THE AUDITORY MODELING TOOLBOX
Go to function
EXP_LABACK2023 - - Figures from Laback (2023)
Program code:
function varargout = exp_laback2023(varargin)
%EXP_LABACK2023 - Figures from Laback (2023)
%
% Usage: exp_laback2023('fig5')
%
% EXP_LABACK2023 predicts the effect of different types of precursors
% on ILD-based perceived lateralization. It is based on AN model by
% Smalt, Heinz and Strickland (2014), which is a binaural version of the
% Auditory Nerve Model by Zilany and Bruce (JASA 2006, 2007), that
% involves ipsi- and contralateral cochlear compression control via
% efferent feedback.
%
%
% Examples:
% ---------
%
% To display Fig.5 use :
%
% exp_laback2023('fig5');
%
% To run 'experiment 2' (with model zilany2014), condition 1, use
% exp_laback2023('fig5','i_cond',1);
%
% See also: laback2023 smalt2014 zilany2014
%
% Url: http://amtoolbox.org/amt-1.4.0/doc/experiments/exp_laback2023.php
% #Author: Bernhard Laback, 2022
% #Author: Clara Hollomey, adaptations for AMT, 2023
% #Author: Alejandro Osses, further adaptations for AMT 1.4, 2023
definput.flags.type = {'missingflag','fig5'};
definput.keyvals.i_cond = [1 2 3 4];
[flags,kv] = ltfatarghelper({},definput,varargin);
idxs_cond = kv.i_cond;
if flags.do_missingflag
flagnames=[sprintf('%s, ',definput.flags.type{2:end-2}),...
sprintf('%s or %s',definput.flags.type{end-1},definput.flags.type{end})];
error('%s: You must specify one of the following flags: %s.',upper(mfilename),flagnames);
end
ILD_vector = 0:2:10; % vector of ILD's
num_ILD = length(ILD_vector); % remove kv.NrILD
% remove: kv.step
modelreps = 3;
if flags.do_fig5
idxs_model = [1 2]; % runs both models
end
if flags.do_fig5 || flags.do_fig5ab || flags.do_fig5cd
for i_model = idxs_model
definput.import = {'laback2023'};
if i_model == 1
definput.importdefaults={'exp1'};
SubExp = 0; % used in sig_laback2023.m for choosing the param set
else
definput.importdefaults={'exp2'};
SubExp = 0; % used in sig_laback2023.m for choosing the param set
end
[fg,kv] = ltfatarghelper({},definput,varargin); % obtained the keyvalues
% Taking some variables out of the keyvalues (easier for debugging)
model = kv.model;
fs = kv.fs;
% end Taking
if kv.PrecILD < 0
error('Precursor ILD has to be >= 0');
end
if kv.PrecITD < 0
error('Precursor ITD has to be >= 0!');
end
switch model
case 1
model_str = 'smalt2014';
Modeldelay = 240; %Delay between stimulus and model response (in samples) [default: 240]
case 2
model_str = 'zilany2014';
Modeldelay = 400; %Delay between stimulus and model response (in samples) [default: 240]
end
% Define time vectors for P and T
nvec = 0: 1/fs : kv.DurN-1/fs;
tvec = 0: 1/fs : kv.DurT-1/fs;
% Memory allocation for results matrices
ResultMatrixvecLOW = zeros(modelreps,8,num_ILD);
RespLMatrixLOW = zeros(modelreps,8,num_ILD);
RespRMatrixLOW = zeros(modelreps,8,num_ILD);
ResultMatrixvecMID = zeros(modelreps,8,num_ILD);
RespLMatrixMID = zeros(modelreps,8,num_ILD);
RespRMatrixMID = zeros(modelreps,8,num_ILD);
ResultMatrixvecHIGH = zeros(modelreps,8,num_ILD);
RespLMatrixHIGH = zeros(modelreps,8,num_ILD);
RespRMatrixHIGH = zeros(modelreps,8,num_ILD);
NrFiberTypes = length(kv.spontvec);
for i_fibtype=1:NrFiberTypes %three different fiber types (based on SR)
% spontaneous rate (in spikes/s) of the fiber BEFORE
% refractory effects stimulus parameters (default: 30)
switch i_fibtype
case 1
i_fibtype_str = 'low-SR';
case 2
i_fibtype_str = 'mid-SR';
case 3
i_fibtype_str = 'high-SR';
end
amt_disp(sprintf('Model=%s; Fibertype=%s', model_str, i_fibtype_str));
for i_modelreps=1:modelreps %loop for repetition
amt_disp(sprintf('\tRepetition %.0f of %.0f', i_modelreps,modelreps));
% Initialisation of the variables containing the results per
% condition and ILD.
ResultMatrix = nan(8,num_ILD); % 8=2*4 conditions
RespLMatrix = nan(size(ResultMatrix));
RespRMatrix = nan(size(ResultMatrix));
for i_cond = idxs_cond %loop for four different stimulus conditions (1: no T, 2:diotic P, 3: ipsi P, 4: contra P)
RespL = zeros(2, num_ILD);
RespR = zeros(2, num_ILD);
diffvec = zeros(2, num_ILD);
%Define precursor conditions
switch i_cond
case 1 % no precursor
precmode = 0; %precursor mode: 0 = without precursor, 1 = with precursor
ILDNoise = 0; %ILD of precursor noise, positive value favoring left ear
ILDNoiseLin = 10^(ILDNoise/20);
PrecITD_side = [];
PrecITD_t= [];
case 2 % diotic precursor
precmode = 1;
ILDNoise = 0;
ILDNoiseLin = 10^(ILDNoise/20);
PrecITD_side = 0;
PrecITD_t=0;
case 3 % precursor favoring LEFT ear (ipsilateral, because target is always left leading)
precmode = 1;
ILDNoise = kv.PrecILD;
ILDNoiseLin = 10^(ILDNoise/20);
PrecITD_side = -1;
PrecITD_t=kv.PrecITD;
case 4 % precursor favoring right ear (contralater, because target is always left leading)
precmode = 1;
ILDNoise = kv.PrecILD*-1;
ILDNoiseLin = 10^(ILDNoise/20);
PrecITD_side = 1;
PrecITD_t=kv.PrecITD;
end
% clear stim RespL RespR diffvec timeout meout mocr c1filterout c2filterout c1vihc c2vihc vihc psthbins psthtime pr psth wstart wend;
stim = [];
%TODO:get stimuli sorted
%[stimTarg, stimPrec, noiseILDvec, TotDur, B_P, A_P, PrecNoise] = sig_laback2023(precmode,...
% kv.frozennoise, kv.Experiment, tvec, nvec, kv.CF, kv.Fs, kv.F0cue,...
% kv.RampDur, kv.stimdb, kv.DurT, kv.DurN, kv.DurG, kv.factor, ILDNoiseLin,...
% PrecITD_side, PrecITD_t);
[stimTarg, ~, noiseILDvec, TotDur, B_P, A_P, PrecNoise] = sig_laback2023(precmode,...
tvec, nvec, ILDNoiseLin, PrecITD_side, PrecITD_t, model, 'SubExp',SubExp);
if kv.AdaptMode == 1
%Generate Pre-precursor
%PrecPrecNoise=randn(length(kv.preprecvec),1);
PrecPrec = filter(B_P, A_P, PrecNoise);
PrecPrec = local_raisedcosine(PrecPrec,fs,kv.RampDur);
%Add ILD to PrecPrec
PrecPrecILDLin = 10^(kv.PrecPrecILD/20);
PrecPrec = PrecPrec * rms(stimTarg)/rms(PrecPrec);
PrecPrecVec(1,:) = PrecPrec * sqrt(PrecPrecILDLin);
PrecPrecVec(2,:) = PrecPrec / sqrt(PrecPrecILDLin);
%TotDur=DurT+DurN+DurG+DurN+DurG; %Duration of stimulus
end
%% Run the models
for MOCstat=1:2 %Moc off vs. on modes
if MOCstat==1
flags_extra = {'no_MOC'};
% if model == 2
% flags_extra(end+1:end+2) = {'ANmode',3}; % default back-end
% end
elseif MOCstat==2
flags_extra = {'MOC'};
% if model == 2
% flags_extra(end+1:end+2) = {'ANmode',1}; % LSO from Ashida2016
% end
end
for i_ILD=1:num_ILD
ILD = ILD_vector(i_ILD); % dB, for calculating all ILDs around zero: add -12
ILD_linear = 10^(ILD/20);
if precmode == 0 % used in cond=1, zilany2014
%Apply ILD
switch kv.AdaptMode
case 0
stim(:,1) = stimTarg*sqrt(ILD_linear); %positive ILD value enhances left ear
stim(:,2) = stimTarg/sqrt(ILD_linear);
case 1
error('Not validated yet by AO') % convert first to column vectors
stim(1,:) = [PrecPrecVec(1,:) gapvecLong stimTarg'*sqrt(ILD_linear)]; %stimnoise
stim(2,:) = [PrecPrecVec(2,:) gapvecLong stimTarg'/sqrt(ILD_linear)];
end
elseif precmode == 1 % used in cond=2, 3, 4, zilany2014
%Apply ILD
targL = stimTarg*sqrt(ILD_linear); %positive ILD value enhances left ear
targR = stimTarg/sqrt(ILD_linear);
if kv.AdaptMode == 0
stim(:,1) = [noiseILDvec(:,1); kv.gapvec; targL]; %stimnoise
stim(:,2) = [noiseILDvec(:,2); kv.gapvec; targR];
elseif kv.AdaptMode ==1
error('Not validated yet by AO')
stim(:,1) = [PrecPrecVec(1,:) kv.gapvecLong noiseILDvec(1,:) kv.gapvec targL']; %stimnoise
stim(:,2) = [PrecPrecVec(2,:) kv.gapvecLong noiseILDvec(2,:) kv.gapvec targR'];
end
end
output = laback2023(stim,fs,'argimport',fg,kv,'fiberType',i_fibtype,'precmode',precmode, ...
'Modeldelay',Modeldelay,'Onset',kv.Onset, ...
'noiseILDvec',noiseILDvec,'length_stimTarg',length(stimTarg), ... % flags from the stimuli
flags_extra{:});
% Nothing to do...already implemented in laback2023.m
RespL(MOCstat, i_ILD) = output.RespL;
RespR(MOCstat, i_ILD) = output.RespR;
diffvec(MOCstat, i_ILD) = output.diffvec;
disp('')
end % for i_ILD
disp('')
end % for MOCstat
disp('')
switch i_cond
case 1
idx2assign = [1 2];
case 2
idx2assign = [3 4];
case 3
idx2assign = [5 6];
case 4
idx2assign = [7 8];
end
ResultMatrix(idx2assign,:) = diffvec(1:2,:);
RespLMatrix(idx2assign,:) = RespL(1:2,:);
RespRMatrix(idx2assign,:) = RespR(1:2,:);
end % i_cond
%different P conditions
switch i_fibtype
case 1
ResultMatrixvecLOW(i_modelreps,:,:)=ResultMatrix;
if kv.model == 2
ResultMatrixvecLOW_LSO(i_modelreps,:,:)=ResultMatrix;
end
RespLMatrixLOW(i_modelreps,:,:)=RespLMatrix;
RespRMatrixLOW(i_modelreps,:,:)=RespRMatrix;
case 2
ResultMatrixvecMID(i_modelreps,:,:)=ResultMatrix;
if kv.model == 2
ResultMatrixvecMID_LSO(i_modelreps,:,:)=ResultMatrix;
end
RespLMatrixMID(i_modelreps,:,:)=RespLMatrix;
RespRMatrixMID(i_modelreps,:,:)=RespRMatrix;
case 3
ResultMatrixvecHIGH(i_modelreps,:,:)=ResultMatrix;
if kv.model == 2
ResultMatrixvecHIGH_LSO(i_modelreps,:,:)=ResultMatrix;
end
RespLMatrixHIGH(i_modelreps,:,:)=RespLMatrix;
RespRMatrixHIGH(i_modelreps,:,:)=RespRMatrix;
end
end %repetition
end %different fiber types
%% Calculate Mean and SD across model repetitions
Results_Ave_LOW = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_STD_LOW = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_Ave_MID = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_STD_MID = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_Ave_HIGH = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_STD_HIGH = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_Ave_LOW_L = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_STD_LOW_L = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_Ave_MID_L = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_STD_MID_L = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_Ave_HIGH_L = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_STD_HIGH_L = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_Ave_LOW_R = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_STD_LOW_R = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_Ave_MID_R = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_STD_MID_R = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_Ave_HIGH_R = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_STD_HIGH_R = zeros(i_ILD, length(ResultMatrix(:,1)));
Results_Ave_LOW_LSO = [];
Results_STD_LOW_LSO = [];
Results_Ave_MID_LSO = [];
Results_STD_MID_LSO = [];
Results_Ave_HIGH_LSO = [];
Results_STD_HIGH_LSO = [];
if modelreps ==1 error('Attention: Error in calculating mean'); end
%for ILDvec=1:ii
for ILDidx=1:i_ILD
for condvec=1:length(ResultMatrix(:,1))
Results_Ave_LOW(ILDidx,condvec) = mean(ResultMatrixvecLOW(:,condvec,ILDidx));
Results_STD_LOW(ILDidx,condvec) = std(ResultMatrixvecLOW(:,condvec,ILDidx));
Results_Ave_MID(ILDidx,condvec) = mean(ResultMatrixvecMID(:,condvec,ILDidx));
Results_STD_MID(ILDidx,condvec) = std(ResultMatrixvecMID(:,condvec,ILDidx));
Results_Ave_HIGH(ILDidx,condvec) = mean(ResultMatrixvecHIGH(:,condvec,ILDidx));
Results_STD_HIGH(ILDidx,condvec) = std(ResultMatrixvecHIGH(:,condvec,ILDidx));
Results_Ave_LOW_L(ILDidx,condvec) = mean(RespLMatrixLOW(:,condvec,ILDidx));
Results_STD_LOW_L(ILDidx,condvec) = std(RespLMatrixLOW(:,condvec,ILDidx));
Results_Ave_MID_L(ILDidx,condvec) = mean(RespLMatrixMID(:,condvec,ILDidx));
Results_STD_MID_L(ILDidx,condvec) = std(RespLMatrixMID(:,condvec,ILDidx));
Results_Ave_HIGH_L(ILDidx,condvec) = mean(RespLMatrixHIGH(:,condvec,ILDidx));
Results_STD_HIGH_L(ILDidx,condvec) = std(RespLMatrixHIGH(:,condvec,ILDidx));
Results_Ave_LOW_R(ILDidx,condvec) = mean(RespRMatrixLOW(:,condvec,ILDidx));
Results_STD_LOW_R(ILDidx,condvec) = std(RespRMatrixLOW(:,condvec,ILDidx));
Results_Ave_MID_R(ILDidx,condvec) = mean(RespRMatrixMID(:,condvec,ILDidx));
Results_STD_MID_R(ILDidx,condvec) = std(RespRMatrixMID(:,condvec,ILDidx));
Results_Ave_HIGH_R(ILDidx,condvec) = mean(RespRMatrixHIGH(:,condvec,ILDidx));
Results_STD_HIGH_R(ILDidx,condvec) = std(RespRMatrixHIGH(:,condvec,ILDidx));
if kv.model == 2
%LSO Rate Difference
Results_Ave_LOW_LSO(ILDidx,condvec) = mean(ResultMatrixvecLOW_LSO(:,condvec,ILDidx));
Results_STD_LOW_LSO(ILDidx,condvec) = std(ResultMatrixvecLOW_LSO(:,condvec,ILDidx));
Results_Ave_MID_LSO(ILDidx,condvec) = mean(ResultMatrixvecMID_LSO(:,condvec,ILDidx));
Results_STD_MID_LSO(ILDidx,condvec) = std(ResultMatrixvecMID_LSO(:,condvec,ILDidx));
Results_Ave_HIGH_LSO(ILDidx,condvec) = mean(ResultMatrixvecHIGH_LSO(:,condvec,ILDidx));
Results_STD_HIGH_LSO(ILDidx,condvec) = std(ResultMatrixvecHIGH_LSO(:,condvec,ILDidx));
end
end
end
%ResultMatrix= ResultMatrix';
%Plotting (as in Laback, 2023).
if i_model == 1
LSOmode = 0;
else
LSOmode = 1;
end
%Plotting_MOC_LSO
local_plot(LSOmode, Results_Ave_LOW, Results_Ave_MID, Results_Ave_HIGH,...
Results_STD_LOW, Results_STD_MID, Results_STD_HIGH, ILD_vector, ILD,...
Results_Ave_LOW_LSO, Results_STD_LOW_LSO, Results_Ave_MID_LSO, Results_STD_MID_LSO,...
Results_Ave_HIGH_LSO, Results_STD_HIGH_LSO,i_model,flags);
end % i_exp (1=smalt2014, 2=zilany2014)
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% local functions
function local_plot(LSOmode, Results_Ave_LOW, Results_Ave_MID, Results_Ave_HIGH,...
Results_STD_LOW, Results_STD_MID, Results_STD_HIGH, ILDvec, ILD,...
Results_Ave_LOW_LSO, Results_STD_LOW_LSO, Results_Ave_MID_LSO, Results_STD_MID_LSO,...
Results_Ave_HIGH_LSO, Results_STD_HIGH_LSO,jj,flags)
if LSOmode == 0
W=[83 17 0]; %Weighting Coefficients for Low-, Mid-, and High-SR fibers of AN model: these can be optimized using the script
W_LSO=[80 20 0]; %Weighting Coefficients for Low, Mid, and High-SR fibers of LSO model; %[70 20 10]
%Calculate Weighted Mean
Results_Ave_WMean = ( Results_Ave_LOW*W(1) + Results_Ave_MID*W(2) + Results_Ave_HIGH*W(3) ) / sum(W);
Results_STD_WMean = ( Results_STD_LOW*W(1) + Results_STD_MID*W(2) + Results_STD_HIGH*W(3) ) / sum(W);
NrILD=length(Results_Ave_WMean(:,1));
%ILDvec = 0:10/(NrILD-1):10;
%ILDvec=0:2:10;
%NrILD=6;
%MOC on=====================================================
figure(1); %Low-SR fibers
subplot(4,4,1);
hold on;
errorbar(ILDvec,Results_Ave_LOW(:,2)', Results_STD_LOW(:,2)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_LOW(:,4)', Results_STD_LOW(:,4)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_LOW(:,6)', Results_STD_LOW(:,6)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_LOW(:,8)', Results_STD_LOW(:,8)', 'c'); %Contra
axis([0 ILD -10 70]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(2,60,'LOW-SR fibers')
legend('NoP','Diotic','Ipsi','Contra');
hold off;
%Mid-SR fibers
subplot(4,4,2);
hold on;
errorbar(ILDvec,Results_Ave_MID(:,2)', Results_STD_MID(:,2)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_MID(:,4)', Results_STD_MID(:,4)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_MID(:,6)', Results_STD_MID(:,6)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_MID(:,8)', Results_STD_MID(:,8)', 'c'); %Contra
axis([0 ILD -10 160]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(2,140,'MID-SR fibers')
hold off;
%High-SR fibers
subplot(4,4,3);
hold on;
errorbar(ILDvec,Results_Ave_HIGH(:,2)', Results_STD_HIGH(:,2)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_HIGH(:,4)', Results_STD_HIGH(:,4)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_HIGH(:,6)', Results_STD_HIGH(:,6)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_HIGH(:,8)', Results_STD_HIGH(:,8)', 'c'); %Contra
axis([0 ILD 0 160]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(2,140,'HIGH-SR fibers')
hold off;
%Weighted Mean
subplot(4,4,4);
hold on;
errorbar(ILDvec,Results_Ave_WMean(:,2)', Results_STD_WMean(:,2)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_WMean(:,4)', Results_STD_WMean(:,4)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_WMean(:,6)', Results_STD_WMean(:,6)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_WMean(:,8)', Results_STD_WMean(:,8)', 'c'); %Contra
axis([0 ILD 0 70]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(2,60,'Weighted Mean')
hold off;
%MOC off=====================================================
figure(1); %Low-SR fibers
subplot(4,4,5);
hold on;
errorbar(ILDvec,Results_Ave_LOW(:,1)', Results_STD_LOW(:,1)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_LOW(:,3)', Results_STD_LOW(:,3)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_LOW(:,5)', Results_STD_LOW(:,5)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_LOW(:,7)', Results_STD_LOW(:,7)', 'c'); %Contra
axis([0 ILD -10 70]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(2,60,'LOW-SR fibers')
legend('NoP','Diotic','Ipsi','Contra');
hold off;
%Mid-SR fibers
subplot(4,4,6);
hold on;
errorbar(ILDvec,Results_Ave_MID(:,1)', Results_STD_MID(:,1)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_MID(:,3)', Results_STD_MID(:,3)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_MID(:,5)', Results_STD_MID(:,5)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_MID(:,7)', Results_STD_MID(:,7)', 'c'); %Contra
axis([0 ILD -10 160]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(2,140,'MID-SR fibers')
hold off;
%High-SR fibers
subplot(4,4,7);
hold on;
errorbar(ILDvec,Results_Ave_HIGH(:,1)', Results_STD_HIGH(:,1)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_HIGH(:,3)', Results_STD_HIGH(:,3)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_HIGH(:,5)', Results_STD_HIGH(:,5)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_HIGH(:,7)', Results_STD_HIGH(:,7)', 'c'); %Contra
axis([0 ILD 0 160]); xlabel('Target ILD (dB)'); ylabel('Response Azimuth (deg)'); text(2,140,'HIGH-SR fibers')
hold off;
%Weighted Mean
subplot(4,4,8);
hold on;
errorbar(ILDvec,Results_Ave_WMean(:,1)', Results_STD_WMean(:,1)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_WMean(:,3)', Results_STD_WMean(:,3)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_WMean(:,5)', Results_STD_WMean(:,5)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_WMean(:,7)', Results_STD_WMean(:,7)', 'c'); %Contra
axis([0 ILD 0 70]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(1,60,'Weighted Mean')
hold off;
end
if LSOmode == 1
%LSO MODEL
%Calculate Weighted Mean
%W=[33.3 33.3 33.3]; %Libermann
W_LSO=[80 20 0]; %Weighting Coefficients for Low, Mid, and High-SR fibers of LSO model; %[70 20 10]
Results_Ave_WMean_LSO = ( Results_Ave_LOW_LSO*W_LSO(1) + Results_Ave_MID_LSO*W_LSO(2) + Results_Ave_HIGH_LSO*W_LSO(3) ) / sum(W_LSO);
Results_STD_WMean_LSO = ( Results_STD_LOW_LSO*W_LSO(1) + Results_STD_MID_LSO*W_LSO(2) + Results_STD_HIGH_LSO*W_LSO(3) ) / sum(W_LSO);
%ILDvec=0:5:10;
%ILDvec=0:2:10;
%MOC on=====================================================
figure(1); %Low-SR fibers
subplot(4,4,9);
hold on;
errorbar(ILDvec,Results_Ave_LOW_LSO(:,2)', Results_STD_LOW_LSO(:,2)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_LOW_LSO(:,4)', Results_STD_LOW_LSO(:,4)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_LOW_LSO(:,6)', Results_STD_LOW_LSO(:,6)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_LOW_LSO(:,8)', Results_STD_LOW_LSO(:,8)', 'c'); %Contra
axis([0 ILD -30 30]); xlabel('Target ILD (dB)'); ylabel('ILD_LSO (spike rate difference in Hz)'); text(2,20,'LOW-SR fibers')
legend('NoP','Diotic','Ipsi','Contra');
hold off;
%Mid-SR fibers
subplot(4,4,10);
hold on;
errorbar(ILDvec,Results_Ave_MID_LSO(:,2)', Results_STD_MID_LSO(:,2)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_MID_LSO(:,4)', Results_STD_MID_LSO(:,4)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_MID_LSO(:,6)', Results_STD_MID_LSO(:,6)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_MID_LSO(:,8)', Results_STD_MID_LSO(:,8)', 'c'); %Contra
axis([0 ILD -60 160]); xlabel('Target ILD (dB)'); ylabel('ILD_LSO (spike rate difference in Hz)'); text(2,100,'MID-SR fibers')
hold off;
%High-SR fibers
subplot(4,4,11)
hold on;
errorbar(ILDvec,Results_Ave_HIGH_LSO(:,2)', Results_STD_HIGH_LSO(:,2)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_HIGH_LSO(:,4)', Results_STD_HIGH_LSO(:,4)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_HIGH_LSO(:,6)', Results_STD_HIGH_LSO(:,6)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_HIGH_LSO(:,8)', Results_STD_HIGH_LSO(:,8)', 'c'); %Contra
axis([0 ILD -30 80]); xlabel('Target ILD (dB)'); ylabel('ILD_LSO (spike rate difference in Hz)'); text(2,35,'HIGH-SR fibers')
hold off;
%Weighted Mean
subplot(4,4,12)
hold on;
errorbar(ILDvec,Results_Ave_WMean_LSO(:,2)', Results_STD_WMean_LSO(:,2)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_WMean_LSO(:,4)', Results_STD_WMean_LSO(:,4)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_WMean_LSO(:,6)', Results_STD_WMean_LSO(:,6)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_WMean_LSO(:,8)', Results_STD_WMean_LSO(:,8)', 'c'); %Contra
axis([0 ILD -30 80]); xlabel('Target ILD (dB)'); ylabel('ILD_LSO (spike rate difference in Hz)'); text(2,35,'Weighted Mean')
hold off;
%===%MOC off=====================================================
figure(1); %Low-SR fibers
subplot(4,4,13)
hold on;
errorbar(ILDvec,Results_Ave_LOW_LSO(:,1)', Results_STD_LOW_LSO(:,1)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_LOW_LSO(:,3)', Results_STD_LOW_LSO(:,3)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_LOW_LSO(:,5)', Results_STD_LOW_LSO(:,5)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_LOW_LSO(:,7)', Results_STD_LOW_LSO(:,7)', 'c'); %Contra
axis([0 ILD -10 40]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(2,35,'LOW-SR fibers')
legend('NoP','Diotic','Ipsi','Contra');
hold off;
%Mid-SR fibers
subplot(4,4,14)
hold on;
errorbar(ILDvec,Results_Ave_MID_LSO(:,1)', Results_STD_MID_LSO(:,1)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_MID_LSO(:,3)', Results_STD_MID_LSO(:,3)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_MID_LSO(:,5)', Results_STD_MID_LSO(:,5)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_MID_LSO(:,7)', Results_STD_MID_LSO(:,7)', 'c'); %Contra
axis([0 ILD -10 40]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(2,35,'MID-SR fibers')
hold off;
%High-SR fibers
subplot(4,4,15)
hold on;
errorbar(ILDvec,Results_Ave_HIGH_LSO(:,1)', Results_STD_HIGH_LSO(:,1)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_HIGH_LSO(:,3)', Results_STD_HIGH_LSO(:,3)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_HIGH_LSO(:,5)', Results_STD_HIGH_LSO(:,5)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_HIGH_LSO(:,7)', Results_STD_HIGH_LSO(:,7)', 'c'); %Contra
axis([0 ILD 0 40]); xlabel('Target ILD (dB)'); ylabel('Response Azimuth (deg)'); text(2,35,'HIGH-SR fibers')
hold off;
%Weighted Mean
subplot(4,4,16)
hold on;
errorbar(ILDvec,Results_Ave_WMean_LSO(:,1)', Results_STD_WMean_LSO(:,1)', 'b'); %NoPrec
errorbar(ILDvec,Results_Ave_WMean_LSO(:,3)', Results_STD_WMean_LSO(:,3)', 'r'); %Diotic
errorbar(ILDvec,Results_Ave_WMean_LSO(:,5)', Results_STD_WMean_LSO(:,5)', 'g'); %Ipsi
errorbar(ILDvec,Results_Ave_WMean_LSO(:,7)', Results_STD_WMean_LSO(:,7)', 'c'); %Contra
axis([0 ILD 0 40]); xlabel('Target ILD (dB)'); ylabel('ILD_INT (spike rate difference in Hz)'); text(2,35,'Weighted Mean')
hold off;
end
Build with Bootstrap