sigma[[x for x in range(len(sfRel)) if sfRel[x] > 1]] = sigHigh
# - now, compute the responses (automatically normalized, since max gaussian value is 1...)
s = [
np.exp(-np.divide(np.square(np.log(x)), 2 * np.square(y)))
for x, y in zip(sfRel, sigma)
]
sfExcCurr = s
sfExc.append(sfExcCurr)
inhSfTuning = hf.getSuppressiveSFtuning()
# Compute weights for suppressive signals
nInhChan = expData['sfm']['mod']['normalization']['pref']['sf']
nTrials = inhSfTuning.shape[0]
inhWeight = hf.genNormWeights(expData, nInhChan, gs_mean, gs_std, nTrials,
expInd)
inhWeight = inhWeight[:, :, 0]
# genNormWeights gives us weights as nTr x nFilters x nFrames - we have only one "frame" here, and all are the same
# first, tuned norm:
sfNormTune = np.sum(-.5 * (inhWeight * np.square(inhSfTuning)), 1)
sfNormTune = sfNormTune / np.amax(np.abs(sfNormTune))
# then, untuned norm:
inhAsym = 0
inhWeight = []
for iP in range(len(nInhChan)):
inhWeight = np.append(
inhWeight, 1 + inhAsym *
(np.log(expData['sfm']['mod']['normalization']['pref']['sf'][iP]) -
np.mean(
np.log(expData['sfm']['mod']['normalization']['pref']['sf'][iP])))
)
measured_resps = hf.organize_resp(
data['spikeCount'], cellStruct,
expInd)[2] # 3rd output is organized sfMix resp.
measured_byDisp = shapeByDisp(measured_resps)
nDisps = len(measured_byDisp)
## get the final filter tunings
omega = np.logspace(-2, 2, 1000)
# where are we evaluating?
# first, normalization
inhSfTuning = hf.getSuppressiveSFtuning(sfs=omega)
nInhChan = cellStruct['sfm']['mod']['normalization']['pref']['sf']
nTrials = inhSfTuning.shape[0]
if fitType == 2:
gs_mean, gs_std = [finalParams[normMu], finalParams[normStd]]
inhWeight = hf.genNormWeights(cellStruct, nInhChan, gs_mean, gs_std,
nTrials, expInd)
inhWeight = inhWeight[:, :, 0]
# genNormWeights gives us weights as nTr x nFilters x nFrames - we have only one "frame" here, and all are the same
# first, tuned norm:
sfNorm = np.sum(-.5 * (inhWeight * np.square(inhSfTuning)), 1)
sfNorm = sfNorm / np.amax(np.abs(sfNorm))
# update function to be used below
updateInhWeight = lambda mn, std: hf.genNormWeights(
cellStruct, nInhChan, mn, std, nTrials, expInd)[:, :, 0]
else:
# then, untuned norm:
inhAsym = 0
inhWeight = []
for iP in range(len(nInhChan)):
inhWeight = np.append(
inhWeight, 1 + inhAsym *
# plot model details - exc/suppressive components
prefSf = modFit[0]
dOrder = modFit[1]
omega = np.logspace(-2, 2, 1000)
sfRel = omega / prefSf
s = np.power(omega, dOrder) * np.exp(-dOrder / 2 * np.square(sfRel))
sMax = np.power(prefSf, dOrder) * np.exp(-dOrder / 2)
sfExc = s / sMax
inhSfTuning = getSuppressiveSFtuning()
# Compute weights for suppressive signals
nInhChan = expData['sfm']['mod']['normalization']['pref']['sf']
if norm_type == 1:
nTrials = inhSfTuning.shape[0]
inhWeight = genNormWeights(expData, nInhChan, gs_mean, gs_std, nTrials)
inhWeight = inhWeight[:, :, 0]
# genNormWeights gives us weights as nTr x nFilters x nFrames - we have only one "frame" here, and all are the same
else:
if modFit[8]: # i.e. if this parameter exists...
inhAsym = modFit[8]
else:
inhAsym = 0
inhWeight = []
for iP in range(len(nInhChan)):
inhWeight = np.append(
inhWeight, 1 + inhAsym *
(np.log(expData['sfm']['mod']['normalization']['pref']['sf'][iP]) -
np.mean(
np.log(expData['sfm']['mod']['normalization']['pref']['sf']
# if modParamsCurr doesn't have inhAsym parameter, add it!
if norm_type == 2:
unweighted = 1
_, _, _, normRespSimple, _ = mod_resp.SFMsimulate(
modParamsCurr,
cellStruct,
disp + 1,
conLevels[conLvl],
sfCenters[sfCent],
unweighted,
normType=norm_type,
expInd=expInd)
nTrials = normRespSimple.shape[0]
nInhChan = cellStruct['sfm']['mod']['normalization'][
'pref']['sf']
inhWeightMat = hf.genNormWeights(cellStruct, nInhChan,
gs_mean, gs_std, nTrials)
normResp = np.sqrt(
(inhWeightMat * normRespSimple).sum(1)).transpose()
norm_sim[disp, conLvl, sfCent] = np.mean(normResp)
# take mean of the returned simulations (10 repetitions per stim. condition)
else: # norm_type == 1 or 3:
_, _, _, _, normResp = mod_resp.SFMsimulate(
modParamsCurr,
cellStruct,
disp + 1,
conLevels[conLvl],
sfCenters[sfCent],
normType=norm_type,
expInd=expInd)
norm_sim[disp, conLvl, sfCent] = np.mean(normResp)
# take mean of the returned simulations (10 repetitions per stim. condition)
def SFMsimulate(params, structureSFM, stimFamily, con, sf_c, unweighted = 0, normType=1):
# Currently, will get slightly different stimuli for excitatory and inhibitory/normalization pools
# But differences are just in phase/TF, but for TF, drawn from same distribution, anyway...
# 4/27/18: if unweighted = 1, then do the calculation/return normResp with weights applied; otherwise, just return the unweighted filter responses
# 00 = preferred spatial frequency (cycles per degree)
# 01 = derivative order in space
# 02 = normalization constant (log10 basis)
# 03 = response exponent
# 04 = response scalar
# 05 = early additive noise
# 06 = late additive noise
# 07 = variance of response gain
# if fitType == 1
# 08 = asymmetry ("historically", bounded [-0.35, 0.35])
# if fitType == 2
# 08 = mean of normalization weights gaussian
# 09 = std of ...
# if fitType == 3
# 08 = offset of c50 tuning filter (filter bounded between [sigOffset, 1]
# 09/10 = standard deviations to the left and right of the peak of the c50 filter
# 11 = peak (in sf cpd) of c50 filter
#print('simulate!');
T = structureSFM['sfm'];
# Get parameter values
# Excitatory channel
pref = {'sf': params[0]};
dord = {'sp': params[1], 'ti': 0.25}; # deriv order in temporal domain = 0.25 ensures broad tuning for temporal frequency
excChannel = {'pref': pref, 'dord': dord};
# Other (nonlinear) model components
sigma = pow(10, params[2]); # normalization constant
# respExp = 2; # response exponent
respExp = params[3]; # response exponent
scale = params[4]; # response scalar
# Noise parameters
noiseEarly = params[5]; # early additive noise
noiseLate = params[6]; # late additive noise
varGain = params[7]; # multiplicative noise
### Normalization parameters
normParams = getNormParams(params, normType);
if normType == 1:
inhAsym = normParams;
elif normType == 2:
gs_mean = normParams[0];
gs_std = normParams[1];
elif normType == 3:
# sigma calculation
offset_sigma = normParams[0]; # c50 filter will range between [v_sigOffset, 1]
stdLeft = normParams[1]; # std of the gaussian to the left of the peak
stdRight = normParams[2]; # '' to the right ''
sfPeak = normParams[3]; # where is the gaussian peak?
else:
inhAsym = normParams;
# Get stimulus structure ready...
stimParams = dict();
stimParams['stimFamily'] = stimFamily;
stimParams['conLevel'] = con;
stimParams['sf_c'] = sf_c;
stimParams['repeats'] = 1; # defaults to 10 anyway, in makeStimulus.py
# Compute weights for suppressive signals
nInhChan = T['mod']['normalization']['pref']['sf'];
nTrials = stimParams['repeats'];
inhWeight = [];
nFrames = 120; # always
if normType == 3:
filter = setSigmaFilter(sfPeak, stdLeft, stdRight);
scale_sigma = -(1-offset_sigma);
evalSfs = structureSFM['sfm']['exp']['trial']['sf'][0]; # the center SF of all stimuli
sigmaFilt = evalSigmaFilter(filter, scale_sigma, offset_sigma, evalSfs);
else:
sigmaFilt = numpy.square(sigma); # i.e. normalization constant squared
if normType == 2:
inhWeightMat = genNormWeights(structureSFM, nInhChan, gs_mean, gs_std, nTrials);
else: # normType == 1 or anything else, we just go with
for iP in range(len(nInhChan)):
inhWeight = numpy.append(inhWeight, 1 + inhAsym*(numpy.log(T['mod']['normalization']['pref']['sf'][iP]) \
- numpy.mean(numpy.log(T['mod']['normalization']['pref']['sf'][iP]))));
# assumption (made by Robbe) - only two normalization pools
inhWeightT1 = numpy.reshape(inhWeight, (1, len(inhWeight)));
inhWeightT2 = repmat(inhWeightT1, nTrials, 1);
inhWeightT3 = numpy.reshape(inhWeightT2, (nTrials, len(inhWeight), 1));
inhWeightMat = numpy.tile(inhWeightT3, (1,1,nFrames));
# Evaluate sfmix experiment
T = structureSFM['sfm']; # [iR]
# Get simple cell response for excitatory channel
E = SFMSimpleResp(structureSFM, excChannel, stimParams);
# Extract simple cell response (half-rectified linear filtering)
Lexc = E['simpleResp'];
# Get inhibitory response (pooled responses of complex cells tuned to wide range of spatial frequencies, square root to bring everything in linear contrast scale again)
normResp = GetNormResp(structureSFM, [], stimParams);
if unweighted == 1:
return [], [], Lexc, normResp['normResp'], [];
Linh = numpy.sqrt((inhWeightMat*normResp['normResp']).sum(1)).transpose();
# Compute full model response (the normalization signal is the same as the subtractive suppressive signal)
numerator = noiseEarly + Lexc;
# taking square root of denominator (after summing squares...) to bring in line with computation in Carandini, Heeger, Movshon, '97
denominator = pow(sigmaFilt + pow(Linh, 2), 0.5); # squaring Linh - edit 7/17
ratio = pow(numpy.maximum(0, numerator/denominator), respExp);
meanRate = ratio.mean(0);
respModel = noiseLate + scale*meanRate; # respModel[iR]
return respModel, Linh, Lexc, normResp['normResp'], denominator;
def SFMGiveBof(params, structureSFM, normType=1, lossType=1, trialSubset=None, maskOri=True, maskIn=None):
# Computes the negative log likelihood for the LN-LN model
# Optional arguments: //note: true means include in mask, false means exclude
# trialSubset - pass in the trials you want to evaluate (ignores all other masks)
# maskOri - in the optimization, we don't include the orientation tuning curve - skip that in the evaluation of loss, too
# maskIn - pass in a mask (overwrite maskOri and trialSubset, i.e. highest presedence)
# MASK IGNORED FOR NOW (12.02.18)
# Returns NLL ###, respModel
# 00 = preferred spatial frequency (cycles per degree)
# 01 = derivative order in space
# 02 = normalization constant (log10 basis)
# 03 = response exponent
# 04 = response scalar
# 05 = early additive noise
# 06 = late additive noise
# 07 = variance of response gain
# if fitType == 1
# 08 = asymmetry ("historically", bounded [-0.35, 0.35])
# if fitType == 2
# 08 = mean of normalization weights gaussian
# 09 = std of ...
# if fitType == 3
# 08 = offset of c50 tuning filter (filter bounded between [sigOffset, 1]
# 09/10 = standard deviations to the left and right of the peak of the c50 filter
# 11 = peak (in sf cpd) of c50 filter
T = structureSFM['sfm'];
# Get parameter values
# Excitatory channel
pref = {'sf': params[0]};
dord = {'sp': params[1], 'ti': 0.25}; # deriv order in temporal domain = 0.25 ensures broad tuning for temporal frequency
excChannel = {'pref': pref, 'dord': dord};
# Inhibitory channel
# nothing in this current iteration - 7/7/17
# Other (nonlinear) model components
sigma = pow(10, params[2]); # normalization constant
# respExp = 2; # response exponent
respExp = params[3]; # response exponent
scale = params[4]; # response scalar
# Noise parameters
noiseEarly = params[5]; # early additive noise
noiseLate = params[6]; # late additive noise
varGain = params[7]; # multiplicative noise
### Normalization parameters
normParams = getNormParams(params, normType);
if normType == 1:
inhAsym = normParams;
elif normType == 2:
gs_mean = normParams[0];
gs_std = normParams[1];
elif normType == 3:
# sigma calculation
offset_sigma = normParams[0]; # c50 filter will range between [v_sigOffset, 1]
stdLeft = normParams[1]; # std of the gaussian to the left of the peak
stdRight = normParams[2]; # '' to the right ''
sfPeak = normParams[3]; # where is the gaussian peak?
else:
inhAsym = normParams;
# Evaluate prior on response exponent -- corresponds loosely to the measurements in Priebe et al. (2004)
priorExp = lognorm.pdf(respExp, 0.3, 0, numpy.exp(1.15)); # matlab: lognpdf(respExp, 1.15, 0.3);
NLLExp = 0; #-numpy.log(priorExp);
# Compute weights for suppressive signals
nInhChan = T['mod']['normalization']['pref']['sf'];
nTrials = len(T['exp']['trial']['num']);
inhWeight = [];
nFrames = 120; # always
if normType == 3:
filter = setSigmaFilter(sfPeak, stdLeft, stdRight);
scale_sigma = -(1-offset_sigma);
evalSfs = structureSFM['sfm']['exp']['trial']['sf'][0]; # the center SF of all stimuli
sigmaFilt = evalSigmaFilter(filter, scale_sigma, offset_sigma, evalSfs);
else:
sigmaFilt = numpy.square(sigma); # i.e. square the normalization constant
if normType == 2:
inhWeightMat = genNormWeights(structureSFM, nInhChan, gs_mean, gs_std, nTrials);
else: # normType == 1 or anything else,
for iP in range(len(nInhChan)):
inhWeight = numpy.append(inhWeight, 1 + inhAsym*(numpy.log(T['mod']['normalization']['pref']['sf'][iP]) \
- numpy.mean(numpy.log(T['mod']['normalization']['pref']['sf'][iP]))));
# assumption by creation (made by Robbe) - only two normalization pools
inhWeightT1 = numpy.reshape(inhWeight, (1, len(inhWeight)));
inhWeightT2 = repmat(inhWeightT1, nTrials, 1);
inhWeightT3 = numpy.reshape(inhWeightT2, (nTrials, len(inhWeight), 1));
inhWeightMat = numpy.tile(inhWeightT3, (1,1,nFrames));
# Evaluate sfmix experiment
for iR in range(1): #range(len(structureSFM['sfm'])): # why 1 for now? We don't have S.sfm as array (just one)
T = structureSFM['sfm']; # [iR]
# Get simple cell response for excitatory channel
E = SFMSimpleResp(structureSFM, excChannel);
# Extract simple cell response (half-rectified linear filtering)
Lexc = E['simpleResp'];
# Get inhibitory response (pooled responses of complex cells tuned to wide range of spatial frequencies, square root to bring everything in linear contrast scale again)
Linh = numpy.sqrt((inhWeightMat*T['mod']['normalization']['normResp']).sum(1)).transpose();
# Compute full model response (the normalization signal is the same as the subtractive suppressive signal)
numerator = noiseEarly + Lexc;
denominator = pow(sigmaFilt + pow(Linh, 2), 0.5); # square Linh added 7/24 - was mistakenly not fixed earlier
ratio = pow(numpy.maximum(0, numerator/denominator), respExp);
meanRate = ratio.mean(0);
respModel = noiseLate + scale*meanRate; # respModel[iR]
rateModel = T['exp']['trial']['duration'] * respModel;
# and get the spike count
spikeCount = T['exp']['trial']['spikeCount'];
if lossType == 1:
# alternative loss function: just (sqrt(modResp) - sqrt(neurResp))^2
lsq = numpy.square(numpy.add(numpy.sqrt(rateModel), -numpy.sqrt(spikeCount)));
NLL = numpy.mean(lsq);
elif lossType == 2:
poiss_llh = numpy.log(poisson.pmf(spikeCount, rateModel));
NLL = numpy.mean(-poiss_llh);
elif lossType == 3:
# Get predicted spike count distributions
mu = numpy.maximum(.01, rateModel); # The predicted mean spike count; respModel[iR]
var = mu + (varGain*pow(mu,2)); # The corresponding variance of the spike count
r = pow(mu,2)/(var - mu); # The parameters r and p of the negative binomial distribution
p = r/(r + mu);
llh = nbinom.pmf(spikeCount, r, p); # Likelihood for each pass under doubly stochastic model
NLL = numpy.mean(-numpy.log(llh)); # The negative log-likelihood of the whole data-set; [iR]
elif lossType == 4: #chi squared
if trialSubset is not None:
warnings.warn('This loss type (chi squared) is not currently equipped to handle hold out subsets');
expByCond, expAll = organize_modResp(spikeCount, structureSFM);
exp_responses = [expByCond.flatten(), numpy.nanvar(expAll, axis=3).flatten()];
modByCond, modAll = organize_modResp(rateModel, structureSFM);
mod_responses = [modByCond.flatten(), numpy.nanvar(modAll, axis=3).flatten()];
NLL = chiSq(exp_responses, mod_responses);
return NLL, respModel;
def SFMGiveBof(params, structureSFM, normType):
# Computes the negative log likelihood for the LN-LN model
# Returns NLL ###, respModel, E
# 00 = preferred spatial frequency (cycles per degree)
# 01 = derivative order in space
# 02 = normalization constant (log10 basis)
# 03 = response exponent
# 04 = response scalar
# 05 = early additive noise
# 06 = late additive noise
# 07 = variance of response gain
# if fitType == 1
# currently, no 08; alternatively, 08 = asymmetry (typically bounded [-0.35, 0.35])
# if fitType == 2
# 08 = mean of normalization weights gaussian
# 09 = std of ...
# if fitType == 3
# 08 = offset of c50 tuning filter (filter bounded between [sigOffset, 1]
# 09/10 = standard deviations to the left and right of the peak of the c50 filter
# 11 = peak (in sf cpd) of c50 filter
print('ha!')
T = structureSFM['sfm']
# Get parameter values
# Excitatory channel
pref = {
'sf': params[0]
}
dord = {
'sp': params[1],
'ti': 0.25
}
# deriv order in temporal domain = 0.25 ensures broad tuning for temporal frequency
excChannel = {
'pref': pref,
'dord': dord
}
# Inhibitory channel
# nothing in this current iteration - 7/7/17
# Other (nonlinear) model components
sigma = pow(10, params[2])
# normalization constant
# respExp = 2; # response exponent
respExp = params[3]
# response exponent
scale = params[4]
# response scalar
# Noise parameters
noiseEarly = params[5]
# early additive noise
noiseLate = params[6]
# late additive noise
varGain = params[7]
# multiplicative noise
### Normalization parameters
normParams = getNormParams(params, normType)
if normType == 1: # flat
inhAsym = normParams
elif normType == 2: # gaussian weighting
gs_mean = normParams[0]
gs_std = normParams[1]
elif normType == 3: # two-halved gaussian for c50
# sigma calculation
offset_sigma = normParams[0]
# c50 filter will range between [v_sigOffset, 1]
stdLeft = normParams[1]
# std of the gaussian to the left of the peak
stdRight = normParams[2]
# '' to the right ''
sfPeak = normParams[3]
# where is the gaussian peak?
else:
inhAsym = normParams
# Evaluate prior on response exponent -- corresponds loosely to the measurements in Priebe et al. (2004)
priorExp = lognorm.pdf(respExp, 0.3, 0, numpy.exp(1.15))
# matlab: lognpdf(respExp, 1.15, 0.3);
NLLExp = 0
#-numpy.log(priorExp);
# Compute weights for suppressive signals
nInhChan = T['mod']['normalization']['pref']['sf']
nTrials = len(T['exp']['trial']['num'])
inhWeight = []
nFrames = 120
# always
if normType == 3:
filter = setSigmaFilter(sfPeak, stdLeft, stdRight)
scale_sigma = -(1 - offset_sigma)
evalSfs = structureSFM['sfm']['exp']['trial']['sf'][0]
# the center SF of all stimuli
sigmaFilt = evalSigmaFilter(filter, scale_sigma, offset_sigma, evalSfs)
else:
sigmaFilt = numpy.square(sigma)
# i.e. square the normalization constant
if normType == 2:
inhWeightMat = genNormWeights(structureSFM, nInhChan, gs_mean, gs_std,
nTrials)
else: # normType == 1 or anything else,
for iP in range(len(nInhChan)):
inhWeight = numpy.append(inhWeight, 1 + inhAsym*(numpy.log(T['mod']['normalization']['pref']['sf'][iP]) \
- numpy.mean(numpy.log(T['mod']['normalization']['pref']['sf'][iP]))))
# assumption (made by Robbe) - only two normalization pools
inhWeightT1 = numpy.reshape(inhWeight, (1, len(inhWeight)))
inhWeightT2 = repmat(inhWeightT1, nTrials, 1)
inhWeightT3 = numpy.reshape(inhWeightT2, (nTrials, len(inhWeight), 1))
inhWeightMat = numpy.tile(inhWeightT3, (1, 1, nFrames))
# Evaluate sfmix experiment
for iR in range(
1
): #range(len(structureSFM['sfm'])): # why 1 for now? We don't have S.sfm as array (just one)
T = structureSFM['sfm']
# [iR]
# Get simple cell response for excitatory channel
E = SFMSimpleResp(structureSFM, excChannel)
# Extract simple cell response (half-rectified linear filtering)
Lexc = E['simpleResp']
# Get inhibitory response (pooled responses of complex cells tuned to wide range of spatial frequencies, square root to bring everything in linear contrast scale again)
Linh = numpy.sqrt(
(inhWeightMat *
T['mod']['normalization']['normResp']).sum(1)).transpose()
# Compute full model response (the normalization signal is the same as the subtractive suppressive signal)
numerator = noiseEarly + Lexc
denominator = pow(sigmaFilt + pow(Linh, 2), 0.5)
# square Linh added 7/24 - was mistakenly not fixed earlier
# denominator = pow(pow(sigma, 2) + pow(Linh, 2), 0.5); # square Linh added 7/24 - was mistakenly not fixed earlier
ratio = pow(numpy.maximum(0, numerator / denominator), respExp)
meanRate = ratio.mean(0)
respModel = noiseLate + scale * meanRate
# respModel[iR]
# Get predicted spike count distributions
mu = numpy.maximum(.01, T['exp']['trial']['duration'] * respModel)
# The predicted mean spike count; respModel[iR]
var = mu + (varGain * pow(mu, 2))
# The corresponding variance of the spike count
r = pow(mu, 2) / (var - mu)
# The parameters r and p of the negative binomial distribution
p = r / (r + mu)
# Evaluate the model
lsq = numpy.square(
numpy.sqrt(respModel) -
numpy.sqrt(T['exp']['trial']['spikeCount']))
NLL = numpy.mean(lsq)
# was 1*lsq
#llh = nbinom.pmf(T['exp']['trial']['spikeCount'], r, p); # Likelihood for each pass under doubly stochastic model
#NLLtempSFM = numpy.mean(-numpy.log(llh)); # The negative log-likelihood of the whole data-set; [iR]
# Combine data and prior
#NLL = NLLtempSFM + NLLExp; # sum over NLLtempSFM if you allow it to be d>1
return NLL, respModel
aRatio)
filt = (filtTemp - filtTemp[0, 0]) / np.amax(np.abs(filtTemp - filtTemp[0, 0]))
# get model details - exc/suppressive components
omega = np.logspace(-2, 2, 1000)
sfRel = omega / prefSf
s = np.power(omega, dOrder) * np.exp(-dOrder / 2 * np.square(sfRel))
sMax = np.power(prefSf, dOrder) * np.exp(-dOrder / 2)
sfExc = s / sMax
inhSfTuning = helper_fcns.getSuppressiveSFtuning()
nInhChan = cellStruct['sfm']['mod']['normalization']['pref']['sf']
if norm_type == 1:
nTrials = inhSfTuning.shape[0]
inhWeight = helper_fcns.genNormWeights(cellStruct, nInhChan, gs_mean,
gs_std, nTrials)
inhWeight = inhWeight[:, :, 0]
# genNormWeights gives us weights as nTr x nFilters x nFrames - we have only one "frame" here, and all are the same
else:
if modFit[8]: # i.e. if this parameter exists...
inhAsym = modFit[8]
else:
inhAsym = 0
inhWeight = []
for iP in range(len(nInhChan)):
inhWeight = np.append(
inhWeight, 1 + inhAsym *
(np.log(cellStruct['sfm']['mod']['normalization']['pref']['sf']
[iP]) - np.mean(
np.log(cellStruct['sfm']['mod']['normalization']