_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},
)
