11_node11 = v_diff1111 * 3.45e+04 *1e12/1e8 \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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('mis_LongTranVoltageDifference_stimulateBOTH_edgedist0.1_.csv', 'w', newline='') as f:\n",
"# csv.writer(f).writerows(zip( t , v_diff_36 , v_diff33 )) \n",
" "
]
},
{
"cell_type": "markdown",
"id": "8f3b15f1",
"metadata": {},
"source": [
"#### saving the data"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "890baeb5",
"metadata": {},
"outputs": [],
"source": [
"## saving the data\n",
"\n",
"\n",
"import csv\n",
"\n",
"\n",
"\n",
"\n",
" \n",
"with open('radius6_misaligned_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_misaligned_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",
"\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('m_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",
" \n",
"# with open('m_vext1_Abeta0_stimulateBOTH_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",
"\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",
" \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": 26,
"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": 27,
"id": "8e386b67",
"metadata": {},
"outputs": [],
"source": [
" \n",
"# with open('icap_Abeta0_stimulateBOTH_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('ik_Abeta0_stimulateBOTH_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('il_Abeta0_stimulateBOTH_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('ina_Abeta0_stimulateBOTH_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('inap_Abeta0_stimulateBOTH_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('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 )) \n",
" \n",
" \n"
]
}
],
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