{
"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 21:39:01.706729\n",
"\n",
"Creating network of 4 cell populations on 1 hosts...\n",
" Number of cells on node 0: 4 \n",
" Done; cell creation time = 0.35 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": [
"3.0\n",
"1903.5717144126938\n",
"3.0\n",
"1903.5688943106286\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": [
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" \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[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": [
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" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -23.8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23.8 0 \n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -11.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11.9 \n"
]
},
{
"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 = 92.12 s; real-time ratio: 0.00.\n",
"\n",
"Gathering data...\n",
" Done; gather time = 0.51 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: 92.12 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 = 0.51 s\n",
"\n",
"Total time = 94.68 s\n",
"\n",
"End time: 2022-10-31 21:40:36.384191\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: 0:01:37.842914\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_edgedist3_.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_edgedist3_.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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