MAP

function [frequencies fft_ampdB]= ...

    MAPdemoMultiChOAE (leveldBSPL, toneFrequencies)

% MAPdemo runs the MATLAB auditory periphery model

%

% The OAE is simulated by combining the output from all DRNL channels

%

% arguments leveldBSPL and toneFrequencies are optional

%  defaults are 70 and [5000 6000]

%

% e.g. 

%  MAPdemoMultiChOAE (60, [3000 4000])

global dt DRNLoutput

dbstop if error

restorePath=path;

addpath (['..' filesep 'MAP'], ['..' filesep 'utilities'], ...

    ['..' filesep 'parameterStore'],  ['..' filesep 'wavFileStore'],...

    ['..' filesep 'testPrograms'])

% set parameter file here

paramsName='Normal';

% choose probability because spikes not used to evaluate BM

AN_spikesOrProbability='probability';

% add parameter changes here. paramchanges is a cell array of command

% strings

paramChanges={};

% DRNL channels

lowestBF=1000; 	highestBF= 8000; 	numChannels=41;

% includes BFs at 250 500 1000 2000 4000 8000 (for 11, 21, 31 BFs)

%  the output from all these filters will be combined to form the OAE

BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels));

if nargin<2

    toneFrequencies= 2000;            % single pure tone test

    toneFrequencies=[ 2000 3000]; 	%  F1 F2 for DPOAEs

    toneFrequencies=[ 5000 6000]; 	%  F1 F2

end

duration=0.05;                  % seconds

duration=0.05;                  % seconds

rampDuration=.005;

if nargin<1

    leveldBSPL=70;                  % dB SPL

end

amp=10^(leveldBSPL/20)*28e-6;   % converts to Pascals (peak level)

% Create pure stimulus

sampleRate= 100000;

dt=1/sampleRate;

time=dt: dt: duration;

inputSignal=sum(sin(2*pi*toneFrequencies'*time), 1);

inputSignal=amp*inputSignal;

% apply ramps

if rampDuration>0.5*duration, rampDuration=duration/2; end

rampTime=dt:dt:rampDuration;

ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ...

    ones(1,length(time)-length(rampTime))];

inputSignal=inputSignal.*ramp;  % at the beginning

ramp=fliplr(ramp);

inputSignal=inputSignal.*ramp;  % and at the end

% add 10 ms silence

silenceDuration=0.01;

silence= zeros(1,round(silenceDuration/dt));

inputSignal= [silence inputSignal silence];

time=dt: dt: dt*length(inputSignal);

%% delare 'showMap' options to control graphical output

showMapOptions.printModelParameters=0;   % prints all parameters

showMapOptions.showModelOutput=1;       % plot of all stages

showMapOptions.printFiringRates=1;      % prints stage activity levels

showMapOptions.showACF=0;               % shows SACF (probability only)

showMapOptions.showEfferent=0;          % tracks of AR and MOC

showMapOptions.surfProbability=0;       % 2D plot of HSR response

showMapOptions.surfSpikes=0;            % 2D plot of spikes histogram

showMapOptions.ICrates=0;               % IC rates by CNtauGk

MAP1_14(inputSignal, 1/dt, BFlist, ...

    paramsName, AN_spikesOrProbability, paramChanges);

UTIL_showMAP(showMapOptions, paramChanges)

pause(0.1)

% use this to produce a comnplete record of model parameters

% UTIL_showAllMAPStructures

OAE=sum(DRNLoutput);

figure(5),subplot(2,1,1)

plot(time,OAE)

title(['F=' num2str(toneFrequencies)])

[fft_powerdB, fft_phase, frequencies, fft_ampdB]= UTIL_FFT(OAE, dt, 1e-15);

idx=find(frequencies<1e4);

figure(5),subplot(2,1,2)

plot(frequencies(idx),fft_ampdB(idx))

title ('FFT of OAE')

ylabel('dB')

ylim([0 100])

grid on

path(restorePath);

function Pavel_MAP1_14

% test_MAP1_14 is a general purpose test routine that can be adjusted to

% test a number of different applications of MAP1_14

%

% A range of options are supplied in the early part of the program

%

% One use of the function is to create demonstrations; filenames <demoxx>

%  to illustrate particular features

%

% #1

% Identify the file (in 'MAPparamsName') containing the model parameters

%

% #2

% Identify the kind of model required (in 'AN_spikesOrProbability').

%  A full brainstem model (spikes) can be computed or a shorter model

%  (probability) that computes only so far as the auditory nerve

%

% #3

% Choose between a tone signal or file input (in 'signalType')

%

% #4

% Set the signal rms level (in leveldBSPL)

%

% #5

% Identify the channels in terms of their best frequencies in the vector

%  BFlist.

%

% Last minute changes to the parameters fetched earlier can be made using

%  the cell array of strings 'paramChanges'.

%  Each string must have the same format as the corresponding line in the

%  file identified in 'MAPparamsName'

%

% When the demonstration is satisfactory, freeze it by renaming it <demoxx>

restorePath=path;

addpath (['..' filesep 'MAP'],    ['..' filesep 'wavFileStore'], ...

    ['..' filesep 'utilities'])

%%  #1 parameter file name

MAPparamsName='Normal';

%% #2 probability (fast) or spikes (slow) representation

AN_spikesOrProbability='spikes';

% or

% AN_spikesOrProbability='probability';

% NB probabilities are not corrected for refractory effects

%% #3 pure tone, harmonic sequence or speech file input

signalType= 'tones';

sampleRate= 100000;

duration=0.1;                 % seconds

% toneFrequency= 250:250:8000;    % harmonic sequence (Hz)

toneFrequency= 1000;            % or a pure tone (Hz8

rampDuration=.005;              % seconds

% or

% signalType= 'file';

% fileName='twister_44kHz';

%% #4 rms level

% signal details

leveldBSPL= 30;                  % dB SPL

%% #5 number of channels in the model

%   21-channel model (log spacing)

% numChannels=21;

% lowestBF=250; 	highestBF= 8000;

% BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels));

%   or specify your own channel BFs

numChannels=1;

BFlist=toneFrequency;

%% #6 change model parameters

paramChanges=[];

% or

% Parameter changes can be used to change one or more model parameters

%  *after* the MAPparams file has been read

% This example declares only one fiber type with a calcium clearance time

% constant of 80e-6 s (HSR fiber) when the probability option is selected.

% It also removes the speed up that normally takes place for AN spikes

% It also increases the number of AN fibers computed to 500.

paramChanges={...

    'AN_IHCsynapseParams.ANspeedUpFactor=1;', ...

    'IHCpreSynapseParams.tauCa=86e-6;',...

    'AN_IHCsynapseParams.numFibers=	500;' };

%% delare 'showMap' options to control graphical output

global showMapOptions

% or (example: show everything including an smoothed SACF output

showMapOptions.printModelParameters=1;   % prints all parameters

showMapOptions.showModelOutput=1;       % plot of all stages

showMapOptions.printFiringRates=1;      % prints stage activity levels

showMapOptions.showACF=0;               % shows SACF (probability only)

showMapOptions.showEfferent=0;          % tracks of AR and MOC

showMapOptions.surfProbability=0;       % 2D plot of HSR response 

if strcmp(AN_spikesOrProbability, 'spikes')

    % avoid nonsensical options

    showMapOptions.surfProbability=0;

    showMapOptions.showACF=0;

end

if strcmp(signalType, 'file')

    % needed for labeling plot

    showMapOptions.fileName=fileName;

else

    showMapOptions.fileName=[];

end

%% Generate stimuli

dbstop if error

restorePath=path;

addpath (['..' filesep 'MAP'],    ['..' filesep 'wavFileStore'])

switch signalType

    case 'tones'

        inputSignal=createMultiTone(sampleRate, toneFrequency, ...

            leveldBSPL, duration, rampDuration);

    case 'file'

        %% file input simple or mixed

        [inputSignal sampleRate]=wavread(fileName);

        dt=1/sampleRate;

        inputSignal=inputSignal(:,1);

        targetRMS=20e-6*10^(leveldBSPL/20);

        rms=(mean(inputSignal.^2))^0.5;

        amp=targetRMS/rms;

        inputSignal=inputSignal*amp;

        silence= zeros(1,round(0.1/dt));

        inputSignal= [silence inputSignal' silence];

end

%% run the model

tic

fprintf('\n')

disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)])

disp([num2str(numChannels) ' channel model'])

disp('Computing ...')

MAP1_14(inputSignal, sampleRate, BFlist, ...

    MAPparamsName, AN_spikesOrProbability, paramChanges);

toc

% the model run is now complete. Now display the results

% the model run is now complete. Now display the results

disp(' param changes to list of parameters below')

for i=1:length(paramChanges)

    disp(paramChanges{i})

end

UTIL_showMAP(showMapOptions)

toc

path(restorePath)

function inputSignal=createMultiTone(sampleRate, toneFrequency, ...

    leveldBSPL, duration, rampDuration)

% Create pure tone stimulus

dt=1/sampleRate; % seconds

time=dt: dt: duration;

inputSignal=sum(sin(2*pi*toneFrequency'*time), 1);

amp=10^(leveldBSPL/20)*28e-6;   % converts to Pascals (peak)

inputSignal=amp*inputSignal;

% apply ramps

% catch rampTime error

if rampDuration>0.5*duration, rampDuration=duration/2; end

rampTime=dt:dt:rampDuration;

ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ...

    ones(1,length(time)-length(rampTime))];

inputSignal=inputSignal.*ramp;

ramp=fliplr(ramp);

inputSignal=inputSignal.*ramp;

% add 10 ms silence

silence= zeros(1,round(0.03/dt));

inputSignal= [silence inputSignal silence];

function pitchModel_RM

% Modification of testMAP_14 to replicate the pitch model published

%  in JASA 2006.

%

% A range of options are supplied in the early part of the program

%

% One use of the function is to create demonstrations; filenames <demoxx>

%  to illustrate particular features

%

% #1

% Identify the file (in 'MAPparamsName') containing the model parameters

%

% #2

% Identify the kind of model required (in 'AN_spikesOrProbability').

%  A full brainstem model (spikes) can be computed or a shorter model

%  (probability) that computes only so far as the auditory nerve

%

% #3

% Choose between a tone signal or file input (in 'signalType')

%

% #4

% Set the signal rms level (in leveldBSPL)

%

% #5

% Identify the channels in terms of their best frequencies in the vector

%  BFlist.

%

% Last minute changes to the parameters fetched earlier can be made using

%  the cell array of strings 'paramChanges'.

%  Each string must have the same format as the corresponding line in the

%  file identified in 'MAPparamsName'

%

% When the demonstration is satisfactory, freeze it by renaming it <demoxx>

dbstop if error

restorePath=path;

addpath (['..' filesep 'MAP'],    ['..' filesep 'wavFileStore'], ...

    ['..' filesep 'utilities'])

% Pitch model modification here

global ICrate           % used to collect rate profile from showMAP temporary

rates=[]; F0count=0;

% F0s=[150 200 250];          % fundamental frequency

% harmonics= 3:5;

% F0s=[3000];          % fundamental frequency

F0s=50:5:1000;

harmonics= 1;

% F0s=150;

for F0=F0s

    F0count=F0count+1;

%%  #1 parameter file name

MAPparamsName='Normal';

%% #2 probability (fast) or spikes (slow) representation

AN_spikesOrProbability='spikes';

% or

% NB probabilities are not corrected for refractory effects

% AN_spikesOrProbability='probability';

%% #3 pure tone, harmonic sequence or speech file input

signalType= 'tones';

sampleRate= 50000;

duration=0.50;                 % seconds

% toneFrequency= 1000;            % or a pure tone (Hz8

% F0=210;

toneFrequency= F0*harmonics;  % harmonic sequence (Hz)

rampDuration=.005;              % raised cosine ramp (seconds)

% or

% signalType= 'file';

% fileName='twister_44kHz';

%% #4 rms level

% signal details

leveldBSPL= 50;                  % dB SPL

%% #5 number of channels in the model

%   21-channel model (log spacing)

numChannels=21;

lowestBF=250; 	highestBF= 8000;

BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels));

%   or specify your own channel BFs

% numChannels=1;

BFlist=toneFrequency;

% BFlist=500;

%% #6 change model parameters

paramChanges={...

    'MacGregorMultiParams.currentPerSpike=25e-9;'...

    'MacGregorMultiParams.tauGk=	[0.1e-3:.00005 : 1e-3];'...

'MacGregorParams.currentPerSpike=40e-9;'...

};

%% delare 'showMap' options to control graphical output

showMapOptions.printModelParameters=0;   % prints all parameters

showMapOptions.showModelOutput=1;       % plot of all stages

showMapOptions.printFiringRates=1;      % prints stage activity levels

showMapOptions.showACF=0;               % shows SACF (probability only)

showMapOptions.showEfferent=0;          % tracks of AR and MOC

showMapOptions.surfProbability=0;       % 2D plot of HSR response 

showMapOptions.surfSpikes=0;            % 2D plot of spikes histogram

showMapOptions.ICrates=1;               % IC rates by CNtauGk

% disable certain silly options

if strcmp(AN_spikesOrProbability, 'spikes')

    % avoid nonsensical options

    showMapOptions.surfProbability=0;

    showMapOptions.showACF=0;

else

    showMapOptions.surfSpikes=0;

end

if strcmp(signalType, 'file')

    % needed for labeling plot

    showMapOptions.fileName=fileName;

else

    showMapOptions.fileName=[];

end

%% Generate stimuli

switch signalType

    case 'tones'

        inputSignal=createMultiTone(sampleRate, toneFrequency, ...

            leveldBSPL, duration, rampDuration);

    case 'file'

        %% file input simple or mixed

        [inputSignal sampleRate]=wavread(fileName);

        dt=1/sampleRate;

        inputSignal=inputSignal(:,1);

        targetRMS=20e-6*10^(leveldBSPL/20);

        rms=(mean(inputSignal.^2))^0.5;

        amp=targetRMS/rms;

        inputSignal=inputSignal*amp;

        silence= zeros(1,round(0.1/dt));

        inputSignal= [silence inputSignal' silence];

end

%% run the model

tic

fprintf('\n')

disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)])

disp([num2str(numChannels) ' channel model'])

disp([num2str(F0) ' F0'])

disp('Computing ...')

MAP1_14(inputSignal, sampleRate, BFlist, ...

    MAPparamsName, AN_spikesOrProbability, paramChanges);

% the model run is now complete. Now display the results

UTIL_showMAP(showMapOptions, paramChanges)

%% pitch model Collect and analyse data

% ICrate is global and computed in showMAP

%  a vector of 'stage4' rates; one value for each tauCNGk

rates=[rates; ICrate];

figure(92), imagesc(rates)

ylabel ('F0 no'), xlabel('tauGk')

% figure(92), plot(rates), ylim([0 inf])

h=figure(99); CNmovie(F0count)=getframe(h);

figure(91), plot(rates'),ylim([0 inf])

pause (0.1)

end

%% show results

toc

figure(91), plot(F0s,rates'), xlabel('F0'), ylabel('rate'),ylim([0 inf])

% figure(99),clf,movie(CNmovie,1,4)

path(restorePath)

function inputSignal=createMultiTone(sampleRate, toneFrequency, ...

    leveldBSPL, duration, rampDuration)

% Create pure tone stimulus

dt=1/sampleRate; % seconds

time=dt: dt: duration;

inputSignal=sum(sin(2*pi*toneFrequency'*time), 1);

amp=10^(leveldBSPL/20)*28e-6;   % converts to Pascals (peak)

inputSignal=amp*inputSignal;

% apply ramps

% catch rampTime error

if rampDuration>0.5*duration, rampDuration=duration/2; end

rampTime=dt:dt:rampDuration;

ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ...

    ones(1,length(time)-length(rampTime))];

inputSignal=inputSignal.*ramp;

ramp=fliplr(ramp);

inputSignal=inputSignal.*ramp;

% add 10 ms silence

silence= zeros(1,round(0.005/dt));

inputSignal= [silence inputSignal silence];

function test_speechInNoise

leveldBSPL= 60;                  % dB SPL

leveldBSPLNoise=-55;

paramChanges={};

% no attenuation

paramChanges={'DRNLParams.rateToAttenuationFactorProb = 0.00; '};

% fixed attenuation

% paramChanges={'DRNLParams.rateToAttenuationFactorProb = -0.04;'};

% % dynamic attenuation

paramChanges={'DRNLParams.MOCtauProb =.15;', ...

    'DRNLParams.rateToAttenuationFactorProb = 0.01; '};

% 

fileName='twister_44kHz';

fileName='1o7a_44kHz';

dbstop if error

restorePath=path;

addpath (['..' filesep 'MAP'],    ['..' filesep 'wavFileStore'], ...

    ['..' filesep 'utilities'])

%%  #1 parameter file name

MAPparamsName='Normal';

%% #2 probability (fast) or spikes (slow) representation

AN_spikesOrProbability='spikes';

%   or

AN_spikesOrProbability='probability';

%% #3  speech file input

beginSilence=.25;

endSilence=0.25;

noiseRampDuration=0.01;

%% #5 number of channels in the model

%   21-channel model (log spacing)

numChannels=21;

lowestBF=300; 	highestBF= 6000;

BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels));

%   or specify your own channel BFs

% numChannels=1;

% BFlist=1000;

%% delare 'showMap' options to control graphical output

showMapOptions.printModelParameters=1;   % prints all parameters

showMapOptions.showModelOutput=1;       % plot of all stages

showMapOptions.printFiringRates=1;      % prints stage activity levels

showMapOptions.showACF=0;               % shows SACF (probability only)

showMapOptions.showEfferent=1;          % tracks of AR and MOC

showMapOptions.surfProbability=1;       % 2D plot of HSR response

showMapOptions.surfSpikes=0;            % 2D plot of spikes histogram

showMapOptions.ICrates=0;               % IC rates by CNtauGk

showMapOptions.PSTHbinwidth=0.002;

% disable certain silly options

if strcmp(AN_spikesOrProbability, 'spikes')

    % avoid nonsensical options

    showMapOptions.surfProbability=0;

    showMapOptions.showACF=0;

else

    showMapOptions.surfSpikes=0;

end

    % needed for labeling plot

    showMapOptions.fileName=fileName;

%% Generate stimuli

        %% file input simple or mixed

        [inputSignal sampleRate]=wavread(fileName);

        dt=1/sampleRate;

        inputSignal=inputSignal(:,1);

        targetRMS=20e-6*10^(leveldBSPL/20);

        rms=(mean(inputSignal.^2))^0.5;

        amp=targetRMS/rms;

        inputSignal=inputSignal*amp;

        % add silences

        intialSilence= zeros(1,round(beginSilence*sampleRate));

        finalSilence= zeros(1,round(endSilence*sampleRate));

        inputSignal= [intialSilence inputSignal' finalSilence];

        [inputNoise sampleRateN]=wavread('babble');

        inputNoise=inputNoise(1:length(inputSignal));

       inputNoise=inputNoise(:,1);

        targetRMS=20e-6*10^(leveldBSPLNoise/20);

        rms=(mean(inputNoise.^2))^0.5;

        amp=targetRMS/rms;

        inputNoise=inputNoise*amp;

        time=dt: dt: dt*length(inputNoise);

        rampTime=dt:dt:noiseRampDuration;

        ramp=[0.5*(1+cos(2*pi*rampTime/(2*noiseRampDuration)+pi)) ...

            ones(1,length(time)-length(rampTime))];

        inputNoise=inputNoise'.*ramp;

        inputSignal=inputSignal+inputNoise;

%% run the model

tic

fprintf('\n')

disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)])

disp([num2str(numChannels) ' channel model'])

disp('Computing ...')

MAP1_14(inputSignal, sampleRate, BFlist, ...

    MAPparamsName, AN_spikesOrProbability, paramChanges);

%% the model run is now complete. Now display the results

UTIL_showMAP(showMapOptions, paramChanges)

figure(97), view([-3 82])

title(['speech/ noise: ' num2str([leveldBSPL leveldBSPLNoise])])

disp(['level=' num2str(leveldBSPL)])

disp(['noise level=' num2str(leveldBSPLNoise)])

global DRNLParams

disp(['attenuation factor =' ...

    num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ])

disp(['attenuation factor (probability)=' ...

    num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ])

disp(AN_spikesOrProbability)

disp(paramChanges)

toc

path(restorePath)

% function [LP_SACF dt lags SACF]= testACF

% testACF is a *script* to demonstrate the smoothed ACF of

% Balaguer-Ballestera, E. Denham, S.L. and Meddis, R. (2008).

%

% Convert this to a *function* by uncommenting the first line

%  The function returns the LP_SACF matrix plotted in Figure 96.

%  If a function is used, the following outputs are returned:

%   LP_SACF:  smoothed SACF (lags x time matrix)

%   dt: time interval between successive columns of LP_SACF

%   lags: lags used in computing LP_SACF

%   SACF: unsmoothed SACFs

%

% A range of options are supplied in the early part of the program

%

% #1

% Identify the model parameter file (in 'MAPparamsName')

%

% #2

% Identify the kind of model required (in 'AN_spikesOrProbability')

%  'probability' is recommended for ACF work

%

% #3

% Choose between a harmonic complex or file input

%  by commenting out unwanted code

%

% #4

% Set the signal rms level (in leveldBSPL)

%

% #5

% Identify the model channel BFs in the vector 'BFlist'.

%

% #6

% Last minute changes to the model parameters can be made using

%  the cell array of strings 'paramChanges'.

%  This is used here to control the details of the ACF computations

%  Read the notes in this section for more information

%

% displays:

% Figure 97 shows the AN response to the stimulus. this is a channel x time

%  display. The z-axis (and colour) is the AN fiber firing rate

%

% Figure 96 shows the LP_SACF-matrix, the smoothed SACF.

%

% Figure 89 shows a summary of the evolution of the unsmoothed SACF

%  over time. If you wish to take a snapshot of the LP_SACF-matrix at a

%  particular time, this figure can help identify when to take it.

%  The index on the y-axis, identifies the required row numbers

%    of the LP_SACF or SACF matrix, e.g. LP_SACF(:,2000)

%

%  On request, (filteredSACFParams.plotACFs=1) Figure 89 shows the channel

%   by channel ACFs at intervals during the computation as a movie.

%  The number of ACF displays is controlled by 'plotACFsInterval'

%   and the movie can be slowed or speeded up using 'plotMoviePauses'

%   (see paramChanges section below).

% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

% This global will find results from MAP1_14

global savedInputSignal ANprobRateOutput ANoutput dt dtSpikes savedBFlist

% This global,from model parameter file

global filteredSACFParams

% User sets up requirements

%%  #1 parameter file name

MAPparamsName='Normal';                 % recommended

%% #2 probability (fast) or spikes (slow) representation: select one

% AN_spikesOrProbability='spikes';

%   or

AN_spikesOrProbability='probability';   % recommended

%% #3 A. harmonic sequence or B. speech file input

% Comment out unwanted code

% A. harmonic tone (Hz) - useful to demonstrate a broadband sound

sampleRate= 44100;              % recommended 44100

signalType= 'tones';

duration=0.100;                 % seconds

beginSilence=0.020;

endSilence=0.020;

rampDuration=.005;              % raised cosine ramp (seconds)

% toneFrequency is a vector of component frequencies

F0=120;

toneFrequency= [3*F0 4*F0 5*F0];

%   or

% B. file input

% signalType= 'file';

% fileName='Oh No';

% fileName='twister_44kHz';

%% #4 rms level

leveldBSPL= 100;                  % dB SPL (80 for Lieberman)

%% #5 number of channels in the model

%   21-channel model (log spacing of BFs)

numChannels=21;

lowestBF=250; 	highestBF= 5000;

BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels));

%% #6 change model parameters

% Parameter changes can be used to change one or more model parameters

%  *after* the MAPparams file has been read (see manual)

% Take control of ACF parameters

%  The filteredACF parameters are set in the MAPparamsNormal file

%  However, it is convenient to change them here leving the file intacta

minPitch=	400; maxPitch=	3000; numPitches=200;

maxLag=1/minPitch; minLag=1/maxPitch;

lags= linspace(minLag, maxLag, numPitches);

paramChanges={...

    'filteredSACFParams.lags=lags;     % autocorrelation lags vector;',...

    'filteredSACFParams.acfTau=	2;     % (Wiegrebe) time constant ACF;',...

    'filteredSACFParams.lambda=	0.12;  % slower filter to smooth ACF;',...

    'filteredSACFParams.plotACFs=1;    % plot ACFs while computing;',...

    'filteredSACFParams.plotACFsInterval=0.01;',...

    'filteredSACFParams.plotMoviePauses=.1;  ',...

    'filteredSACFParams.usePressnitzer=0; % attenuates ACF at  long lags;',...

    'filteredSACFParams.lagsProcedure=  ''useAllLags'';',...

    };

% Notes:

% acfTau:   time constant of unsmoothed ACF

% lambda:   time constant of smoothed ACFS

% plotACFs: plot ACFs during computation (0 to switch off, for speed)

% plotACFsInterval: sampling interval for plots

% plotMoviePauses:  pause duration between frames to allow viewing

% usePressnitzer:   gives low weights to long lags

% lagsProcedure:    used to fiddle with output (ignore)

%% delare 'showMap' options to control graphical output

% see UTIL_showMAP for more options

showMapOptions=[];

% showMapOptions.showModelOutput=0;     % plot of all stages

showMapOptions.surfAN=1;                % surface plot of HSR response

showMapOptions.PSTHbinwidth=0.001;      % smoothing for PSTH

if exist('fileName','var')

    % needed for labeling plot

    showMapOptions.fileName=fileName;

end

%% Generate stimuli

switch signalType

    case 'tones'

        % Create tone stimulus

        dt=1/sampleRate; % seconds

        time=dt: dt: duration;

        inputSignal=sum(sin(2*pi*toneFrequency'*time), 1);

        amp=10^(leveldBSPL/20)*28e-6;   % converts to Pascals (peak)

        inputSignal=amp*inputSignal;

        % apply ramps

        % catch rampTime error

        if rampDuration>0.5*duration, rampDuration=duration/2; end

        rampTime=dt:dt:rampDuration;

        ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ...

            ones(1,length(time)-length(rampTime))];

        inputSignal=inputSignal.*ramp;

        ramp=fliplr(ramp);

        inputSignal=inputSignal.*ramp;

        % add silence

        intialSilence= zeros(1,round(beginSilence/dt));

        finalSilence= zeros(1,round(endSilence/dt));

        inputSignal= [intialSilence inputSignal finalSilence];

    case 'file'

        %% file input simple or mixed

        [inputSignal sampleRate]=wavread(fileName);

        dt=1/sampleRate;

        inputSignal=inputSignal(:,1);

        targetRMS=20e-6*10^(leveldBSPL/20);

        rms=(mean(inputSignal.^2))^0.5;

        amp=targetRMS/rms;

        inputSignal=inputSignal*amp;

end

wavplay(inputSignal, sampleRate)

%% run the model

dbstop if error

restorePath=path;

addpath (['..' filesep 'MAP'],    ['..' filesep 'wavFileStore'], ...

    ['..' filesep 'utilities'])

fprintf('\n')

disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)])

disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability])

disp('Computing MAP ...')

MAP1_14(inputSignal, sampleRate, BFlist, ...

    MAPparamsName, AN_spikesOrProbability, paramChanges);

%% The model run is now complete. Now display the results

% display the AN response

UTIL_showMAP(showMapOptions)

% compute ACF

switch AN_spikesOrProbability

    case 'probability'

        % use only HSR fibers

        inputToACF=ANprobRateOutput(end-length(savedBFlist)+1:end,:);

    otherwise

        inputToACF=ANoutput;

        dt=dtSpikes;

end

disp ('computing ACF...')

% read paramChanges to get new filteredSACFParams

for i=1:length(paramChanges)

    eval(paramChanges{i});

end

[LP_SACF BFlist SACF]= filteredSACF(inputToACF, dt, savedBFlist, ...

    filteredSACFParams);

disp(' ACF done.')

%% plot original waveform on summary/smoothed ACF plot

figure(96), clf

subplot(3,1,3)

t=dt*(1:length(savedInputSignal));

plot(t,savedInputSignal, 'k')

xlim([0 t(end)])

title(['stimulus: ' num2str(leveldBSPL, '%4.0f') ' dB SPL']);

% plot SACF

figure(96)

subplot(2,1,1)

imagesc(LP_SACF)

colormap bone

ylabel('periodicities (Hz)'), xlabel('time (s)')

title(['smoothed SACF. (periodicity x time)'])

% y-axis specifies pitches (1/lags)

% Force MATLAB to show the lowest pitch

postedYvalues=[1 get(gca,'ytick')]; set(gca,'ytick',postedYvalues)

pitches=1./filteredSACFParams.lags;

set(gca,'ytickLabel', round(pitches(postedYvalues)))

% x-axis is time at which LP_SACF is samples

[nCH nTimes]=size(LP_SACF);

t=dt:dt:dt*nTimes;

tt=get(gca,'xtick');

set(gca,'xtickLabel', round(100*t(tt))/100)

%% On a new figure show a cascade of SACFs

figure(89), clf

% select 100 samples;

[r c]=size(SACF);

step=round(c/100);

idx=step:step:c;

UTIL_cascadePlot(SACF(:,idx)', 1./pitches)

xlabel('lag (s)'), ylabel('time pointer -->')

title(' SACF summary over time')

yValues=get(gca,'yTick');

set(gca,'yTickLabel', num2str(yValues'*100))

path(restorePath)

% testDPOAE

addpath (['..' filesep 'testPrograms'])

leveldB=60;

f1=3000;

frequencyDiffs=20:20:1000;

result=[];

frequenciesSoFar=[];

for f2=f1+frequencyDiffs

    [frequencies fft_ampdB]=testDPOAE (leveldB, [f1 f2]);

    dpFreq=2*f1-f2;

    [a idx]=min((frequencies-dpFreq).^2);

    result=[result fft_ampdB(idx)];

    frequenciesSoFar=[frequenciesSoFar dpFreq];

    figure(4), plot(frequenciesSoFar, result)

    title(['F1= ' num2str(f1) '  F2= ' ...

        num2str(f1+ [min(frequencyDiffs) max(frequencyDiffs)])...

        '  leveldB= ' num2str(leveldB)])

    xlabel('DP (2f1- f2) frequency'), ylim([0 100])

end

grid on

disp(result)

function test_Dolan_and_Nuttall

% test_MAP1_14 is a general purpose test routine that can be adjusted to

% test a number of different applications of MAP1_14

%

% A range of options are supplied in the early part of the program

%

% One use of the function is to create demonstrations; filenames <demoxx>

%  to illustrate particular features

%

% #1

% Identify the file (in 'MAPparamsName') containing the model parameters

%

% #2

% Identify the kind of model required (in 'AN_spikesOrProbability').

%  A full brainstem model (spikes) can be computed or a shorter model

%  (probability) that computes only so far as the auditory nerve

%

% #3

% Choose between a tone signal or file input (in 'signalType')

%

% #4

% Set the signal rms level (in leveldBSPL)

%

% #5

% Identify the channels in terms of their best frequencies in the vector

%  BFlist.

%

% Last minute changes to the parameters fetched earlier can be made using

%  the cell array of strings 'paramChanges'.

%  Each string must have the same format as the corresponding line in the

%  file identified in 'MAPparamsName'

%

% When the demonstration is satisfactory, freeze it by renaming it <demoxx>

global dt dtSpikes  savedBFlist saveAN_spikesOrProbability saveMAPparamsName...

    savedInputSignal OMEextEarPressure TMoutput OMEoutput ARattenuation ...

    DRNLoutput IHC_cilia_output IHCrestingCiliaCond IHCrestingV...

    IHCoutput ANprobRateOutput ANoutput savePavailable ANtauCas  ...

    CNtauGk CNoutput  ICoutput ICmembraneOutput ICfiberTypeRates ...

    MOCattenuation

global OMEParams DRNLParams IHC_cilia_RPParams IHCpreSynapseParams

global AN_IHCsynapseParams MacGregorParams MacGregorMultiParams

global ICrate

dbstop if error

restorePath=path;

addpath (['..' filesep 'MAP'],    ['..' filesep 'wavFileStore'], ...

    ['..' filesep 'utilities'])

%%  #1 parameter file name

MAPparamsName='Normal';

%% #2 probability (fast) or spikes (slow) representation

AN_spikesOrProbability='spikes';

%   or

% AN_spikesOrProbability='probability';

%% #3 pure tone, harmonic sequence or speech file input

signalType= 'tones';

toneFrequency= 4000;            % or a pure tone (Hz)

sampleRate= 44100;          % must agree with noise

duration=0.010;                 % seconds

beginSilence=0.010;

endSilence=0.010;

rampDuration=.001;              % raised cosine ramp (seconds)

noiseRampDuration=0.002;

%   or

% harmonic sequence (Hz)

% F0=210;

% toneFrequency= F0:F0:8000;

%   or

% signalType= 'file';

% fileName='twister_44kHz';

% %% #4 rms level

% % signal details

% leveldBSPL= 80;                  % dB SPL (80 for Lieberman)

% leveldBSPLNoise=-30;

%% #5 number of channels in the model

%   21-channel model (log spacing)

numChannels=21;

lowestBF=250; 	highestBF= 8000;

BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels));

% %   or specify your own channel BFs

% numChannels=1;

% BFlist=toneFrequency;

%% #6 change model parameters

paramChanges={};

% Parameter changes can be used to change one or more model parameters

%  *after* the MAPparams file has been read

% This example declares only one fiber type with a calcium clearance time

% constant of 80e-6 s (HSR fiber) when the probability option is selected.

% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ...

%     'IHCpreSynapseParams.tauCa=86e-6; '};

% paramChanges={'DRNLParams.MOCtauProb =.25;', ...

%     'DRNLParams.rateToAttenuationFactorProb = 0.02; '};

paramChanges={'AN_IHCsynapseParams.numFibers=	50; ',...

    'DRNLParams.MOCtauProb =.15;', ...

    'DRNLParams.rateToAttenuationFactorProb = 0.00; '};

% paramChanges={'AN_IHCsynapseParams.numFibers=	50; ',...

% 'DRNLParams.rateToAttenuationFactorProb = -0.007;'};

%% delare 'showMap' options to control graphical output

showMapOptions.printModelParameters=1;   % prints all parameters

showMapOptions.showModelOutput=0;       % plot of all stages

showMapOptions.printFiringRates=1;      % prints stage activity levels

showMapOptions.showACF=0;               % shows SACF (probability only)

showMapOptions.showEfferent=1;          % tracks of AR and MOC

showMapOptions.surfProbability=1;       % 2D plot of HSR response

showMapOptions.surfSpikes=1;            % 2D plot of spikes histogram

showMapOptions.ICrates=0;               % IC rates by CNtauGk

% disable certain silly options

if strcmp(AN_spikesOrProbability, 'spikes')

    % avoid nonsensical options

    showMapOptions.surfProbability=0;

    showMapOptions.showACF=0;

end

if strcmp(signalType, 'file')

    % needed for labeling plot

    showMapOptions.fileName=fileName;

else

    showMapOptions.fileName=[];

end

fprintf('\n')

disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability])

disp('Computing ...')

%%systematic

probeLevels=30:10:80;

noiseLevels=[-100 30];

noRepeats=10;

% probeLevels=80;

% noiseLevels=[-30];

% noRepeats=10;

peakCAPs=zeros(4,length(probeLevels));

for noiseCondition=1:length(noiseLevels)

    leveldBSPLNoise=noiseLevels(noiseCondition);

    levelNo=0;

    for probeLevel=probeLevels

        leveldBSPL=probeLevel;

        levelNo=levelNo+1;

        summedCAP=[];

        for repeatNo= 1:noRepeats

            disp(['repeat no: ' num2str(repeatNo)])

            %% Generate stimuli

            switch signalType

                case 'tones'

                    % Create pure tone stimulus

                    dt=1/sampleRate; % seconds

                    time=dt: dt: duration;

                    inputSignal=sum(sin(2*pi*toneFrequency'*time), 1);

                    amp=10^(leveldBSPL/20)*28e-6;   % converts to Pascals (peak)

                    inputSignal=amp*inputSignal;

                    % apply ramps

                    % catch rampTime error

                    if rampDuration>0.5*duration, rampDuration=duration/2; end

                    rampTime=dt:dt:rampDuration;

                    ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ...

                        ones(1,length(time)-length(rampTime))];

                    inputSignal=inputSignal.*ramp;

                    ramp=fliplr(ramp);

                    inputSignal=inputSignal.*ramp;

                    % add silence

                    intialSilence= zeros(1,round(beginSilence/dt));

                    finalSilence= zeros(1,round(endSilence/dt));

                    inputSignal= [intialSilence inputSignal finalSilence];

                    %         [inputNoise sampleRateN]=wavread('babble');

                    [inputNoise sampleRateN]=wavread('white noise');

                    inputNoise=inputNoise(1:length(inputSignal));

                    inputNoise=inputNoise(:,1);

                    targetRMS=20e-6*10^(leveldBSPLNoise/20);

                    rms=(mean(inputNoise.^2))^0.5;

                    amp=targetRMS/rms;

                    inputNoise=inputNoise*amp;

                    time=dt: dt: dt*length(inputNoise);

                    rampTime=dt:dt:noiseRampDuration;

                    ramp=[0.5*(1+cos(2*pi*rampTime/(2*noiseRampDuration)+pi)) ...

                        ones(1,length(time)-length(rampTime))];

                    inputNoise=inputNoise'.*ramp;

                    ramp=fliplr(ramp);

                    inputNoise=inputNoise.*ramp;

                    inputSignal=inputSignal+inputNoise;

                    intialSilence= zeros(1,round(beginSilence/dt));

                    finalSilence= zeros(1,round(endSilence/dt));

                    inputSignal= [intialSilence inputSignal finalSilence];

                    toneOnset=2*beginSilence;

                    figure(2), subplot(3,1,1)

                    time=dt:dt:dt*length(inputSignal);

                    plot(time,inputSignal,'k')

                case 'file'

                    %% file input simple or mixed

                    [inputSignal sampleRate]=wavread(fileName);

                    dt=1/sampleRate;

                    inputSignal=inputSignal(:,1);

                    targetRMS=20e-6*10^(leveldBSPL/20);

                    rms=(mean(inputSignal.^2))^0.5;

                    amp=targetRMS/rms;

                    inputSignal=inputSignal*amp;

                    intialSilence= zeros(1,round(0.1/dt));

                    finalSilence= zeros(1,round(0.2/dt));

                    inputSignal= [intialSilence inputSignal' finalSilence];

            end

            %% run the model

            tic

            MAP1_14(inputSignal, sampleRate, BFlist, ...

                MAPparamsName, AN_spikesOrProbability, paramChanges);

            %% the model run is now complete. Now display the results

                %                 UTIL_showMAP(showMapOptions, paramChanges)

                wholeNerveCAP  = UTIL_CAPgenerator...

                    (ANoutput, dtSpikes, BFlist, AN_IHCsynapseParams.numFibers, 1);

                if isempty(summedCAP)

                    summedCAP=wholeNerveCAP;

                else

                    summedCAP=summedCAP+wholeNerveCAP;

                end

                switch AN_spikesOrProbability

                    case 'spikes'

                        ANoutput = sum(ANoutput, 1);

                    case 'probability'

                        ANoutput = ANprobRateOutput(13+21,:);

                end

                figure(2), subplot(3,1,2), plot(ANoutput)

                spikeTimes=dtSpikes:dtSpikes:dtSpikes* length(wholeNerveCAP);

                figure(2), subplot(3,1,3), plot(spikeTimes,summedCAP/repeatNo)

                ylim([-50 50])

        end % repeat

        spikeTimes=dtSpikes:dtSpikes:dtSpikes* length(wholeNerveCAP);

        idx=find(spikeTimes>toneOnset & ...

            spikeTimes>toneOnset+duration+.005);

        averageCAP=summedCAP/repeatNo;

        peakCAP=max(averageCAP(idx));

        peakCAPs(noiseCondition,levelNo)=peakCAPs(noiseCondition,levelNo)+ peakCAP;

        if strcmp(signalType,'tones')

            disp(['duration=' num2str(duration)])

            disp(['level=' num2str(leveldBSPL)])

            disp(['toneFrequency=' num2str(toneFrequency)])

            disp(['leveldBSPLNoise=' num2str(leveldBSPLNoise)])

            disp(['attenuation factor =' ...

                num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ])

            disp(['attenuation factor (probability)=' ...

                num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ])

            disp(AN_spikesOrProbability)

        end

        disp([ 'peak CAP ' num2str(peakCAP)])

        for i=1:length(paramChanges)

            disp(paramChanges{i})

        end

    end % probe level

    figure(9), subplot(3,1,3), plot(probeLevels,peakCAPs)

end % condition

%%

path(restorePath)

function test_MAP1_14Hopkins

% test_MAP1_14 is a general purpose test routine that can be adjusted to

% test a number of different applications of MAP1_14

%

% A range of options are supplied in the early part of the program

%

% One use of the function is to create demonstrations; filenames <demoxx>

%  to illustrate particular features

%

% #1

% Identify the file (in 'MAPparamsName') containing the model parameters

%

% #2

% Identify the kind of model required (in 'AN_spikesOrProbability').

%  A full brainstem model (spikes) can be computed or a shorter model

%  (probability) that computes only so far as the auditory nerve

%

% #3

% Choose between a tone signal or file input (in 'signalType')

%

% #4

% Set the signal rms level (in leveldBSPL)

%

% #5

% Identify the channels in terms of their best frequencies in the vector

%  BFlist.

%

% Last minute changes to the parameters fetched earlier can be made using

%  the cell array of strings 'paramChanges'.

%  Each string must have the same format as the corresponding line in the

%  file identified in 'MAPparamsName'

%

% When the demonstration is satisfactory, freeze it by renaming it <demoxx>

dbstop if error

restorePath=path;

addpath (['..' filesep 'MAP'],    ['..' filesep 'wavFileStore'], ...

    ['..' filesep 'utilities'])

%%  #1 parameter file name

MAPparamsName='Normal';

%% #2 probability (fast) or spikes (slow) representation

AN_spikesOrProbability='spikes';

%   or

AN_spikesOrProbability='probability';

%% #3 pure tone, harmonic sequence or speech file input

signalType= 'tones';

sampleRate= 50000;

duration=0.100;                 % seconds

rampDuration=.005;              % raised cosine ramp (seconds)

beginSilence=0.050;               

endSilence=0.050;                  

toneFrequency= 1000;            % or a pure tone (Hz)

%   or

% harmonic sequence (Hz)

F0=1000/11;

toneFrequency= 10*F0:F0:12*F0;    

%   or

% signalType= 'file';

% fileName='twister_44kHz';

%% #4 rms level

% signal details

leveldBSPL= [44 50 44];                  % dB SPL (80 for Lieberman)

leveldBSPL= [50 50 50];                  % dB SPL (80 for Lieberman)

noiseLevel=-35;

%% #5 number of channels in the model

%   21-channel model (log spacing)

numChannels=21;

lowestBF=500; 	highestBF= 2000;

BFlist=round(logspace(log10(lowestBF), log10(highestBF), numChannels));

%   or specify your own channel BFs

numChannels=1;

BFlist=1000;

%% #6 change model parameters

paramChanges={};

% Parameter changes can be used to change one or more model parameters

%  *after* the MAPparams file has been read

% This example declares only one fiber type with a calcium clearance time

% constant of 80e-6 s (HSR fiber) when the probability option is selected.

% paramChanges={'AN_IHCsynapseParams.ANspeedUpFactor=5;', ...

%     'IHCpreSynapseParams.tauCa=86e-6; '};

%% delare 'showMap' options to control graphical output

showMapOptions.printModelParameters=1;   % prints all parameters

showMapOptions.showModelOutput=1;       % plot of all stages

showMapOptions.printFiringRates=1;      % prints stage activity levels

showMapOptions.showACF=0;               % shows SACF (probability only)

showMapOptions.showEfferent=0;          % tracks of AR and MOC

showMapOptions.surfProbability=1;       % 2D plot of HSR response 

showMapOptions.surfSpikes=0;            % 2D plot of spikes histogram

showMapOptions.ICrates=0;               % IC rates by CNtauGk

showMapOptions.PSTHbinwidth=0.001;

showMapOptions.colorbar=0;

showMapOptions.view=[0 90];

% disable certain silly options

if strcmp(AN_spikesOrProbability, 'spikes')

    % avoid nonsensical options

    showMapOptions.surfProbability=0;

    showMapOptions.showACF=0;

end

if strcmp(signalType, 'file')

    % needed for labeling plot

    showMapOptions.fileName=fileName;

else

    showMapOptions.fileName=[];

end

%% Generate stimuli

switch signalType

    case 'tones'

        % Create pure tone stimulus

        dt=1/sampleRate; % seconds

        time=dt: dt: duration;

        amp=10.^(leveldBSPL/20)*28e-6;   % converts to Pascals (peak)

        inputSignal=sin(2*pi*toneFrequency'*time);

        amps=repmat(amp',1,length(time));

        inputSignal=amps.*inputSignal;

        inputSignal=sum(inputSignal, 1);

        % apply ramps

        % catch rampTime error

        if rampDuration>0.5*duration, rampDuration=duration/2; end

        rampTime=dt:dt:rampDuration;

        ramp=[0.5*(1+cos(2*pi*rampTime/(2*rampDuration)+pi)) ...

            ones(1,length(time)-length(rampTime))];

        inputSignal=inputSignal.*ramp;

        ramp=fliplr(ramp);

        inputSignal=inputSignal.*ramp;

        % add silence

        intialSilence= zeros(1,round(beginSilence/dt));

        finalSilence= zeros(1,round(endSilence/dt));

        inputSignal= [intialSilence inputSignal finalSilence];

        %% TEN noise input simple or mixed

        [noise sampleRate]=wavread('TEN.wav');

        dt=1/sampleRate;

        noise=noise(:,1);

        targetRMS=20e-6*10^(noiseLevel/20);

        rms=(mean(noise.^2))^0.5;

        amp=targetRMS/rms;

        noise=noise*amp;

        inputSignal=inputSignal+noise(1:length(inputSignal))';

end

%% run the model

tic

fprintf('\n')

disp(['Signal duration= ' num2str(length(inputSignal)/sampleRate)])

disp([num2str(numChannels) ' channel model: ' AN_spikesOrProbability])

disp('Computing ...')

MAP1_14(inputSignal, sampleRate, BFlist, ...

    MAPparamsName, AN_spikesOrProbability, paramChanges);

%% the model run is now complete. Now display the results

UTIL_showMAP(showMapOptions, paramChanges)

figure(97)

title (['tones / noise levels: ' num2str([leveldBSPL noiseLevel])])

if strcmp(signalType,'tones')

    disp(['duration=' num2str(duration)])

    disp(['level=' num2str(leveldBSPL)])

    disp(['toneFrequency=' num2str(toneFrequency)])

    global DRNLParams

    disp(['attenuation factor =' ...

        num2str(DRNLParams.rateToAttenuationFactor, '%5.3f') ])

    disp(['attenuation factor (probability)=' ...

        num2str(DRNLParams.rateToAttenuationFactorProb, '%5.3f') ])

    disp(AN_spikesOrProbability)

end

disp(paramChanges)

toc

path(restorePath)

function test_DRNL_Ruggero97

% test_DRNL_Ruggero97 attempts to match Ruggero's (1992 and 1997)

%  iso-intensity data by fiddling with the parameters

% # BF is the BF of the filter to be assessed

BF=9000;

% # test frequencies. check that BF is one of them

%    copy Ruggero's test tones as fara as possible

numFs=6; lowestF=4000; 	highestF= 11030;

toneFrequencyList=round(logspace(log10(lowestF), log10(highestF), numFs));

%  # parameter file name. this is the base set of parameters

MAPparamsName='Normal';

% # probability representation (not directly relevant here as only

%    the DRNL output is used

AN_spikesOrProbability='probability';

% # tone characteristics

sampleRate= 100000;

duration=0.0200;                % Ruggero uses 5, 10, 25 ms tones

rampDuration=0.0015;              % raised cosine ramp (seconds)

beginSilence=0.050;

endSilence=0.020;

% # levels

levels=[3 10:10:80];

%% # change model parameters

% Parameter changes can be used to change one or more model parameters

%  *after* the MAPparams file has been read

paramChanges={};

% switch off all efferent effects and then play with DRNL params

paramChanges={...

    'DRNLParams.rateToAttenuationFactorProb = 0.00; ',...

    'OMEParams.rateToAttenuationFactorProb=0.0;', ...

    'DRNLParams.ctBMdB = -20;'...

    'DRNLParams.g=1000;'...

    'DRNLParams.linCFs=7000;'...

    'DRNLParams.linBWs=3500;'...

    };