def lson(lsoInputSpkFileTuple, temp_degC=37):
defaultclock.dt = 0.02 * ms # for better precision
neuron_type = 'type2' # medium-fast membrane f0 180-260Hz, CF4kHz
#temp_degC=37.
Vrest = -63.6 * mV # resting potential for type1c from RM2003
nLsons = 1 # number of LSO neurons
nGbcsCo = 4
# nGbcsIp = 4
# nSbcsCo = 4
nSbcsIp = 4
# nSbcs = 4
# nAnfsPerSbc = 3
# nGbcs = 4
# nAnfsPerGbc=30
nAnfsPerInputFile = 40
nGbcsCoPerLson = 8 # Gjoni et al. 2018
nSbcsIpPerLson = 40 # Gjoni et al. 2018
# sbCoSpkFile = inputSpkFileTuple[0]
# sbIpSpkFile = lsoInputSpkFileTuple[1]
# gbCoSpkFile = lsoInputSpkFileTuple[2]
# gbIpSpkFile = inputSpkFileTuple[3]
anCoSpkFile = lsoInputSpkFileTuple[0]
anIpSpkFile = lsoInputSpkFileTuple[1]
# sbCoIdxSpktimeArray = np.loadtxt(sbCoSpkFile)
# sbCoCellIndices = sbCoIdxSpktimeArray[:, 0].astype(int)
# sbCoSpkTimes = sbCoIdxSpktimeArray[:, 1] * ms
# sbCoSpkGenGrp = SpikeGeneratorGroup(nSbcsCo, sbCoCellIndices, sbCoSpkTimes)
gbCoIdxSpktimeArray = np.loadtxt(anCoSpkFile)
gbCoCellIndices = gbCoIdxSpktimeArray[:, 0].astype(int)
# For now, spiketimes from Zilany AN in SECONDS, so * 1000*ms
gbCoSpkTimes = gbCoIdxSpktimeArray[:, 1] * 1000. * ms
gbCoSpkGenGrp = SpikeGeneratorGroup(nAnfsPerInputFile, gbCoCellIndices,
gbCoSpkTimes)
sbIpIdxSpktimeArray = np.loadtxt(anIpSpkFile)
sbIpCellIndices = sbIpIdxSpktimeArray[:, 0].astype(int)
# For now, spiketimes from Zilany AN in SECONDS, so * 1000*ms
sbIpSpkTimes = sbIpIdxSpktimeArray[:, 1] * 1000. * ms
sbIpSpkGenGrp = SpikeGeneratorGroup(nAnfsPerInputFile, sbIpCellIndices,
sbIpSpkTimes)
# gbIpIdxSpktimeArray = np.loadtxt(gbIpSpkFile)
# gbIpCellIndices = gbIpIdxSpktimeArray[:, 0].astype(int)
# gbIpSpkTimes = gbIpIdxSpktimeArray[:, 1] * ms
# gbIpSpkGenGrp = SpikeGeneratorGroup(nGbcsIp, gbIpCellIndices, gbIpSpkTimes)
# anfIdxSpktimeArray = np.loadtxt(anfSpkFile)
# anfIndices = anfIdxSpktimeArray[:, 0].astype(int)
# nANF = 132
# #anfSpkTimes = [i * second for i in anfIdxSpktimeArray[:, 1]]
# anfSpkTimes = anfIdxSpktimeArray[:, 1] * 1000*ms
# anfSpkGeneratorGrp = SpikeGeneratorGroup(nANF, anfIndices, anfSpkTimes)
# Membrane and Ion-Channel parameters
C = 12 * pF
EH = -43 * mV
EK = -70 * mV # -77*mV in mod file
EL = -65 * mV
ENa = 55 * mV # 55*mV in RM2003; 50*mv by Brette
nf = 0.85 # proportion of n vs p kinetics
zss = 0.5 # steady state inactivation of glt
# default temp_degC = 37., human body temperature in degree celcius
# q10 for ion-channel time-constants (RM2003, p.3106):
q10 = 3.**((temp_degC - 22.) / 10.)
# q10 for ion-channel gbar parameters (RM2003, p.3106):
q10gbar = 2.**((temp_degC - 22.) / 10.)
# hcno current (octopus cell)
frac = 0.0
qt = 4.5**((temp_degC - 33.) / 10.)
# Synaptic parameters:
Es_e = 0. * mV
tausE = 0.5 * ms
Es_i = -90 * mV
tausI = 1.0 * ms
'''Synaptic weights are unitless according to Brian2.
The effective unit is siemens, so they can work in amp, volt, siemens eqns.
We multiply synaptic weight w_e by unit siemens when adding it to g_e.
We use a local variable w_e for synaptic weight rather than the standard w:'''
w_elson = 5e-9
w_ilson = 50e-9 # 6e-9 @ 200Hz; 12e-9 @ 600 Hz
'''Here's why:
The synapses sbc3SynE.w synaptic weight references the Same State Variable as
as the neuron group sbc3Grp.w (klt activation w).
Update sbc3Grp.w, and you set sbc3SynE.w to same value, and vice versa.
This way klt activation and synaptic weight are identical and quite ridiculous.
So use a local variable other than w for the synaptic weight!'''
# Maximal conductances of different cell types in nS
maximal_conductances = dict(
type1c=(1000, 150, 0, 0, 0.5, 0, 2),
type1t=(1000, 80, 0, 65, 0.5, 0, 2),
type12=(1000, 150, 20, 0, 2, 0, 2),
type21=(1000, 150, 35, 0, 3.5, 0, 2),
type2=(1000, 150, 200, 0, 20, 0, 2),
type2g1p5x=(1000, 150, 300, 0, 30, 0, 2),
type2g0p5x=(1000, 150, 100, 0, 10, 0, 2),
type2o=(1000, 150, 600, 0, 0, 40, 2) # octopus cell
)
gnabar, gkhtbar, gkltbar, gkabar, ghbar, gbarno, gl = [
x * nS for x in maximal_conductances[neuron_type]
]
# Classical Na channel
eqs_na = """
ina = gnabar*m**3*h*(ENa-v) : amp
dm/dt=q10*(minf-m)/mtau : 1
dh/dt=q10*(hinf-h)/htau : 1
minf = 1./(1+exp(-(vu + 38.) / 7.)) : 1
hinf = 1./(1+exp((vu + 65.) / 6.)) : 1
mtau = ((10. / (5*exp((vu+60.) / 18.) + 36.*exp(-(vu+60.) / 25.))) + 0.04)*ms : second
htau = ((100. / (7*exp((vu+60.) / 11.) + 10.*exp(-(vu+60.) / 25.))) + 0.6)*ms : second
"""
# KHT channel (delayed-rectifier K+)
eqs_kht = """
ikht = gkhtbar*(nf*n**2 + (1-nf)*p)*(EK-v) : amp
dn/dt=q10*(ninf-n)/ntau : 1
dp/dt=q10*(pinf-p)/ptau : 1
ninf = (1 + exp(-(vu + 15) / 5.))**-0.5 : 1
pinf = 1. / (1 + exp(-(vu + 23) / 6.)) : 1
ntau = ((100. / (11*exp((vu+60) / 24.) + 21*exp(-(vu+60) / 23.))) + 0.7)*ms : second
ptau = ((100. / (4*exp((vu+60) / 32.) + 5*exp(-(vu+60) / 22.))) + 5)*ms : second
"""
# Ih channel (subthreshold adaptive, non-inactivating)
eqs_ih = """
ih = ghbar*r*(EH-v) : amp
dr/dt=q10*(rinf-r)/rtau : 1
rinf = 1. / (1+exp((vu + 76.) / 7.)) : 1
rtau = ((100000. / (237.*exp((vu+60.) / 12.) + 17.*exp(-(vu+60.) / 14.))) + 25.)*ms : second
"""
# KLT channel (low threshold K+)
eqs_klt = """
iklt = gkltbar*w**4*z*(EK-v) : amp
dw/dt=q10*(winf-w)/wtau : 1
dz/dt=q10*(zinf-z)/ztau : 1
winf = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25 : 1
zinf = zss + ((1.-zss) / (1 + exp((vu + 71.) / 10.))) : 1
wtau = ((100. / (6.*exp((vu+60.) / 6.) + 16.*exp(-(vu+60.) / 45.))) + 1.5)*ms : second
ztau = ((1000. / (exp((vu+60.) / 20.) + exp(-(vu+60.) / 8.))) + 50)*ms : second
"""
# Ka channel (transient K+)
eqs_ka = """
ika = gkabar*a**4*b*c*(EK-v): amp
da/dt=q10*(ainf-a)/atau : 1
db/dt=q10*(binf-b)/btau : 1
dc/dt=q10*(cinf-c)/ctau : 1
ainf = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25 : 1
binf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1
cinf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1
atau = ((100. / (7*exp((vu+60) / 14.) + 29*exp(-(vu+60) / 24.))) + 0.1)*ms : second
btau = ((1000. / (14*exp((vu+60) / 27.) + 29*exp(-(vu+60) / 24.))) + 1)*ms : second
ctau = ((90. / (1 + exp((-66-vu) / 17.))) + 10)*ms : second
"""
# Leak
eqs_leak = """
ileak = gl*(EL-v) : amp
"""
# h current for octopus cells
eqs_hcno = """
ihcno = gbarno*(h1*frac + h2*(1-frac))*(EH-v) : amp
dh1/dt=(hinfno-h1)/tau1 : 1
dh2/dt=(hinfno-h2)/tau2 : 1
hinfno = 1./(1+exp((vu+66.)/7.)) : 1
tau1 = bet1/(qt*0.008*(1+alp1))*ms : second
tau2 = bet2/(qt*0.0029*(1+alp2))*ms : second
alp1 = exp(1e-3*3*(vu+50)*9.648e4/(8.315*(273.16+temp_degC))) : 1
bet1 = exp(1e-3*3*0.3*(vu+50)*9.648e4/(8.315*(273.16+temp_degC))) : 1
alp2 = exp(1e-3*3*(vu+84)*9.648e4/(8.315*(273.16+temp_degC))) : 1
bet2 = exp(1e-3*3*0.6*(vu+84)*9.648e4/(8.315*(273.16+temp_degC))) : 1
"""
eqs = """
dv/dt = (ileak + ina + ikht + iklt + ika + ih + ihcno + I + Is_e + Is_i)/C : volt
Is_e = gs_e * (Es_e - v) : amp
gs_e : siemens
Is_i = gs_i * (Es_i - v) : amp
gs_i : siemens
vu = v/mV : 1 # unitless v
I : amp
"""
#Added Is_i to RM2003
eqs += eqs_leak + eqs_ka + eqs_na + eqs_ih + eqs_klt + eqs_kht + eqs_hcno
lsonGrp = NeuronGroup(nLsons,
eqs,
method='exponential_euler',
threshold='v > -30*mV',
refractory='v > -45*mV')
#gbcGrp.I = 2500.0*pA
lsonGrp.I = 0.0 * pA
# Initialize model near v_rest with no inputs
lsonGrp.v = Vrest
#vu = EL/mV # unitless v
vu = lsonGrp.v / mV # unitless v
lsonGrp.m = 1. / (1 + exp(-(vu + 38.) / 7.))
lsonGrp.h = 1. / (1 + exp((vu + 65.) / 6.))
lsonGrp.n = (1 + exp(-(vu + 15) / 5.))**-0.5
lsonGrp.p = 1. / (1 + exp(-(vu + 23) / 6.))
lsonGrp.r = 1. / (1 + exp((vu + 76.) / 7.))
lsonGrp.w = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25
lsonGrp.z = zss + ((1. - zss) / (1 + exp((vu + 71.) / 10.)))
lsonGrp.a = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25
lsonGrp.b = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.c = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.h1 = 1. / (1 + exp((vu + 66.) / 7.))
lsonGrp.h2 = 1. / (1 + exp((vu + 66.) / 7.))
#lsonGrp.gs_e = 0.0*siemens
#netGbcEq = Network(gbcGrp, report='text')
#netGbcEq.run(50*ms, report='text')
lsonSynI = Synapses(gbCoSpkGenGrp,
lsonGrp,
model='''dg_i/dt = -g_i/tausI : siemens (clock-driven)
gs_i_post = g_i : siemens (summed)''',
on_pre='g_i += w_ilson*siemens',
method='exact')
lsonSynI.connect(i=np.arange(nGbcsCoPerLson), j=0)
lsonSynE = Synapses(sbIpSpkGenGrp,
lsonGrp,
model='''dg_e/dt = -g_e/tausE : siemens (clock-driven)
gs_e_post = g_e : siemens (summed)''',
on_pre='g_e += w_elson*siemens',
method='exact')
lsonSynE.connect(i=np.arange(nSbcsIpPerLson), j=0)
lsonSpks = SpikeMonitor(lsonGrp)
lsonState = StateMonitor(lsonGrp, ['v', 'gs_e'], record=True)
run(300 * ms, report='text')
# Console Output Won't Clear from Script
# Memory issue with so many repeated simulations:
# Comment out the plt commands
#plt.plot(lsonState.t / ms, lsonState[0].v / mV)
#plt.xlabel('t (ms)')
#plt.ylabel('v (mV)')
#plt.show()
# Output file - EIPD in output filename. Spiketimes in file
EPhsStrCo = anCoSpkFile[27:31]
EPhsStrIp = anIpSpkFile[27:31]
if (EPhsStrCo[0] == 'N'):
EPhsIntCo = -1 * int(EPhsStrCo[1:4])
else:
EPhsIntCo = int(EPhsStrCo[0:3])
if (EPhsStrIp[0] == 'N'):
EPhsIntIp = -1 * int(EPhsStrIp[1:4])
else:
EPhsIntIp = int(EPhsStrIp[0:3])
# EIPD = (EPhsIntCo - EPhsIntIp) % 360
EIPDint = (EPhsIntCo - EPhsIntIp) # unwrapped Envelope IPD
#EIPDstr = str(EIPDint)
if (EIPDint == 15):
EIPDstr = 'EIPDP015'
elif (EIPDint == 30):
EIPDstr = 'EIPDP030'
elif (EIPDint == 45):
EIPDstr = 'EIPDP045'
elif (EIPDint == 60):
EIPDstr = 'EIPDP060'
elif (EIPDint == 75):
EIPDstr = 'EIPDP075'
elif (EIPDint == 90):
EIPDstr = 'EIPDP090'
elif (EIPDint == -15):
EIPDstr = 'EIPDN015'
elif (EIPDint == -30):
EIPDstr = 'EIPDN030'
elif (EIPDint == -45):
EIPDstr = 'EIPDN045'
elif (EIPDint == -60):
EIPDstr = 'EIPDN060'
elif (EIPDint == -75):
EIPDstr = 'EIPDN075'
elif (EIPDint == -90):
EIPDstr = 'EIPDN090'
elif (EIPDint > 0):
EIPDstr = 'EIPDP' + str(EIPDint)
elif (EIPDint < 0):
EIPDstr = 'EIPDN' + str(-EIPDint)
elif (EIPDint == 0):
EIPDstr = 'EIPDP000'
# if (EIPDint < 0):
# EIPDstr = EIPDstr.replace('-','N')
# Synaptic parameters in output filename
if (abs(round(tausE / ms) - (tausE / ms)) < 0.1):
Te = str(round(tausE / ms))
else:
Te = str(tausE / ms)
Te = Te.replace('.', 'p')
if (abs(round(w_elson / 1e-9) - (w_elson / 1e-9)) < 0.1):
We = str(round(w_elson / 1e-9))
else:
We = str(w_elson / 1e-9)
We = We.replace('.', 'p')
if (abs(round(tausI / ms) - (tausI / ms)) < 0.1):
Ti = str(round(tausI / ms))
else:
Ti = str(tausI / ms)
Ti = Ti.replace('.', 'p')
if (abs(round(w_ilson / 1e-9) - (w_ilson / 1e-9)) < 0.1):
Wi = str(round(w_ilson / 1e-9))
else:
Wi = str(w_ilson / 1e-9)
Wi = Wi.replace('.', 'p')
lsonSpkFile = 'Lso2SpTms' + anCoSpkFile[6:13] + anCoSpkFile[
16:
23] + 'Te' + Te + 'We' + We + 'Ti' + Ti + 'Wi' + Wi + EIPDstr + 'Co' + anCoSpkFile[
38:40] + anCoSpkFile[23:31] + anCoSpkFile[45:]
file0 = open(lsonSpkFile, 'w')
for index in range(len(lsonSpks.t)):
file0.write(
str(lsonSpks.i[index]) + " " + str(lsonSpks.t[index] / ms) + '\n')
file0.close()
return (lsonGrp, lsonSpks, lsonState)
# end of mkgbcs
def lson(lsoInputSpkFileTuple, temp_degC=37):
defaultclock.dt = 0.02*ms # for better precision
neuron_type = 'LsoNp1a' # non-principal, moderately slow (typical)
Vrest = -64.1*mV # observed resting potential for model LsoNp1a
nLsons = 1 # number of LSO neurons
# As in Wang & Colburn (2012), the LSO model has simplified inputs.
# Spike times from model auditory nerve fibers represent inputs
# driven by the cochlear nucleus: contralateral inhibitory inputs
# driven by globular bushy cells via the MNTB, and ipsilateral
# excitatory inputs from spherical bushy cells.
nAnfsPerInputFile = 40
nGbcsCoPerLson = 8 # Gjoni et al. 2018
nSbcsIpPerLson = 40 # Gjoni et al. 2018
# sbCoSpkFile = inputSpkFileTuple[0]
# sbIpSpkFile = lsoInputSpkFileTuple[1]
# gbCoSpkFile = lsoInputSpkFileTuple[2]
# gbIpSpkFile = inputSpkFileTuple[3]
anCoSpkFile = lsoInputSpkFileTuple[0]
anIpSpkFile = lsoInputSpkFileTuple[1]
# sbCoIdxSpktimeArray = np.loadtxt(sbCoSpkFile)
# sbCoCellIndices = sbCoIdxSpktimeArray[:, 0].astype(int)
# sbCoSpkTimes = sbCoIdxSpktimeArray[:, 1] * ms
# sbCoSpkGenGrp = SpikeGeneratorGroup(nSbcsCo, sbCoCellIndices, sbCoSpkTimes)
gbCoIdxSpktimeArray = np.loadtxt(anCoSpkFile)
gbCoCellIndices = gbCoIdxSpktimeArray[:, 0].astype(int)
# For now, spiketimes from Zilany AN in SECONDS, so * 1000*ms
gbCoSpkTimes = gbCoIdxSpktimeArray[:, 1] * 1000. * ms
gbCoSpkGenGrp = SpikeGeneratorGroup(nAnfsPerInputFile, gbCoCellIndices, gbCoSpkTimes)
sbIpIdxSpktimeArray = np.loadtxt(anIpSpkFile)
sbIpCellIndices = sbIpIdxSpktimeArray[:, 0].astype(int)
# For now, spiketimes from Zilany AN in SECONDS, so * 1000*ms
sbIpSpkTimes = sbIpIdxSpktimeArray[:, 1] * 1000. * ms
sbIpSpkGenGrp = SpikeGeneratorGroup(nAnfsPerInputFile, sbIpCellIndices, sbIpSpkTimes)
# gbIpIdxSpktimeArray = np.loadtxt(gbIpSpkFile)
# gbIpCellIndices = gbIpIdxSpktimeArray[:, 0].astype(int)
# gbIpSpkTimes = gbIpIdxSpktimeArray[:, 1] * ms
# gbIpSpkGenGrp = SpikeGeneratorGroup(nGbcsIp, gbIpCellIndices, gbIpSpkTimes)
# anfIdxSpktimeArray = np.loadtxt(anfSpkFile)
# anfIndices = anfIdxSpktimeArray[:, 0].astype(int)
# nANF = 132
# #anfSpkTimes = [i * second for i in anfIdxSpktimeArray[:, 1]]
# anfSpkTimes = anfIdxSpktimeArray[:, 1] * 1000*ms
# anfSpkGeneratorGrp = SpikeGeneratorGroup(nANF, anfIndices, anfSpkTimes)
# Membrane and Ion-Channel parameters
# C = 12*pF
C = 21.7*pF # 21.7pF: non-principal LSO neuron (Barnes-Davies et al. 2004)
Eh = -43*mV
EK = -70*mV # -77*mV in mod file
El = -65*mV
ENa = 50*mV
nf = 0.85 # proportion of n vs p kinetics
zss = 0.5 # steady state inactivation of glt
# default temp_degC = 37., human body temperature in degree celcius
# q10 for ion-channel time-constants (RM2003, p.3106):
q10 = 3. ** ((temp_degC - 22.) / 10.)
# q10 for ion-channel gbar parameters (RM2003, p.3106):
q10gbar = 2. ** ((temp_degC - 22.) / 10.)
# hcno current (octopus cell)
frac = 0.0
qt = 4.5 ** ((temp_degC - 33.) / 10.)
# Synaptic parameters:
Es_e = 0.*mV
tausE = 1.5*ms
Es_i = -90*mV
tausI = 3.0*ms
'''Synaptic weights are unitless according to Brian2.
The effective unit is siemens, so they can work in amp, volt, siemens eqns.
We multiply synaptic weight w_e by unit siemens when adding it to g_e.
We use a local variable w_e for synaptic weight rather than the standard w:'''
w_elson = 4.5e-9 #0.7e-9
w_ilson = 45.e-9 #7e-9 # 6e-9 @ 200Hz; 12e-9 @ 600 Hz
'''Here's why:
The synapses sbc3SynE.w synaptic weight references the Same State Variable as
as the neuron group sbc3Grp.w (klt activation w).
Update sbc3Grp.w, and you set sbc3SynE.w to same value, and vice versa.
This way klt activation and synaptic weight are identical and quite ridiculous.
So use a local variable other than w for the synaptic weight!'''
# Maximal conductances of different cell types in nS
maximal_conductances = dict(
LsoNp1a=(1000, 150, 0, 0, 1.85, 0, 7.4),
type1c=(1000, 150, 0, 0, 0.5, 0, 2),
type1t=(1000, 80, 0, 65, 0.5, 0, 2),
type12=(1000, 150, 20, 0, 2, 0, 2),
type21=(1000, 150, 35, 0, 3.5, 0, 2),
type2=(1000, 150, 200, 0, 20, 0, 2),
type2g2x=(2000, 300, 400, 0, 40, 0, 2),
type2g1p5x=(1000, 150, 300, 0, 30, 0, 2),
type2g1p2x=(1200, 180, 240, 0, 24, 0, 2),
type2g0p5x=(1000, 150, 100, 0, 10, 0, 2),
type2o=(1000, 150, 600, 0, 0, 40, 2) # octopus cell
)
gnabar, gkhtbar, gkltbar, gkabar, ghbar, gbarno, gl = [x * nS for x in maximal_conductances[neuron_type]]
# # Maximal conductances of different cell types in nS
# maximal_conductances = dict(
# type1c=(1000, 150, 0, 0, 0.5, 0, 2),
# type1t=(1000, 80, 0, 65, 0.5, 0, 2),
# type12=(1000, 150, 20, 0, 2, 0, 2),
# type21=(1000, 150, 35, 0, 3.5, 0, 2),
# type2=(1000, 150, 200, 0, 20, 0, 2),
# type2gLTH2x=(1000, 150, 400, 0, 40, 0, 2),
# type2gLTH0p5x=(1000, 150, 100, 0, 10, 0, 2),
# type2o=(1000, 150, 600, 0, 0, 40, 2) # octopus cell
# )
# gnabar, gkhtbar, gkltbar, gkabar, ghbar, gbarno, gl = [x * nS for x in maximal_conductances[neuron_type]]
# Classical Na channel
eqs_na = """
ina = gnabar*m**3*h*(ENa-v) : amp
dm/dt=q10*(minf-m)/mtau : 1
dh/dt=q10*(hinf-h)/htau : 1
minf = 1./(1+exp(-(vu + 38.) / 7.)) : 1
hinf = 1./(1+exp((vu + 65.) / 6.)) : 1
mtau = ((10. / (5*exp((vu+60.) / 18.) + 36.*exp(-(vu+60.) / 25.))) + 0.04)*ms : second
htau = ((100. / (7*exp((vu+60.) / 11.) + 10.*exp(-(vu+60.) / 25.))) + 0.6)*ms : second
"""
# KHT channel (delayed-rectifier K+)
eqs_kht = """
ikht = gkhtbar*(nf*n**2 + (1-nf)*p)*(EK-v) : amp
dn/dt=q10*(ninf-n)/ntau : 1
dp/dt=q10*(pinf-p)/ptau : 1
ninf = (1 + exp(-(vu + 15) / 5.))**-0.5 : 1
pinf = 1. / (1 + exp(-(vu + 23) / 6.)) : 1
ntau = ((100. / (11*exp((vu+60) / 24.) + 21*exp(-(vu+60) / 23.))) + 0.7)*ms : second
ptau = ((100. / (4*exp((vu+60) / 32.) + 5*exp(-(vu+60) / 22.))) + 5)*ms : second
"""
# Ih channel (subthreshold adaptive, non-inactivating)
eqs_ih = """
ih = ghbar*r*(Eh-v) : amp
dr/dt=q10*(rinf-r)/rtau : 1
rinf = 1. / (1+exp((vu + 76.) / 7.)) : 1
rtau = ((100000. / (237.*exp((vu+60.) / 12.) + 17.*exp(-(vu+60.) / 14.))) + 25.)*ms : second
"""
# KLT channel (low threshold K+)
eqs_klt = """
iklt = gkltbar*w**4*z*(EK-v) : amp
dw/dt=q10*(winf-w)/wtau : 1
dz/dt=q10*(zinf-z)/ztau : 1
winf = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25 : 1
zinf = zss + ((1.-zss) / (1 + exp((vu + 71.) / 10.))) : 1
wtau = ((100. / (6.*exp((vu+60.) / 6.) + 16.*exp(-(vu+60.) / 45.))) + 1.5)*ms : second
ztau = ((1000. / (exp((vu+60.) / 20.) + exp(-(vu+60.) / 8.))) + 50)*ms : second
"""
# Ka channel (transient K+)
eqs_ka = """
ika = gkabar*a**4*b*c*(EK-v): amp
da/dt=q10*(ainf-a)/atau : 1
db/dt=q10*(binf-b)/btau : 1
dc/dt=q10*(cinf-c)/ctau : 1
ainf = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25 : 1
binf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1
cinf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1
atau = ((100. / (7*exp((vu+60) / 14.) + 29*exp(-(vu+60) / 24.))) + 0.1)*ms : second
btau = ((1000. / (14*exp((vu+60) / 27.) + 29*exp(-(vu+60) / 24.))) + 1)*ms : second
ctau = ((90. / (1 + exp((-66-vu) / 17.))) + 10)*ms : second
"""
# Leak
eqs_leak = """
ileak = gl*(El-v) : amp
"""
# h current for octopus cells
eqs_hcno = """
ihcno = gbarno*(h1*frac + h2*(1-frac))*(Eh-v) : amp
dh1/dt=(hinfno-h1)/tau1 : 1
dh2/dt=(hinfno-h2)/tau2 : 1
hinfno = 1./(1+exp((vu+66.)/7.)) : 1
tau1 = bet1/(qt*0.008*(1+alp1))*ms : second
tau2 = bet2/(qt*0.0029*(1+alp2))*ms : second
alp1 = exp(1e-3*3*(vu+50)*9.648e4/(8.315*(273.16+temp_degC))) : 1
bet1 = exp(1e-3*3*0.3*(vu+50)*9.648e4/(8.315*(273.16+temp_degC))) : 1
alp2 = exp(1e-3*3*(vu+84)*9.648e4/(8.315*(273.16+temp_degC))) : 1
bet2 = exp(1e-3*3*0.6*(vu+84)*9.648e4/(8.315*(273.16+temp_degC))) : 1
"""
eqs = """
dv/dt = (ileak + ina + ikht + iklt + ika + ih + ihcno + I + Is_e + Is_i)/C : volt
Is_e = gs_e * (Es_e - v) : amp
gs_e : siemens
Is_i = gs_i * (Es_i - v) : amp
gs_i : siemens
vu = v/mV : 1 # unitless v
I : amp
"""
#Added Is_i to RM2003
eqs += eqs_leak + eqs_ka + eqs_na + eqs_ih + eqs_klt + eqs_kht + eqs_hcno
lsonGrp = NeuronGroup(nLsons, eqs, method='exponential_euler',
threshold='v > -30*mV', refractory='v > -45*mV')
#gbcGrp.I = 2500.0*pA
lsonGrp.I = 0.0*pA
# Initialize model near v_rest with no inputs
lsonGrp.v = Vrest
vu = lsonGrp.v/mV # unitless v
lsonGrp.m = 1./(1+exp(-(vu + 38.) / 7.))
lsonGrp.h = 1./(1+exp((vu + 65.) / 6.))
lsonGrp.n = (1 + exp(-(vu + 15) / 5.))**-0.5
lsonGrp.p = 1. / (1 + exp(-(vu + 23) / 6.))
lsonGrp.r = 1. / (1+exp((vu + 76.) / 7.))
lsonGrp.w = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25
lsonGrp.z = zss + ((1.-zss) / (1 + exp((vu + 71.) / 10.)))
lsonGrp.a = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25
lsonGrp.b = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.c = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.h1 = 1./(1+exp((vu+66.)/7.))
lsonGrp.h2 = 1./(1+exp((vu+66.)/7.))
#lsonGrp.gs_e = 0.0*siemens
#netGbcEq = Network(gbcGrp, report='text')
#netGbcEq.run(50*ms, report='text')
lsonSynI = Synapses(gbCoSpkGenGrp, lsonGrp,
model='''dg_i/dt = -g_i/tausI : siemens (clock-driven)
gs_i_post = g_i : siemens (summed)''',
on_pre='g_i += w_ilson*siemens',
method = 'exact')
lsonSynI.connect(i=np.arange(nGbcsCoPerLson), j=0)
lsonSynE = Synapses(sbIpSpkGenGrp, lsonGrp,
model='''dg_e/dt = -g_e/tausE : siemens (clock-driven)
gs_e_post = g_e : siemens (summed)''',
on_pre='g_e += w_elson*siemens',
method = 'exact')
lsonSynE.connect(i=np.arange(nSbcsIpPerLson), j=0)
lsonSpks = SpikeMonitor(lsonGrp)
lsonState = StateMonitor(lsonGrp, ['v','gs_e'], record=True)
run(8150*ms, report='text')
plt.plot(lsonState.t / ms, lsonState[0].v / mV)
#plt.plot(gbcState.t / ms, gbcState[0].gs_e)
#plt.plot(gSynEState.t / ms, gSynEState[0].gs_e_post)
#plt.plot(gSynEState.t / ms, gSynEState[0].g_e)
plt.xlabel('t (ms)')
plt.ylabel('v (mV)')
plt.show()
dBSPLStrCo = anCoSpkFile[28:30]
dBSPLStrIp = anIpSpkFile[28:30]
dBSPLCo = int(dBSPLStrCo)
dBSPLIp = int(dBSPLStrIp)
IIDdB = dBSPLCo - dBSPLIp
if (IIDdB >= 0):
IIDdBStr = 'IIDP' + str(IIDdB).zfill(2) + 'dBSPL'
elif (IIDdB < 0):
IIDdBStr = 'IIDN' + str(-IIDdB).zfill(2) + 'dBSPL'
# Synaptic parameters in output filename
if (abs(round(tausE/ms)-(tausE/ms)) < 0.1):
Te = str(round(tausE/ms))
else:
#Te = str(tausE/ms)
Te = str(round(10*tausE/ms)/10.)
Te = Te.replace('.','p')
if (abs(round(w_elson/1e-9)-(w_elson/1e-9)) < 0.1):
We = str(round(w_elson/1e-9))
else:
#We = str(w_elson/1e-9)
We = str(round(10*w_elson/1e-9)/10)
We = We.replace('.','p')
if (abs(round(tausI/ms)-(tausI/ms)) < 0.1):
Ti = str(round(tausI/ms))
else:
Ti = str(tausI/ms)
Ti = Ti.replace('.','p')
if (abs(round(w_ilson/1e-9)-(w_ilson/1e-9)) < 0.1):
Wi = str(round(w_ilson/1e-9))
else:
Wi = str(w_ilson/1e-9)
Wi = Wi.replace('.','p')
# lsonSpkFile = 'Lso1aSpTms' + anCoSpkFile[6:13] + anCoSpkFile[16:23] + 'Te'+Te + 'We'+We + 'Ti'+Ti + 'Wi'+Wi + EIPDstr + 'Co' + anCoSpkFile[38:40] + anCoSpkFile[23:31] + anCoSpkFile[45:]
# CaCF, Te We Ti Wi, IID, SPLdBiNNcDD, .txt
lsonSpkFile = 'Lso1aSpTms' + anCoSpkFile[10:17] + 'Te'+Te + 'We'+We + 'Ti'+Ti + 'Wi'+Wi + IIDdBStr + 'co'+dBSPLStrCo + 'ip'+dBSPLStrIp + anCoSpkFile[35:]
file0 = open(lsonSpkFile,'w')
for index in range(len(lsonSpks.t)):
file0.write(str(lsonSpks.i[index]) + " " + str(lsonSpks.t[index] / ms) + '\n')
file0.close()
return (lsonGrp, lsonSpks, lsonState)
# end of mkgbcs
Is_i = gs_i * (Es_i - v) : amp
gs_i : siemens
vu = v/mV : 1 # unitless v
I : amp
"""
#Added Is_i to RM2003
eqs += eqs_leak + eqs_ka + eqs_na + eqs_ih + eqs_klt + eqs_kht + eqs_hcno
lsonGrp = NeuronGroup(nLsons,
eqs,
method='exponential_euler',
threshold='v > -30*mV',
refractory='v > -45*mV')
#gbcGrp.I = 2500.0*pA
lsonGrp.I = 0.0 * pA
# Initialize model near v_rest with no inputs
lsonGrp.v = Vrest
vu = lsonGrp.v / mV # unitless v
lsonGrp.m = 1. / (1 + exp(-(vu + 38.) / 7.))
lsonGrp.h = 1. / (1 + exp((vu + 65.) / 6.))
lsonGrp.n = (1 + exp(-(vu + 15) / 5.))**-0.5
lsonGrp.p = 1. / (1 + exp(-(vu + 23) / 6.))
lsonGrp.r = 1. / (1 + exp((vu + 76.) / 7.))
lsonGrp.w = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25
lsonGrp.z = zss + ((1. - zss) / (1 + exp((vu + 71.) / 10.)))
lsonGrp.a = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25
lsonGrp.b = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.c = 1. / (1 + exp((vu + 66) / 7.))**0.5
lsonGrp.h1 = 1. / (1 + exp((vu + 66.) / 7.))
lsonGrp.h2 = 1. / (1 + exp((vu + 66.) / 7.))
from brian2.units import ms
from brian2 import NeuronGroup, Synapses, StateMonitor, run
import matplotlib.pyplot as plt
import numpy as np
eqs = '''
dv/dt = (I-v)/tau : 1
I : 1
tau : second
'''
# Predefined seed so the experiment is repeatable.
np.random.seed(67726)
DrivingGroup = NeuronGroup(9, eqs, threshold='v>1', reset='v = 0', method='exact')
DrivingGroup.I = np.random.uniform(low=0, high=2, size=9)
DrivingGroup.tau = np.full(
shape=9,
fill_value=10,
dtype=np.int)*ms
# DrivingGroup.tau = [10, 5, 100]*ms
OutputGroup = NeuronGroup(1, eqs, threshold='v>2', reset='v = 0', method='exact')
OutputGroup.I = [0]
OutputGroup.tau = [100]*ms
# Comment these two lines out to see what happens without Synapses
S = Synapses(DrivingGroup, OutputGroup, on_pre='v_post += 0.2')
S.connect(i=np.arange(9), j=0)
# S.connect(i=0, j=2)
