_D1 = _util.NamedDict( 'D1', Krp={ prox: 0.030412982511354256, med: 0.1187764150066657, dist: 0.04371546368456764 }, KaF={ prox: 1198.6106336137718, med: 694.7565410220733, dist: 73.63781307916403 }, KaS={ prox: 42.301506989682956, med: 598.1981044394101, dist: 48.109060863002135 }, Kir={ prox: 4.593346732504891, med: 4.593346732504891, dist: 4.593346732504891 }, CaL13={ prox: 13.497468485634915 * ghKluge, med: 0.0003742350945052135 * ghKluge, dist: 0.14313485017720698 * ghKluge }, CaL12={ prox: 8.224130813140018 * ghKluge, med: 4.755641803836486 * ghKluge, dist: 1.359189909219976 * ghKluge }, CaR={ prox: 2.0319640194309367 * ghKluge, med: 42.098081446111564 * ghKluge, dist: 77.87435524955357 * ghKluge }, CaN={ prox: 4.8225437615192845 * ghKluge, med: 0.0 * ghKluge, dist: 0.0 * ghKluge }, CaT={ prox: 0.0 * ghKluge, med: 0.06754073749968142 * ghKluge, dist: 0.003149595759888971 * ghKluge }, NaF={ prox: 278913.52391468134, med: 3191.508903477709, dist: 1469.6950316113168 }, SKCa={ prox: 4.098903673534784, med: 4.098903673534784, dist: 4.098903673534784 }, BKCa={ prox: 5.7478346619307406, med: 5.7478346619307406, dist: 5.7478346619307406 }, CaCC={ prox: 0.31307249403534215, med: 0.31307249403534215, dist: 0.31307249403534215 }, )
_proto = _util.NamedDict( 'proto', KDr={ prox: 40, dist: 7.75, axon: 77.5 }, Kv3={ prox: 800, dist: 140, axon: 1400 }, # , med:38.4}, KvF={ prox: 10, dist: 10, axon: 100 }, # , med: 48}, KvS={ prox: 3, dist: 3, axon: 30 }, # , med: 100 KCNQ={ prox: 0.04, dist: 0.04, axon: 0.04 }, # , med: 2.512 NaF={ prox: 40000, dist: 400, axon: 40000 }, # , med: 100 NaS={ prox: 0.5, dist: 0.5, axon: 0.5 }, # med: 251.2, Ca={ prox: 0.1, dist: 0.06, axon: 0 }, # med: 0, HCN1={ prox: 0.2, dist: 0.2, axon: 0 }, # , med: 0 HCN2={ prox: 0.5, dist: 0.5, axon: 0 }, # med: 0, SKCa={ prox: 2, dist: 0.15, axon: 0 }, # med: 0, BKCa={ prox: 0.1, dist: 0.1, axon: 0 }, # med: 10, )
# UNITS: meters prox = (0, 50.1e-6) med = (50.1e-6, 50e-6) dist = (50e-6, 1000e-6) #If using swc files for morphology, can add with morphology specific helper variables #e.g. med=(26.1e-6, 50e-6,'_2') #_1 as soma, _2 as apical dend, _3 as basal dend and _4 as axon #Parameters used by optimization from here down morph_file = {'D1':'MScell-soma.p', 'D2': 'MScell-soma.p'} # old_version. #CONDUCTANCES - UNITS of Siemens/meter squared _D1 = _util.NamedDict( 'D1', KaF = {prox:1530.85}, KaS = {prox:113.95}, Kir = {prox:12.7875}, CaL12 = {prox:29.868*ghKluge}, CaR = {prox:10.63412*ghKluge}, NaF = {prox:140e3}, ) _D2 = _util.NamedDict( 'D2', KaF = {prox:1441}, KaS = {prox:672}, Kir = {prox:8.2}, CaL12 = {prox:4*ghKluge}, CaR = {prox:10*ghKluge}, NaF = {prox:150.0e3}, ) Condset = _util.NamedDict(
from . import param_cond from moose_nerp.prototypes import util as _util SpineParams = _util.NamedDict( 'SpineParams', spineDensity=1.01e6, #should make this distance dependent necklen=0.5e-6, #define all these parameters elsewhere neckdia=0.12e-6, headdia=0.5e-6, headlen=0.5e-6, headRA= 1.25, #additional factor of 4 due to exptl higher than expected Ra Spine neckRA=11.3, spineRM=1.875, spineCM=0.01, spineELEAK=-70e-3, spineEREST=-80e-3, spineStart=26.1e-6, spineEnd=300e-6, compensationSpineDensity=0, spineChanList=[['CaL13'], ['CaL12', 'CaR', 'CaT']], #spines added to branches that are childrne of this branch: spineParent='secdend11', #spineCond = [0.65 *cond for param_cond.ghKluge], )
_ep = _util.NamedDict( 'ep', KDr={ prox: 4.770646343567948, dist: 11.580858134398845, axon: 195.0403867814316 }, # KDr is Kv2 Kv3={ prox: 736.8216998732888, dist: 1402.900645554406, axon: 729.2217379045869 }, KvS={ prox: 9.625003609239965, dist: 14.816596591818115, axon: 1.5141050650221697 }, KvF={ prox: 10.067142050937445, dist: 7.8844967404188235, axon: 16.175716784730458 }, NaF={ prox: 181.05412302395015, dist: 151.40473183042286, axon: 18391.466370001686 }, NaS={ prox: 4.978184560770554, dist: 6.763970102779737, axon: 7.136171989201076 }, Ca={ prox: 0.15508085299411955, dist: 0.08440912242796829, axon: 0 }, HCN1={ prox: 0.08583627688169095, dist: 0.44055539373336716, axon: 0 }, HCN2={ prox: 0.47365915156951843, dist: 0.5262383869994962, axon: 0 }, SKCa={ prox: 0.43383651241744997, dist: 0.6505619092332097, axon: 0 }, BKCa={ prox: 6.968570081935845, dist: 5.931895239981525, axon: 0 }, )
# e.g. med=(26.1e-6, 50e-6,'_2') # _1 as soma, _2 as apical dend, _3 as basal dend and _4 as axon # Parameters used by optimization from here down # morph_file = {'D1':'MScell-primDend.p', 'D2': 'MScell-primDend.p'} # test_version. morph_file = {'D1': 'MScelltaperspines.p', 'D2': 'MScelltaperspines.p'} # old_version. # CONDUCTANCES - UNITS of Siemens/meter squared _D1 = _util.NamedDict( 'D1', Krp={prox: 0.0266, med: 0.0740, dist: 0.02746}, KaF={prox: 1430.85, med: 406.796, dist: 91.140}, KaS={prox: 13.95, med: 1419.54, dist: 62.524}, Kir={prox: 12.7875, med: 12.7875, dist: 12.7875}, CaL13={prox: 16.8551 * ghKluge, med: 4.01164 * ghKluge, dist: 2.0984 * ghKluge}, CaL12={prox: 99.868 * ghKluge, med: 7.40227 * ghKluge, dist: 5.9285 * ghKluge}, CaR={prox: 10.63412 * ghKluge, med: 5.010147 * ghKluge, dist: 46.54782 * ghKluge}, CaN={prox: 7.2082 * ghKluge, med: 0.0 * ghKluge, dist: 0.0 * ghKluge}, CaT32={prox: 0.0 * ghKluge, med: 8.1514 * ghKluge, dist: 4.09788 * ghKluge}, CaT33={prox: 0.0 * ghKluge, med: 8.1514 * ghKluge, dist: 4.09788 * ghKluge}, NaF={prox: 229400, med: 4080.6, dist: 705.49}, SKCa={prox: 1.4664, med: 1.4664, dist: 1.4664}, BKCa={prox: 12.96, med: 12.96, dist: 12.96}, CaCC={prox: 5, med: 2, dist: 2}, ) _D2 = _util.NamedDict( 'D2', Krp={prox: 177.25, med: 177.25, dist: 27.25}, KaF={prox: 641, med: 300, dist: 100}, KaS={prox: 372, med: 32.9, dist: 0}, Kir={prox: 6.2, med: 6.2, dist: 6.2}, CaL13={prox: 10 * ghKluge, med: 4 * ghKluge, dist: 4 * ghKluge},
# CONDUCTANCES # helper variables to index the Conductance and synapses with distance # axon cylindrical so x,y=0,0, 1e-6 means parent compartment only for spherical. prox = (0, 1e-3) # Length Range of the proximal dendrite from center of soma. dist = (1e-3, 100e-3) # Channel conductances declaration for the squid axon. _squid = _util.NamedDict( 'squid', K={ prox: 375.03539122514456, dist: 528.0073725807185 }, # Potassium channel g_bar to the squid axon surface. Na={ prox: 1239.4810169236575, dist: 1349.8902763186481 }, # Sodium channel g_bar to the squid axon surface. Krp={ prox: 1, dist: 3 }, SKCa={ prox: .01, dist: .01 }) # Channel conductances Condset = _util.NamedDict( 'Condset', squid=_squid, )
_ep = _util.NamedDict( 'ep', KDr={ prox: 3.44, dist: 9.21, axon: 218.8 }, #KDr is Kv2 Kv3={ prox: 1982.9, dist: 1622.5, axon: 1012.31 }, KvS={ prox: 1.62, dist: 16.98, axon: 0.025 }, NaF={ prox: 225, dist: 141.8, axon: 69.8 }, NaS={ prox: 3.50, dist: 4.06, axon: 0.0 }, Ca={ prox: 0.036, dist: 0.073, axon: 0 }, HCN1={ prox: 0.10, dist: 0.69, axon: 0 }, HCN2={ prox: 0.85, dist: 1.10, axon: 0 }, SKCa={ prox: 10.980, dist: 2.86, axon: 0 }, BKCa={ prox: 0.92, dist: 0.698, axon: 0 }, )
_proto = _util.NamedDict( 'proto', KDr={ prox: 252.11742766709327, dist: 189.58923312652283, axon: 256.04921275490784 }, Kv3={ prox: 847.5656407446413, dist: 827.3424242663868, axon: 1215.4767445923346 }, # , med:847.5656407446413}, KvF={ prox: 32.74023152805182, dist: 38.986090675424656, axon: 88.32946939915182 }, # , med: 32.74023152805182}, KvS={ prox: 0.004268194532763626, dist: 7.2201454857388425, axon: 14.330839867323718 }, # , med: 0.004268194532763626 KCNQ={ prox: 0.33327276651857024, dist: 0.33327276651857024, axon: 0.33327276651857024 }, # , med: 0.33327276651857024 NaF={ prox: 8677.968478081542, dist: 13.051035835032058, axon: 6518.153479823622 }, # , med: 8677.968478081542 NaS={ prox: 4.896882716585459, dist: 4.428424596320797, axon: 0.7584080351316547 }, # med: 4.896882716585459, Ca={ prox: 0.022734861190674006, dist: 0.00030035624117286574, axon: 0 }, # med: 0.022734861190674006, HCN1={ prox: 0.7768751202592691, dist: 1.5515693745222825, axon: 0 }, # , med: 0.7768751202592691 HCN2={ prox: 2.5734741500058576, dist: 3.88790810050192, axon: 0 }, # med: 2.5734741500058576, SKCa={ prox: 0.07619950383116704, dist: 3.7819926981266327, axon: 0 }, # med: 0.07619950383116704, BKCa={ prox: 67.60005704890932, dist: 111.9306268912748, axon: 0 }, #BKCa={prox: 140, dist: 224, axon: 0}, # med: 67.60005704890932, )
# UNITS: meters #soma spherical so x,y=0,0, 1e-6 means prox=soma prox = (0,1e-6) #med = (0,50e-6) dist = (1e-6, 1000e-6) axon = (0.,1., 'axon') #If using swc files for morphology, specify structure using: _1 as soma, _2 as apical dend, _3 as basal dend and _4 as axon #CONDUCTANCE VALUES - UNITS of Siemens/meter squared _ep = _util.NamedDict( 'ep', KDr={prox: 40.72418803210975, dist: 15.281021142616872, axon: 54.17521010980912}, #KDr is Kv2 Kv3={prox: 894.296110564778, dist: 320.5199679796315, axon: 545.0279818514284}, KvS={prox: 2.6809614156561015, dist: 13.284569545389955, axon: 20.250942471815257}, KvF={prox: 16.52422175199889, dist: 5.728087116287962, axon: 2.9237873704641246}, NaF={prox: 463.54289871619443, dist: 44.792343245965654, axon: 3333.775290658194}, NaS={prox: 9.989552104855122, dist: 0.15181497997116822, axon: 8.240459761630305}, Ca={prox: 0.013112098865593962, dist: 1.5914295903786322, axon: 0}, HCN1={prox: 0.6535629583013801, dist: 0.034112443425029226, axon: 0}, HCN2={prox: 3.4854628178634406, dist: 3.815174011992935, axon: 0}, SKCa={prox: 2.289940602646903, dist: 1.2098490677740237, axon: 0}, BKCa={prox: 4.158820279828881, dist: 8.057287727080345, axon: 0}, ) Condset = _util.NamedDict( 'Condset', ep = _ep, ) #Kv3 produces the early, fast transient AHP, if cond is high enough
_ep = _util.NamedDict( 'ep', KDr={ prox: 40.72418803210975, dist: 15.281021142616872, axon: 54.17521010980912 }, #KDr is Kv2 Kv3={ prox: 894.296110564778, dist: 320.5199679796315, axon: 545.0279818514284 }, KvS={ prox: 12.6809614156561015, dist: 13.284569545389955, axon: 20.250942471815257 }, KvF={ prox: 16.52422175199889, dist: 5.728087116287962, axon: 2.9237873704641246 }, NaF={ prox: 463.54289871619443, dist: 44.792343245965654, axon: 3333.775290658194 }, HCN2={ prox: 3.4854628178634406, dist: 3.815174011992935, axon: 0 }, )
_D1 = _util.NamedDict( 'D1', Krp={ prox: 0.0266, med: 0.0740, dist: 0.02746 }, KaF={ prox: 1430.85, med: 406.796, dist: 91.140 }, KaS={ prox: 13.95, med: 1419.54, dist: 62.524 }, Kir={ prox: 12.7875, med: 12.7875, dist: 12.7875 }, CaL13={ prox: 16.8551 * ghKluge, med: 4.01164 * ghKluge, dist: 2.0984 * ghKluge }, CaL12={ prox: 99.868 * ghKluge, med: 7.40227 * ghKluge, dist: 5.9285 * ghKluge }, CaR={ prox: 10.63412 * ghKluge, med: 5.010147 * ghKluge, dist: 46.54782 * ghKluge }, CaN={ prox: 7.2082 * ghKluge, med: 0.0 * ghKluge, dist: 0.0 * ghKluge }, CaT={ prox: 0.0 * ghKluge, med: 8.1514 * ghKluge, dist: 4.09788 * ghKluge }, NaF={ prox: 229400, med: 4080.6, dist: 705.49 }, SKCa={ prox: 1.4664, med: 1.4664, dist: 1.4664 }, BKCa={ prox: 12.96, med: 12.96, dist: 12.96 }, CaCC={ prox: 5, med: 2, dist: 2 }, )
# helper variables to index the Conductance and synapses with distance # UNITS: meters #soma spherical so x,y=0,0, 1e-6 means prox=soma prox = (0,20e-6) #med = (0,50e-6) dist = (1e-6, 1000e-6) axon = (0.,1., 'axon') #If using swc files for morphology, specify structure using: _1 as soma, _2 as apical dend, _3 as basal dend and _4 as axon #CONDUCTANCE VALUES - UNITS of Siemens/meter squared # _proto for prototypical GP neuron _proto = _util.NamedDict( 'proto', KDr={prox: 457.11742766709327}, Kv3={prox: 895.5656407446413}, KvF={prox: 21.74023152805182}, KvS={prox: 35.004268194532763626}, NaF={prox: 32415.968478081542}, HCN2={prox: 1.5734741500058576}, ) _arky = _util.NamedDict( 'arky', KDr={prox: 708.11742766709327}, Kv3={prox: 536.5656407446413}, KvF={prox: 1.74023152805182}, KvS={prox: 48.004268194532763626}, NaF={prox: 4321.968478081542}, HCN2={prox: 3.5734741500058576}, )
f = a * (np.exp(-x/tau1) - np.exp(-x/tau2)) return f _condfrac = 1.0 SpineParams = _util.NamedDict( 'SpineParams', # Actual, experimentally reported/estimated spine density, used to # compensate for spines when spines not explicitly modeled; can be a value # or a distance dependent function #spineDensity = 1.01e6, # spineDensity as a value spineDensity = _callableSpineDensity, # spineDensity as a callable necklen = 0.5e-6, neckdia = 0.12e-6, headdia = 0.5e-6, headlen = 0.5e-6, headRA = None, neckRA = 11.3, # gives neckRa = 500 megaOhms, experimentally estimated value at least in pyramidal neurons. spineRM = None, spineCM = None, spineELEAK = None, spineEREST = None, spineStart = 26.1e-6, spineEnd = 300e-6, explicitSpineDensity = .1e6, #Density of spines to explicitly model, Should be < or = to spineDensity. TODO: Consider changing to Fraction of SpineDensity spineChanList = [[('CaL13',_condfrac)],[('CaL12',_condfrac),('CaR',_condfrac),('CaT',_condfrac)]], #TODO: Specify for each channel the gbar ratio as a dictionary or named dict rather than list; also specify which difshell; #spines added to branches that are children of this branch: spineParent = 'soma', #spineCond = [0.65 *cond for param_cond.ghKluge], )
_D1 = _util.NamedDict( 'D1', #Krp = {prox:77.963, med:77.25, dist:7.25}, Krp={ prox: 150.963, med: 70.25, dist: 77.25 }, #KaF = {prox:3214, med: 571, dist: 314}, #KaF = {prox:1157, med:500, dist:200}, KaF={ prox: 600, med: 500, dist: 100 }, KaS={ prox: 404.7, med: 35.2, dist: 0 }, Kir={ prox: 9.4644, med: 9.4644, dist: 9.4644 }, CaL13={ prox: 12 * ghKluge, med: 5.6 * ghKluge, dist: 5.6 * ghKluge }, CaL12={ prox: 8 * ghKluge, med: 4 * ghKluge, dist: 4 * ghKluge }, CaR={ prox: 20 * ghKluge, med: 45 * ghKluge, dist: 44 * ghKluge }, CaN={ prox: 4.0 * ghKluge, med: 0.0 * ghKluge, dist: 0.0 * ghKluge }, CaT={ prox: 0.0 * ghKluge, med: 1.9 * ghKluge, dist: 1.9 * ghKluge }, NaF={ prox: 130e3, med: 1894, dist: 927 }, SKCa={ prox: 0.5, med: 0.5, dist: 0.5 }, BKCa={ prox: 10.32, med: 10, dist: 10 }, )
from moose_nerp.prototypes import util as _util SpineParams = _util.NamedDict( 'SpineParams', spineDensity= 0, #Actual, experimentally reported/estimated spine density, in spines/meter, used to compensate for spines when spines not explicitly modeled; should make this distance dependent necklen=0.3e-6, #define all these parameters elsewhere neckdia=0.1e-6, headdia=0.5e-6, headlen=0.5e-6, headRA=4 * 4, #additional factor of 4 due to exptl higher than expected Ra Spine neckRA=11.3, spineRM=2.8, spineCM=0.01, spineELEAK=-50e-3, spineEREST=-80e-3, spineStart=26.1e-6, spineEnd=300e-6, explicitSpineDensity= 0, #Density of spines (per meter dendritic length) to explicitly model, < or = to spineDensity spineChanList=[], #['CaL13'] spineParent='soma', )
from . import param_cond from moose_nerp.prototypes import util as _util SpineParams = _util.NamedDict( 'SpineParams', spineDensity=0.1e6, #should make this distance dependent necklen=0.3e-6, #define all these parameters elsewhere neckdia=0.1e-6, headdia=0.5e-6, headlen=0.5e-6, headRA=1.25, neckRA=4 * 4, #additional factor of 4 due to exptl higher than expected Ra Spine spineRM=2.8, spineCM=0.01, spineELEAK=-50e-3, spineEREST=-80e-3, spineStart=26.1e-6, spineEnd=300e-6, compensationSpineDensity=0, #add spines to dendrites connected to /downstream of this branch. spineParent='31_4', spineChanList=[], #['CaL13'] )
morph_file = {'FSI': 'fs_morph.p'} # old_version. #CONDUCTANCES - UNITS of Siemens/meter squared _FSI = _util.NamedDict( 'FSI', Ka={ prox: 500, med: 90, dist: 0 }, Kv13={ prox: 1460, med: 0, dist: 0 }, Kv3132={ prox: 582, med: 0, dist: 0 }, NaF={ prox: 1149, med: 0, dist: 0 }, ) Condset = _util.NamedDict( 'Condset', FSI=_FSI, )
_D1 = _util.NamedDict( 'D1', Krp={ prox: 0.05006551249741205, med: 0.05006551249741205, dist: 0.05006551249741205 }, KaF={ prox: 4389.470290538669, med: 749.8955695196053, dist: 28.14751553099997 }, KaS={ prox: 475.0972939473723, med: 597.1440354876032, dist: 76.32524179163596 }, Kir={ prox: 5.933189483909874, med: 5.933189483909874, dist: 5.933189483909874 }, CaL13={ prox: 9.231777352086931 * ghKluge, med: 2.4013133071400934 * ghKluge, dist: 0.6803723796672949 * ghKluge }, CaL12={ prox: 0.3670566089203581 * ghKluge, med: 3.9112224494974255 * ghKluge, dist: 2.3162095058181715 * ghKluge }, CaR={ prox: 6.020952615897578 * ghKluge, med: 9.738386461210768 * ghKluge, dist: 17.31893386016392 * ghKluge }, CaN={ prox: 9.033232913044772 * ghKluge, med: 0.0 * ghKluge, dist: 0.0 * ghKluge }, CaT33={ prox: 0.0 * ghKluge, med: .1 * 0.007721704132985384 * ghKluge, dist: .1 * 0.4371369995209336 * ghKluge }, CaT32={ prox: 0.0 * ghKluge, med: 0.007721704132985384 * ghKluge, dist: 0.4371369995209336 * ghKluge }, NaF={ prox: 20264.011048395758, med: 35054.87847330811, dist: 2795.7343482062784 }, SKCa={ prox: 0.6810806557420438, med: 0.6810806557420438, dist: 0.6810806557420438 }, BKCa={ prox: 12.648119595394444, med: 12.648119595394444, dist: 12.648119595394444 }, CaCC={ prox: 1.0887331920472634, med: 1.0887331920472634, dist: 1.0887331920472634 }, )
#CONDUCTANCES # helper variables to index the Conductance and synapses with distance #axon cylindrical so x,y=0,0, 1e-6 means parent compartment only for spherical. prox = (0, 1e-3) # Length Range of the proximal dendrite from center of soma. dist = (1e-3, 100e-3) # Channel conductances declaration for the squid axon. _squid = _util.NamedDict( 'squid', K={ prox: 360, dist: 560 }, # Potassium channel g_bar to the squid axon surface. Na={ prox: 1200, dist: 1000 }, # Sodium channel g_bar to the squid axon surface. Krp={ prox: 1, dist: 3 }, SKCa={ prox: 1, dist: 3 }) # Channel conductances Condset = _util.NamedDict( 'Condset', squid=_squid, )
parameter. This can be bypassed by setting spineDensity = 0. Spines can also be explicitly modeled at the density specified by explicitSpineDensity (which at this point should be a value, not a callable). Spines are only explicitly modeled on branches that are children of spineParent. This will only be done if the spinesYN option is set to True (e.g. by --spines 1 from command line argument). ''' from moose_nerp.prototypes import util as _util SpineParams = _util.NamedDict( 'SpineParams', spineDensity = 0, #Actual, experimentally reported/estimated spine density, in spines/meter, necklen = 0.3e-6, #define all these parameters elsewhere neckdia = 0.1e-6, headdia = 0.5e-6, headlen = 0.5e-6, headRA = 4, neckRA = 11.3, spineRM = 2.8, spineCM = 0.01, spineELEAK = -50e-3, spineEREST = -80e-3, spineStart = 26.1e-6, spineEnd = 300e-6, explicitSpineDensity = 0, #Density of spines (per meter dendritic length) to explicitly model, < or = to spineDensity spineChanList = [], spineParent = 'soma', )
ghKluge = 1 #using 0.035e-9 makes NMDA calcium way too small, using single Tau calcium ConcOut = 2e-3 # mM, default for GHK is 2e-3 Temp = 30 # Celsius, needed for GHK objects, some channels neurontypes = None #can use different morphologies for different neuron types morph_file = {'CA1': 'out_ri04_v3.p'} NAME_SOMA = 'soma' #CONDUCTANCES # helper variables to index the Conductance and synapses with distance # UNITS: meters inclu = (0, 1000e-6) #CONDUCTANCE VALUES - UNITS of Siemens/meter squared _CA1 = _util.NamedDict( 'CA1', Kdr={inclu: 70.0}, Kadist={inclu: 200.0}, Kaprox={inclu: 200.0}, Na={inclu: 140.0}, ) Condset = _util.NamedDict( 'Condset', CA1=_CA1, )
# helper variables to index the Conductance and synapses with distance # UNITS: meters #soma spherical so x,y=0,0, 1e-6 means prox=soma prox = (0,20e-6) #med = (0,50e-6) dist = (1e-6, 1000e-6) axon = (0.,1., 'axon') #If using swc files for morphology, specify structure using: _1 as soma, _2 as apical dend, _3 as basal dend and _4 as axon #CONDUCTANCE VALUES - UNITS of Siemens/meter squared # _proto for prototypical GP neuron _proto = _util.NamedDict( 'proto', KDr={prox: 27.343138967696184}, Kv3={prox: 977.0650830330653}, KvF={prox: 228.49240134488954}, KvS={prox: 87.48247275990892}, NaF={prox: 3285.69063083201}, HCN2={prox: 4.130672163441272}, ) _arky = _util.NamedDict( 'arky', KDr={prox: 708.11742766709327}, Kv3={prox: 536.5656407446413}, KvF={prox: 1.74023152805182}, KvS={prox: 48.004268194532763626}, NaF={prox: 4321.968478081542}, HCN2={prox: 3.5734741500058576}, )