tau2=37.5e-3,
Gbar=2e-9,
var=0.05,
MgBlock=_NMDA_MgParams,
spinic=True,
NMDA=True,
nmdaCaFrac=0.05,
)
#nmdaCaFra fraction of nmda current carried by calcium
#Note that since Ca reversal produces ~2x driving potential,
#need to make this half of typical value. Default is 0.02 in Moose
SYNAPSE_TYPES = NamedDict(
'SYNAPSE_TYPES',
ampa=_SynAMPA,
gaba=_SynGaba,
nmda=_SynNMDA,
)
DesensitizationParams = NamedDict('Synapse_desensitization',
gaba=None,
nmda=None,
ampa=DesensitizationParams(
dep_per_spike=0.39, dep_tau=0.8))
#These will be used by synconn in connect.py, since AMPA and NMDA synapses usually go together
#for same reason, the next lines only list ampa and gaba, and nmda are created the same as ampa
NAME_AMPA = 'ampa'
NAME_NMDA = 'nmda'
# number of synapses at each distance
_gaba = {param_cond.prox: 3, param_cond.med: 2, param_cond.dist: 1}
#Buffer params
calbindin = BufferParams('Calbindin', kf=0.028e6, kb=19.6, D=66e-12)
camc = BufferParams('CaMC', kf=0.006e6, kb=9.1, D=66.0e-12)
camn = BufferParams('CaMN', kf=0.1e6, kb=1000., D=66.0e-12)
fixed_buffer = BufferParams('Fixed_Buffer', kf=0.4e6, kb=20e3, D=0)
Fura2 = BufferParams('Fura-2', kf=1000e3, kb=185, D=6e-11)
Fluo5F = BufferParams('Fluo5f_Wickens', kf=2.36e5, kb=82.6, D=6e-11)
Fluo4 = BufferParams('Fluo4', kf=2.36e5, kb=82.6, D=6e-11)
Fluo4FF = BufferParams('Fluo4FF', kf=.8e5, kb=776, D=6e-11)
#Buffer params dictionary
BufferParams = NamedDict('BufferParams',
Calbindin=calbindin,
CaMN=camn,
CaMC=camc,
FixedBuffer=fixed_buffer,
Fura2=Fura2,
Fluo5F=Fluo5F,
Fluo4=Fluo4,
Flou4FF=Fluo4FF)
#Pump params
MMPump = PumpParams('MMpump', Kd=0.3e-3)
NCX = PumpParams("NCX", Kd=1e-3)
#Pump params dictionary
PumpKm = {'MMPump': MMPump, 'NCX': NCX}
#dye used in simulations
#possible dye sets used in experiments
Channels = NamedDict(
'Channels',
Krp=TypicalOneD(Krpparam, Krp_X_params, Krp_Y_params),
KaF=TypicalOneD(KaFparam, KaF_X_params, KaF_Y_params),
KaS=TypicalOneD(KaSparam, KaS_X_params, KaS_Y_params),
Kir=TypicalOneD(Kirparam, Kir_X_params, []),
CaL12=TypicalOneD(CaL12param,
CaL12_X_params,
CaL12_Y_params,
CDI_Z_params,
calciumPermeable=True,
calciumDependent=True),
CaL13=TypicalOneD(CaL13param,
CaL13_X_params,
CaL13_Y_params,
CDI_Z_params,
calciumPermeable=True,
calciumDependent=True),
CaN=TypicalOneD(CaNparam,
CaN_X_params,
CaN_Y_params,
CDI_Z_params,
calciumPermeable=True,
calciumDependent=True),
CaR=TypicalOneD(CaRparam,
CaR_X_params,
CaR_Y_params,
CDI_Z_params,
calciumPermeable=True,
calciumDependent=True),
CaT=TypicalOneD(CaTparam,
CaT_X_params,
CaT_Y_params, [],
calciumPermeable=True),
CaT32=TypicalOneD(CaT32param,
CaT32_X_params,
CaT32_Y_params, [],
calciumPermeable=True),
CaT33=TypicalOneD(CaT33param,
CaT33_X_params,
CaT33_Y_params, [],
calciumPermeable=True),
SKCa=TypicalOneD(SKparam, [], [], SK_Z_params, calciumDependent=True),
NaF=TypicalOneD(NaFparam, Na_m_params, Na_h_params),
BKCa=TwoD(BKparam, BK_X_params, calciumDependent=True),
CaCC=TypicalOneD(CaCCparam, [], [], CaCC_Z_params, calciumDependent=True),
)
tau2=30e-3,
Gbar=0.252e-9,
var=0.05,
MgBlock=_NMDA_MgParams,
spinic=True,
NMDA=True,
nmdaCaFrac=0.05
)
# nmdaCaFra fraction of nmda current carried by calcium
# Note that since Ca reversal produces ~2x driving potential,
# need to make this half of typical value. Default is 0.02 in Moose
SYNAPSE_TYPES = NamedDict(
'SYNAPSE_TYPES',
ampa=_SynAMPA,
gaba=_SynGaba,
nmda=_SynNMDA,
)
# These will be used by synconn in connect.py, since AMPA and NMDA synapses usually go together
# for same reason, the next lines only list ampa and gaba, and nmda are created the same as ampa
NAME_AMPA = 'ampa'
NAME_NMDA = 'nmda'
# number of synapses at each distance
_gaba = {param_cond.prox: 3, param_cond.dist: 1}
_ampa = {param_cond.prox: 1, param_cond.dist: 3}
NumSyn = {'gaba': _gaba,
'ampa': _ampa}
A_C=-1,
A_vhalf=30.013e-3,
A_vslope=-7.2e-3,
B_rate=3.722e-3 * 2.14e6,
B_B=0.124 * 2.14e6,
B_C=-1,
B_vhalf=30.013e-3,
B_vslope=7.2e-3)
Na_h_params = AlphaBetaChannelParams(
A_rate=(45.013e-3 + 15.0e-3) * 0.03 * 2.14e6,
A_B=0.03 * 2.14e6,
A_C=-1,
A_vhalf=45.013e-3 + 15e-3,
A_vslope=3.5e-3,
B_rate=-(45.013e-3 + 15e-3) * 0.01 * 2.14e6,
B_B=-0.01 * 2.14e6,
B_C=-1,
B_vhalf=45.013e-3 + 15e-3,
B_vslope=-3.5e-3)
naFparams = ChannelSettings(Xpow=3, Ypow=1, Zpow=0, Erev=55.0e-3, name='NaF')
Channels = NamedDict(
'Channels',
Kdr=TypicalOneD(kDrparams, kDr_X_params, kDr_Y_params),
Kadist=TypicalOneD(KAdistparams, KAdist_m_params, KAdist_h_params),
Kaprox=TypicalOneD(KAproxparams, KAprox_m_params, KAdist_h_params),
Na=TypicalOneD(naFparams, Na_m_params, Na_h_params),
)
B_vhalf=0.0,
B_vslope=40e-3)
Krp_Y_params = TauInfMinChannelParams(T_min=0.287,
T_vdep=4.16,
T_vhalf=-0.042,
T_vslope=0.013,
SS_min=0.13,
SS_vdep=0.87,
SS_vhalf=-0.056,
SS_vslope=0.014)
Krpparam = ChannelSettings(Xpow=2, Ypow=1, Zpow=0, Erev=krev, name='Krp')
SK_Z_params = ZChannelParams(Kd=0.00035,
power=4.6,
tau=0.002,
taumax=0.0037928,
tau_power=4.3,
cahalf=0.002703)
SKparam = ChannelSettings(Xpow=0, Ypow=0, Zpow=1, Erev=krev, name='SKCa')
Channels = NamedDict('Channels',
Na=TypicalOneD(NaSparam, Na_m_params, Na_h_params),
K=TypicalOneD(KSparam, K_n_params, None),
Krp=TypicalOneD(Krpparam, Krp_X_params, Krp_Y_params),
SKCa=TypicalOneD(SKparam, [], [],
SK_Z_params,
calciumDependent=True))
# Tau is used only if using single time constant of decay instead of pumps and buffers
CaBasal = 50e-6
# much slower tau decay to better match Korngreen data
CaTau = 20e-3 # 0.001 sec used in Jadegers model
CalciumParams = CellCalcium(CaName='Shell', Ceq=CaBasal, DCa=200e-12, tau=CaTau)
BufCapacity = 20 # 20e-3 # use 1 from Jaegers model to match the value of B (B=1/Z.F.V.KB)
# 10 implies that 90% of calcium immediately binds to buffer
CaThick = 0.020e-6 # 0.02 microns
# Buffer capacity specification -- this is used with CaConc (single time constant of Ca decay)
BufferCapacityDensity = {soma: BufCapacity, dend: BufCapacity}
#################################
BufferParams = NamedDict('BufferParams') # Buffer params dictionary
# kinetic parameters of various calcium Buffers / indicators
# Calbindin binding rates from Schmidt et al. 2007 J Physiol
BufferParams.Calbindin = SingleBufferParams('Calbindin', kf=0.028e6, kb=19.6, D=66e-12)
# Calmodulin binding rates from Brown SE, Martin SR, Bayley PM (1997) J Biol Chem and Putkey JA, Kleerekoper Q, Gaertner TR, Waxham MN (2003) J Biol Chem
BufferParams.CaMC = SingleBufferParams('CaMC', kf=0.006e6, kb=9.1, D=66.0e-12)
BufferParams.CaMN = SingleBufferParams('CaMN', kf=0.1e6, kb=1000., D=66.0e-12)
BufferParams.FixedBuffer = SingleBufferParams('Fixed_Buffer', kf=0.4e6, kb=20e3, D=0)
BufferParams.Fura2 = SingleBufferParams('Fura-2', kf=1000e3, kb=185, D=6e-11)
BufferParams.Fluo5F = SingleBufferParams('Fluo5f_Wickens', kf=2.36e5, kb=82.6, D=6e-11)
BufferParams.Fluo4 = SingleBufferParams('Fluo4', kf=2.36e5, kb=82.6, D=6e-11)
BufferParams.Fluo4FF = SingleBufferParams('Fluo4FF', kf=.8e5, kb=776, D=6e-11)
####################################################
# Kinetic parameters for calcium extrusion (pump) mechanisms
PumpParams = NamedDict('PumpParams') # Buffer params dictionary
B_B=0,
B_C=1,
B_vhalf=0.03,
B_vslope=-0.012)
HCN1_Y_params = []
HCN2param = ChannelSettings(Xpow=1, Ypow=0, Zpow=0, Erev=hcnrev, name='HCN2')
HCN2_X_params = AlphaBetaChannelParams(A_rate=10.872436855994172,
A_B=0.0,
A_C=1,
A_vhalf=0.0775131813908781,
A_vslope=0.01,
B_rate=7.248291237329447,
B_B=0.0,
B_C=1,
B_vhalf=-0.0424868186091219,
B_vslope=-0.012)
HCN2_Y_params = []
Channels = NamedDict(
'Channels',
KDr=TypicalOneD(KDrparam, KDr_X_params, KDr_Y_params),
Kv3=TypicalOneD(Kv3param, Kv3_X_params, Kv3_Y_params),
KvF=TypicalOneD(KvFparam, KvF_X_params, KvF_Y_params),
KvS=TypicalOneD(KvSparam, KvS_X_params, KvS_Y_params),
HCN1=TypicalOneD(HCN1param, HCN1_X_params, []),
HCN2=TypicalOneD(HCN2param, HCN2_X_params, []),
NaF=TypicalOneD(NaFparam, Na_m_params, Na_h_params),
)
B_vslope=80e-3)
KSparam = ChannelSettings(Xpow=4, Ypow=0, Zpow=0, Erev=krev, name='K')
Krp_X_params = AlphaBetaChannelParams(A_rate=16,
A_B=0,
A_C=0.0,
A_vhalf=0,
A_vslope=-20e-3,
B_rate=2.4,
B_B=0.0,
B_C=0.0,
B_vhalf=0.0,
B_vslope=40e-3)
Krp_Y_params = TauInfMinChannelParams(T_min=0.287,
T_vdep=4.16,
T_vhalf=-0.042,
T_vslope=0.013,
SS_min=0.13,
SS_vdep=0.87,
SS_vhalf=-0.056,
SS_vslope=0.014)
Krpparam = ChannelSettings(Xpow=2, Ypow=1, Zpow=0, Erev=krev, name='Krp')
Channels = NamedDict('Channels',
Na=TypicalOneD(NaSparam, Na_m_params, Na_h_params),
K=TypicalOneD(KSparam, K_n_params, None),
Krp=TypicalOneD(Krpparam, Krp_X_params, Krp_Y_params))
Ka_X_params = TauInfMinChannelParams(T_min=1e-3,
T_vdep=1e-3,
T_vhalf=0.07,
T_vslope=-0.013,
SS_min=0,
SS_vdep=1,
SS_vhalf=-0.045,
SS_vslope=-0.013)
Ka_Y_params = TauInfMinChannelParams(T_min=0.014,
T_vdep=0,
T_vhalf=37.0e-3,
T_vslope=5.0e-3,
SS_min=0,
SS_vdep=1,
SS_vhalf=-0.077,
SS_vslope=0.008)
Kaparam = ChannelSettings(Xpow=4, Ypow=1, Zpow=0, Erev=carev, name='Ka')
#Dictionary of "standard" channels, to create channels using a loop
#NaF doesn't fit since it uses different prototype form
#will need separate dictionary for BK
Channels = NamedDict(
'Channels',
Ka=TypicalOneD(Kaparam, Ka_X_params, Ka_Y_params),
Kv13=TypicalOneD(Kv13param, Kv13_X_params, []),
Kv3132=TypicalOneD(Kv3132param, Kv3132_X_params, []),
NaF=TypicalOneD(NaFparam, NaF_X_params, NaF_Y_params),
)
SKparam = ChannelSettings(Xpow=0, Ypow=0, Zpow=1, Erev=krev, name='SKCa')
SK_Z_params = ZChannelParams(Kd=0.00035,
power=4.6,
tau=0.002,
taumax=0.0037928,
tau_power=4.3,
cahalf=0.002703)
#BK channel
BKparam = ChannelSettings(Xpow=1, Ypow=0, Zpow=0, Erev=krev, name='BKCa')
BK_X_params = [
BKChannelParams(alphabeta=480, K=0.18, delta=-0.84),
BKChannelParams(alphabeta=280, K=0.011, delta=-1.0)
]
Channels = NamedDict(
'Channels',
KDr=TypicalOneD(KDrparam, KDr_X_params, KDr_Y_params),
Kv3=TypicalOneD(Kv3param, Kv3_X_params, Kv3_Y_params),
#KvF = TypicalOneD(KvFparam, KvF_X_params, KvF_Y_params),
KvS=TypicalOneD(KvSparam, KvS_X_params, KvS_Y_params),
HCN1=TypicalOneD(HCN1param, HCN1_X_params, []),
HCN2=TypicalOneD(HCN2param, HCN2_X_params, []),
NaF=TypicalOneD(NaFparam, Na_m_params, Na_h_params, Na_s_params),
NaS=TypicalOneD(NaSparam, NaS_m_params, NaS_h_params),
Ca=TypicalOneD(Caparam, Ca_X_params, [], [], calciumPermeable=True),
SKCa=TypicalOneD(SKparam, [], [], SK_Z_params, calciumDependent=True),
BKCa=TwoD(BKparam, BK_X_params, calciumDependent=True),
)
# have to add NaP and calcium channels
GEOMETRIC = 1
LINEAR = 0
#SPECIFY TYPE OF CALCIUM DYNAMICS HERE
#CaShellModeDensity = {soma:SHELL, dend:SHELL, spines:SLAB}
CaShellModeDensity = {soma:CAPOOL, dend:CAPOOL, spines:CAPOOL} #TODO: #FIXME: segfault when dend uses capool and spines are included
############################################
#intrinsic calcium params
#diffusion constant from Allbritton et al.1992.
# Tau is used only if using single time constant of decay instead of pumps and buffers
CaBasal = 50e-6
CalciumParams = CellCalcium(CaName='Shell',Ceq=CaBasal,DCa=200e-12,tau=20e-3)
#################################
BufferParams = NamedDict('BufferParams') # Buffer params dictionary
#kinetic parameters of various calcium Buffers / indicators
#Calbindin binding rates from Schmidt et al. 2007 J Physiol
BufferParams.Calbindin = SingleBufferParams('Calbindin', kf=0.028e6, kb=19.6, D=66e-12)
#Calmodulin binding rates from Brown SE, Martin SR, Bayley PM (1997) J Biol Chem and Putkey JA, Kleerekoper Q, Gaertner TR, Waxham MN (2003) J Biol Chem
BufferParams.CaMC = SingleBufferParams('CaMC', kf=0.006e6, kb=9.1, D=66.0e-12)
BufferParams.CaMN = SingleBufferParams('CaMN', kf=0.1e6, kb=1000., D=66.0e-12)
BufferParams.FixedBuffer = SingleBufferParams('Fixed_Buffer', kf=0.4e6, kb=20e3, D=0)
BufferParams.Fura2 = SingleBufferParams('Fura-2', kf=1000e3, kb=185, D=6e-11)
BufferParams.Fluo5F = SingleBufferParams('Fluo5f_Wickens', kf=2.36e5, kb=82.6, D=6e-11)
BufferParams.Fluo4 = SingleBufferParams('Fluo4', kf=2.36e5, kb=82.6, D=6e-11)
BufferParams.Fluo4FF = SingleBufferParams('Fluo4FF', kf=.8e5, kb=776, D=6e-11)
####################################################
#Kinetic parameters for calcium extrusion (pump) mechanisms
PumpParams = NamedDict('PumpParams') # Buffer params dictionary
A_vslope=17e-3,
B_rate=20,
B_B=0,
B_C=0.0,
B_vhalf=0.0,
B_vslope=-30.0e-3)
#Dictionary of "standard" channels, to create channels using a loop
#NaF doesn't fit since it uses different prototype form
#will need separate dictionary for BK
Channels = NamedDict(
'Channels',
KaF=TypicalOneD(KaFparam, KaF_X_params, KaF_Y_params),
KaS=TypicalOneD(KaSparam, KaS_X_params, KaS_Y_params),
Kir=TypicalOneD(Kirparam, Kir_X_params, []),
CaL12=TypicalOneD(CaL12param,
CaL12_X_params,
CaL12_Y_params,
calciumPermeable=True),
CaL13=TypicalOneD(CaL13param,
CaL13_X_params,
CaL13_Y_params,
calciumPermeable=True),
CaR=TypicalOneD(CaRparam,
CaR_X_params,
CaR_Y_params,
calciumPermeable=True),
NaF=TypicalOneD(NaFparam, Na_m_params, Na_h_params),
)
B_vslope=22.222e-3)
Kv3132param = ChannelSettings(Xpow=2, Ypow=0, Zpow=0, Erev=krev, name='Kv3132')
Ka_X_params = TauInfMinChannelParams(T_min=1e-3,
T_vdep=1e-3,
T_vhalf=0.07,
T_vslope=-0.013,
SS_min=0,
SS_vdep=1,
SS_vhalf=-0.045,
SS_vslope=-0.013)
Ka_Y_params = TauInfMinChannelParams(T_min=0.014,
T_vdep=0,
T_vhalf=37.0e-3,
T_vslope=5.0e-3,
SS_min=0,
SS_vdep=1,
SS_vhalf=-0.077,
SS_vslope=0.008)
Kaparam = ChannelSettings(Xpow=4, Ypow=1, Zpow=0, Erev=carev, name='Ka')
Channels = NamedDict(
'Channels',
KvF=TypicalOneD(KvFparam, KvF_X_params, KvF_Y_params),
KvS=TypicalOneD(KvSparam, KvS_X_params, KvS_Y_params),
NaF=TypicalOneD(NaFparam, NaF_X_params, NaF_Y_params),
Kv3132=TypicalOneD(Kv3132param, Kv3132_X_params, []),
)
