def test_nl(contrast, thresh):
bipolar = ns.cell(.005)
lp = bipolar.sim_central_pathway('gaussian', contrast, length=1000, mean=127)
bipolar.nl_basal.thresh = thresh
letters = bipolar.nl_basal.torate(lp)
nl = ns.compute_nl(lp, letters, 100)
#vesicles = bipolar.adaptation.adapt(letters)
plt.close('nl')
fig, ax = plt.subplots(num='nl')
ax.plot(nl[0], nl[1])
return nl
def test_adaptation_2(delta_t):
'''
fake gaussian stimuli of different contrasts and pass them through the model
'''
#pdb.set_trace()
mean = 127
bipolar = ns.cell(delta_t)
bipolar.adaptation.offset=.6
bipolar.nl_basal.thresh=59
# I want to simulate the central pathway for N trials each lasting the same as periphery_kernel
trials = 1000
length = trials * len(bipolar.periphery.kernel) * ns.sim_delta_t
gaussian_psth = []
nls = []
for c in [3,6,12,24,100]:
# simulate central pathway
lp = bipolar.sim_central_pathway('gaussian', c, length=length, mean=mean)
# redimension lp to be shape = (trials, len(peirphery.kernel))
lp = lp.reshape(trials, -1)
# add peipheral pathway
lp += bipolar.periphery.weight*bipolar.periphery.kernel
# generate [Ca] from lp, using peripheral signal
letters = bipolar.nl_basal.torate(lp)
# generate vesicle release by adapting [Ca]
vesicles = bipolar.adaptation.adapt(letters)
# generate PSTH from vesicles
psth_pnts = len(bipolar.periphery.kernel)
gaussian_psth.append(vesicles.mean(axis=0))
#nl.append(ns.compute_nl(lp.flatten(), vesicles.flatten(), 100))
# Divide vesicles and lp into time bins according to time from saccade and compute nls
nl_perTW =[]
for i in range(10):
startP = int(i*lp.shape[1]/10)
endP = int((i+1)*lp.shape[1]/10)
nl_perTW.append(ns.compute_nl(lp[:,startP:endP].flatten(), vesicles[:,startP:endP].flatten(),100))
nls.append(nl_perTW)
constant_psth = []
trials = 10
length = trials * len(bipolar.periphery.kernel) * ns.sim_delta_t
for m in [32,64,128,256]:
# simulate central pathway
lp = bipolar.sim_central_pathway('gaussian', 0, length=length, mean=m)
# redimension lp to be shape = (trials, len(peirphery.kernel))
lp = lp.reshape(trials, -1)
# add peipheral pathway
lp += bipolar.periphery.weight*bipolar.periphery.kernel
# generate [Ca] from lp, using peripheral signal
letters = bipolar.nl_basal.torate(lp)
# generate vesicle release by adapting [Ca]
vesicles = bipolar.adaptation.adapt(letters)
# generate PSTH from vesicles
psth_pnts = len(bipolar.periphery.kernel)
constant_psth.append(vesicles.mean(axis=0))
return gaussian_psth, nls, constant_psth