def mapfig(N, R, dendtype, cellnr):
M = sem.SE_3D(
S=np.zeros((32, 1)),
Simdur=10.0,
dt=0.1,
cell=cellnr,
impedancetesting=1,
impdendtype=dendtype,
impdendid=N,
debug=1,
)
thishand = plt.figure()
plt.semilogx(R[3], R[2], "k-")
plt.plot(R[0], R[1], "ro")
plt.plot(R[3][0], R[2][0], "mo")
plt.text(R[0], R[1], str(R[0]) + "Hz/" + str(R[1]) + "$(M\Omega)$")
plt.xlabel("Frequency (Hz)")
plt.ylabel("$Z (M\Omega)$")
if dendtype == 0:
plt.title("Cell " + str(cellnr) + ", distal dend " + str(N))
else:
plt.title("Cell " + str(cellnr) + ", proximal dend " + str(N))
plt.ylim((0, 15))
plt.tight_layout()
return thishand, M["impathlen"], R[0], R[1], R[2][0]
def runonce(
x,
impfreq,
Ra,
imptesttype,
impdendtype,
impdendid,
cell,
):
time.sleep(
np.random.uniform()) # testing whether this helps parallel processing
# errors caused by reading the sloc files..-
print(str(x))
simdur = 1000.0 # 1000
pretime = 100.0
dt = 0.01
N = 32
S = np.zeros((N, 1))
# T = np.linspace(0, simdur - dt, int(round(simdur / dt)))
impamp = 0.1
impphase = 0.0
# IS = impamp * np.sin(impfreq[x]/1000.0 * 2 * np.pi * T + impphase)
if imptesttype[x] == 1:
thisfreq = impfreq[x]
else:
thisfreq = impfreq
R = sem.SE_3D(
S,
Simdur=simdur,
dt=dt,
cell=cell[x],
Ra=Ra[x],
EoHseed=8347236,
impedancetesting=imptesttype[x],
impstim=(impamp, thisfreq, impphase),
impdendtype=impdendtype[x],
impdendid=impdendid[x],
)
if imptesttype[x] == 1:
A = average_over_cyle(R["Vm"], impfreq[x], dt, simdur)
cytax = np.linspace(0, 1, A[0].size)
vmax = np.max(A[0])
imax = np.argmax(A[0])
vmin = np.min(A[0])
imin = np.argmin(A[0])
r_megohm = (vmax - A[2]) / impamp
phi = cytax[imax]
return (r_megohm, phi)
elif imptesttype[x] == -1:
fres, zmax, Pxx, f = analyze_chirpdata(R["Vm"], dt, simdur)
return (fres, zmax, Pxx, f)
def runmain():
simdur = 400.0 # 1000
dt = 0.01
N = 32
freq = 1450.0
cf = 1501.0
db = 70 # 70?
SO = seh.create_sound(fs=100e3,
freq=freq,
duration=(simdur * 0.6) / 1000.0,
dbspl=db)
Silence = seh.create_sound(fs=100e3,
freq=freq,
duration=(simdur * 0.4) / 1000.0,
dbspl=-10)
SP = seh.create_spk(
np.concatenate((SO, Silence)),
fs=100e3,
N=N + 1,
cf=cf,
seed=65562,
anf_num=(N, 0, 0),
)
S = np.array(SP["spikes"] * 1000.0)
R32Som = sem.SE_BS(
S,
Simdur=simdur,
dt=dt,
G_EoH=0.055,
StochasticEoH=True,
N=N,
G_Dend=0.016,
EoHseed=8347236,
somaticsynapsesonly=1,
)
R32 = sem.SE_BS(
S,
Simdur=simdur,
dt=dt,
G_EoH=0.055,
StochasticEoH=True,
N=N,
G_Dend=0.016,
EoHseed=8347236,
)
R0 = sem.SE_BS(
S,
Simdur=simdur,
dt=dt,
G_EoH=0.055,
StochasticEoH=True,
N=N,
G_Dend=0.0,
EoHseed=8347236,
)
R3d_32 = sem.SE_3D(
S,
Simdur=simdur,
dt=dt,
G_EoH=0.055,
StochasticEoH=True,
N=N,
G_Dend=0.064,
EoHseed=8347236,
)
R3d_32som = sem.SE_3D(
S,
Simdur=simdur,
dt=dt,
G_EoH=0.055,
StochasticEoH=True,
N=N,
G_Dend=0.064,
EoHseed=8347236,
somaticsynapsesonly=1,
)
R3d_0 = sem.SE_3D(
S,
Simdur=simdur,
dt=dt,
G_EoH=0.055,
StochasticEoH=True,
N=N,
G_Dend=0.0,
EoHseed=8347236,
)
T = np.linspace(0, simdur - dt, int(round(simdur / dt)))
fh = plt.figure()
sp1 = fh.add_subplot(4, 1, 1)
sp1.set_title("Stylized Dendrite")
sp1.plot(T, R0["Vm"], "b-", linewidth=0.5, label="0nS")
sp1.plot(T, R32["Vm"], "g-", linewidth=0.5, label="16nS")
sp1.set_ylabel("Vm (mV)")
sp1.set_xlabel("Time (ms)")
sp1.plot(T, (np.concatenate((SO, Silence)) * 75) - 75.0,
color="g",
linewidth=0.5)
sp1.legend()
sp2 = fh.add_subplot(4, 1, 2)
sp2.plot(T, R32["Vm"], "g-", linewidth=0.5, label="Dendritic")
sp2.plot(T, R32Som["Vm"], "r-", linewidth=0.5, label="Somatic")
sp2.plot(T, (np.concatenate((SO, Silence)) * 75) - 75.0,
color="g",
linewidth=0.5)
sp2.set_ylabel("Vm (mV)")
sp2.set_xlabel("Time (ms)")
sp2.spines["top"].set_visible(False)
sp2.spines["right"].set_visible(False)
sp2.legend()
sp3 = fh.add_subplot(4, 1, 3)
sp3.set_title("3D Dendrite")
sp3.plot(T, R3d_0["Vm"], "b-", linewidth=0.5, label="0nS")
sp3.plot(T, R3d_32["Vm"], "g-", linewidth=0.5, label="32nS")
sp3.plot(T, (np.concatenate((SO, Silence)) * 75) - 75.0,
color="g",
linewidth=0.5)
sp3.set_ylabel("Vm (mV)")
sp3.set_xlabel("Time (ms)")
sp3.spines["top"].set_visible(False)
sp3.spines["right"].set_visible(False)
sp3.legend()
sp4 = fh.add_subplot(4, 1, 4)
sp4.plot(T, R3d_32["Vm"], "g-", linewidth=0.5, label="Dendritic")
sp4.plot(T, R3d_32som["Vm"], "r-", linewidth=0.5, label="Somatic")
sp4.plot(T, (np.concatenate((SO, Silence)) * 75) - 75.0,
color="g",
linewidth=0.5)
sp4.set_ylabel("Vm (mV)")
sp4.set_xlabel("Time (ms)")
sp4.spines["top"].set_visible(False)
sp4.spines["right"].set_visible(False)
sp4.legend()
plt.tight_layout()
plt.show()
def runonce(x, P):
####hard coded settings, rarely change these:
APdetect_thr = -75
####GENERATE SPIKE INPUTS (the spiketimes are always the same to improve comparability)
thisdur = (P["dur"][x] - 5.0) / 1000.0
N = P["Nsyn"][x] + 1
anf_num = (N, 0, 0)
if P["AdvZseed"]:
thisZseed = P["Zseed"] + x
else:
thisZseed = P["Zseed"]
if P["AdvEoHseed"]:
thisEoHseed = P["EoHseed"] + x
else:
thisEoHseed = P["EoHseed"]
SO = seh.create_sound(fs=100e3,
freq=P["freq"][x],
duration=thisdur,
dbspl=P["dB"][x])
SP = seh.create_spk(SO,
fs=100e3,
N=N,
cf=P["cf"][x],
seed=thisZseed,
anf_num=anf_num) # generate enough spiketrains
S = np.array(SP["spikes"] * 1000.0)
R0 = sem.SE_3D(
S,
Simdur=P["dur"][x],
dt=P["dt"][x],
G_EoH=P["G_EoH"][x],
StochasticEoH=P["Stochastic_EoH"][x],
N=P["Nsyn"][x],
G_Dend=P["gsyn"][x] * 0.0, # set dendritic synapses to zero
tau_Dend=P["tau_dendsyn"][x],
gKLT_d=P["gKLT_d"][x],
gIH_d=P["gIH_d"][x],
gLEAK_d=P["gLEAK_d"][x],
cell=P["cell"][x],
EoHseed=thisEoHseed,
somaticsynapsesonly=P["synapselocationtype"][x],
)
#
RD = sem.SE_3D(
S,
Simdur=P["dur"][x],
dt=P["dt"][x],
G_EoH=P["G_EoH"][x],
StochasticEoH=P["Stochastic_EoH"][x],
N=P["Nsyn"][x],
G_Dend=P["gsyn"][x],
tau_Dend=P["tau_dendsyn"][x],
gKLT_d=P["gKLT_d"][x],
gIH_d=P["gIH_d"][x],
gLEAK_d=P["gLEAK_d"][x],
cell=P["cell"][x],
EoHseed=thisEoHseed,
somaticsynapsesonly=P["synapselocationtype"][x],
)
Ev0 = seh.SimpleDetectAP(R0["Vm"],
thr=APdetect_thr,
dt=P["dt"][x],
LM=-20,
RM=10)
EvD = seh.SimpleDetectAP(RD["Vm"],
thr=APdetect_thr,
dt=P["dt"][x],
LM=-20,
RM=10)
APr0 = len(Ev0["PeakT"]) / (P["dur"][x] / 1000.0)
APrD = len(EvD["PeakT"]) / (P["dur"][x] / 1000.0)
VS0, phi0, Ray0, phases0 = seh.vectorstrength(Ev0["PeakT"], P["freq"][x],
[0.0, P["dur"][x]])
VSD, phiD, RayD, phasesD = seh.vectorstrength(EvD["PeakT"], P["freq"][x],
[0.0, P["dur"][x]])
print((str(x)))
return [VS0, phi0, Ray0, APr0, VSD, phiD, RayD, APrD]
def runonce(x, cf=450.0, freq=500.0, dbspl=65.0, dur=1000.0):
thisdur = (dur - 5.0) / 1000.0
N = 32
anf_num = (N + 1, 0, 0)
SO = seh.create_sound(
fs=100e3,
freq=freq,
duration=thisdur,
dbspl=dbspl,
)
SP = seh.create_spk(SO,
fs=100e3,
N=N + 1,
cf=cf,
seed=65564 + x,
anf_num=anf_num)
S = np.array(SP["spikes"] * 1000.0)
R0 = sem.SE_BS(
S,
Simdur=dur,
dt=0.01,
G_EoH=0.055,
StochasticEoH=True,
EoHseed=917634 + x,
N=32,
G_Dend=0.0,
)
print(".")
RD = sem.SE_BS(
S,
Simdur=dur,
dt=0.01,
G_EoH=0.055,
StochasticEoH=True,
EoHseed=917634 + x,
N=32,
G_Dend=0.016,
)
print(".")
R03 = sem.SE_3D(
S,
Simdur=dur,
dt=0.01,
G_EoH=0.055,
StochasticEoH=True,
EoHseed=917634 + x,
N=32,
G_Dend=0.0,
cell=1,
)
print(".")
RD3 = sem.SE_3D(
S,
Simdur=dur,
dt=0.01,
G_EoH=0.055,
StochasticEoH=True,
EoHseed=917634 + x,
N=32,
G_Dend=0.064,
cell=1,
)
print(".")
Ev0 = seh.SimpleDetectAP(R0["Vm"], thr=-100, dt=0.01, LM=-20, RM=10)
EvD = seh.SimpleDetectAP(RD["Vm"], thr=-100, dt=0.01, LM=-20, RM=10)
Ev03 = seh.SimpleDetectAP(R03["Vm"], thr=-100, dt=0.01, LM=-20, RM=10)
EvD3 = seh.SimpleDetectAP(RD3["Vm"], thr=-100, dt=0.01, LM=-20, RM=10)
VS0, phi0, R0, phases0 = seh.vectorstrength(Ev0["PeakT"], freq, [0.0, dur])
VSD, phiD, RD, phasesD = seh.vectorstrength(EvD["PeakT"], freq, [0.0, dur])
VS03, phi03, R03, phases03 = seh.vectorstrength(Ev03["PeakT"], freq,
[0.0, dur])
VSD3, phiD3, RD3, phasesD3 = seh.vectorstrength(EvD3["PeakT"], freq,
[0.0, dur])
nAP0 = len(Ev0["PeakT"])
nAPD = len(EvD["PeakT"])
nAP03 = len(Ev03["PeakT"])
nAPD3 = len(EvD3["PeakT"])
ISI0 = np.diff(Ev0["PeakT"])
ISID = np.diff(EvD["PeakT"])
ISI03 = np.diff(Ev03["PeakT"])
ISID3 = np.diff(EvD3["PeakT"])
cycdurms = 1000.0 / freq
ncycles = (thisdur * 1000.0) / cycdurms
bins = np.arange(0.0, 25.0, cycdurms)
hist0, bin_edges0 = np.histogram(ISI0, bins)
histD, bin_edgesD = np.histogram(ISID, bins)
hist03, bin_edges03 = np.histogram(ISI03, bins)
histD3, bin_edgesD3 = np.histogram(ISID3, bins)
EI0 = hist0[0] / ncycles
EID = histD[0] / ncycles
EI03 = hist03[0] / ncycles
EID3 = histD3[0] / ncycles
return (nAP0, nAPD, VS0, VSD, EI0, EID, nAP03, nAPD3, VS03, VSD3, EI03,
EID3)
if __name__ == "__main__":
addedargs = sys.argv[1:] # args are (fname, weload, nconds, ncores, cell)
myargs = [1, 5, 4, 1]
for iarg, thisarg in enumerate(addedargs):
myargs[iarg] = int(thisarg)
nconds = myargs[1]
ncores = myargs[2]
cell = myargs[3]
if myargs[0] == 1:
weload = True
else:
weload = False
#
nprox, ndist = sem.SE_3D(np.array((0, )), cell=cell, listinfo=True)
allprox = np.arange(nprox)
alldist = np.arange(ndist)
print("cell" + str(cell) + ": NProx=" + str(nprox) + ", NDist=" +
str(ndist))
if weload and os.path.isfile("./results/impedance_res_3d_R_" + str(cell) +
".npy"):
resR = np.load("./results/impedance_res_3d_R_" + str(cell) + ".npy")
resR = resR.tolist()
datashape_R = np.shape(resR)
allRa = np.linspace(25.0, 300.0, datashape_R[0])
#
resD = np.load("./results/impedance_res_3d_D_" + str(cell) + ".npy")
resD = resD.tolist()
resP = np.load("./results/impedance_res_3d_P_" + str(cell) + ".npy")
resP = resP.tolist()