MAP

nANchannels= nANfiberTypes*nBFs;

synapticCa= zeros(nANchannels,segmentLength);

mICa=zeros(nANchannels,segmentLength);

tauCa=reshape(tauCa, nANchannels, 1);

% presynapse startup values (vectors, length:nANchannels)

nANfibers= nANchannels*nFibersPerChannel;

PAN_ydt = repmat(AN_IHCsynapseParams.y*dt, nANchannels,1);

PAN_ldt = repmat(AN_IHCsynapseParams.l*dt, nANchannels,1);

PAN_xdt = repmat(AN_IHCsynapseParams.x*dt, nANchannels,1);

PAN_rdt = repmat(AN_IHCsynapseParams.r*dt, nANchannels,1);

ANprobability=zeros(nANchannels,segmentLength);

ANprobRateOutput=zeros(nANchannels,signalLength);

savePavailableSeg=zeros(nANchannels,segmentLength);

savePavailable=zeros(nANchannels,signalLength);

% If there is more than one value, everything is replicated accordingly

ANfibersFanInToCN=MacGregorMultiParams.fibersPerNeuron;

CNtauGk=MacGregorMultiParams.tauGk; % row vector of CN types (by tauGk)

nCNtauGk=length(CNtauGk);

% the total number of 'channels' is now greater

nCNchannels=nANchannels*nCNtauGk;

nCNneuronsPerChannel=MacGregorMultiParams.nNeuronsPerBF;

tauGk=repmat(CNtauGk, nCNneuronsPerChannel,1);

tauGk=reshape(tauGk,nCNneuronsPerChannel*nCNtauGk,1);

% Now the number of neurons has been increased

nCNneurons=nCNneuronsPerChannel*nCNchannels;

CNinputfiberLists=zeros(nANchannels*nCNneuronsPerChannel, ANfibersFanInToCN);

for ch=1:nANchannels

        for idx2=1:nCNtauGk

            fibersUsed=(ch-1)*ANavailableFibersPerChan + ...

                ceil(rand(1,ANfibersFanInToCN)* ANavailableFibersPerChan);

            CNinputfiberLists(unitNo,:)=fibersUsed;

            unitNo=unitNo+1;

        end

tauGk=repmat(tauGk,nANchannels,1);

nICcells=nANchannels*nCNtauGk;  % one cell per channel

CN_PSTH=zeros(nICcells ,reducedSegmentLength);

ICoutput=false(nICcells,reducedSignalLength);

                releaseProb=reshape(releaseProb, nFibersPerChannel*nANchannels,1);

            for ch=1:nCNchannels

                % combine CN neurons in same channel, 

                %  i.e. same BF & same tauCa

% function setLocationOfGUIs(handles)

% global checkForPreviousGUI  % holds screen positioning across repeated calls

% scrnsize=get(0,'screensize');

% checkForPreviousGUI=[];

% % if isstruct(checkForPreviousGUI)...

% %         && checkForPreviousGUI.GUIalreadyStarted==1 ...

% %         && isfield(checkForPreviousGUI,'GUIposition')

% %     set(handles.figure1,'position',checkForPreviousGUI.GUIposition)

% % else

% %     % relocate the GUI only if this is the first time of use

% %     set(0, 'units','pixels')

% %     % occupies top to bottom of screen but only 60% width

% %     % [left bottom width height]

% %     firstPos=[0.01*scrnsize(4) 0.03*scrnsize(3) 0.6*scrnsize(3) 0.92*scrnsize(4)];

% %     firstPos=[4 0.045*scrnsize(4) 0.6*scrnsize(3) 0.93*scrnsize(4)];

% %     set(handles.figure1, 'units','pixels')

% %     set(handles.figure1,'position',firstPos)

% %     checkForPreviousGUI.GUIalreadyStarted=1;

% %     checkForPreviousGUI.GUIposition=firstPos;

% % end

% set(handles.figure1,'color',[.871 .961 .996])

% set(handles.figure1,'name', pwd)

% 

% % MAP model figure; sits alongside GUI if requested

% figure(99)

% % [left bottom width height]

% MAPpos=[0.615*scrnsize(3) 0.05*scrnsize(4) 0.15*scrnsize(3) 0.85*scrnsize(4)];

% % visible only on request.

% set(gcf,'position',MAPpos , 'visible','off')

% 

% Specify order of fields in main structures

% identify as empty values or empty strings only

addpath ('paradigms')   %paradigm informations is stored here

addpath (['..' filesep 'testPrograms']) % model physiology tests

%  (names of variable that can changed between runs)

% populate the 'between runs variable' menus

set(handles.editBackgroundLevel,'string', '0')

paradigmNames= paradigmNames.m; % select m files only

for i=1:length(paradigmNames)   % strip off file extension

    paradigmNames{i}=paradigmNames{i}(10:end-2); 

end

    % training paradigm must exist

% phase

set(handles.popupmenuPhase,'value', 1)

set(handles.popupmenuCueNoCue, 'value', 1);

set(handles.editMsgFont,'string','10')

if ~isfield(experiment,'maskerInUse')

    error('selected paradigm does not specify if masker is used')

end

if ~experiment.maskerInUse

    stimulusParameters.maskerType='tone';

    stimulusParameters.maskerPhase='sin';

    stimulusParameters.maskerDuration=0.0;

    stimulusParameters.maskerLevel= -50;

    stimulusParameters.maskerRelativeFrequency= 1 ;

% set(handles.popupmenuRandomize,'value', betweenRuns.randomizeSequence)

sequenceOptions=get(handles.popupmenuRandomize,'string');

idx=find(strcmp(betweenRuns.randomizeSequence, sequenceOptions)==1);

set(handles.popupmenuRandomize,'value', idx)

phaseOptions=get(handles.popupmenuPhase,'string');

idx=find(strcmp(stimulusParameters.maskerPhase, phaseOptions)==1);

set(handles.popupmenuPhase,'value', idx)

experiment.stop=0;

        % {'randomize within blocks', 'fixed sequence',...

        %  'randomize across blocks'}

experiment.maxLogisticK=[];

experiment.numPossLogisticK=[];

experiment.stop=	[];

withinRuns.nowInPhase2=[];

withinRuns.beginningOfPhase2=[];

withinRuns.babblePlaying=[];

    round(globalStimParams.overallDuration*globalStimParams.FS);

experiment.maskerInUse=1;

experiment.maskerInUse=1;

stimulusParameters.maskerType='tone';

stimulusParameters.maskerPhase='sin';

experiment.maskerInUse=0;

experiment.maskerInUse=0;

experiment.maskerInUse=1;

experiment.maskerInUse=0;

experiment.maskerInUse=0;

% 'randomize within blocks', 'fixed sequence', 'randomize across blocks'

betweenRuns.randomizeSequence='fixed sequence'; 

experiment.maskerInUse=0;

betweenRuns.variableList2=[80 60 40 20];

experiment.maskerInUse=1;

stimulusParameters.maskerPhase='cos';

stimulusParameters.maskerLevel=20;

global stimulusParameters  betweenRuns experiment

experiment.maskerInUse=1;

experiment.maskerInUse=0;

experiment.maskerInUse=0;

% 'randomize within blocks', 'fixed sequence', 'randomize across blocks'

betweenRuns.randomizeSequence='randomize within blocks'; 

experiment.maskerInUse=0;

stimulusParameters.maskerPhase='cos';

stimulusParameters.maskerRelativeFrequency= 1 ; 

stimulusParameters.targetPhase='cos'; %{'sin','cos','alt','rand'}

experiment.maskerInUse=1;

    % {'randomize within blocks', 'fixed sequence',...

    %  'randomize across blocks'}

function method=MAPparamsNormalIC ...

    (BFlist, sampleRate, showParams, paramChanges)

% MAPparams<> establishes a complete set of MAP parameters

% Parameter file names must be of the form <MAPparams> <name>

%

% input arguments

%  BFlist     (optional) specifies the desired list of channel BFs

%    otherwise defaults set below

%  sampleRate (optional), default is 50000.

%  showParams (optional) =1 prints out the complete set of parameters

% output argument

%  method passes a miscelleny of values

global inputStimulusParams OMEParams DRNLParams IHC_cilia_RPParams

global IHC_VResp_VivoParams IHCpreSynapseParams  AN_IHCsynapseParams

global MacGregorParams MacGregorMultiParams  filteredSACFParams

global experiment % used by calls from multiThreshold only

currentFile=mfilename;                      % i.e. the name of this mfile

method.parameterSource=currentFile(10:end); % for the record

efferentDelay=0.010;

method.segmentDuration=efferentDelay;

if nargin<3, showParams=0; end

if nargin<2, sampleRate=50000; end

if nargin<1 || BFlist(1)<0 % if BFlist= -1, set BFlist to default

    lowestBF=250; 	highestBF= 8000; 	numChannels=21;

    % 21 chs (250-8k)includes BFs at 250 500 1000 2000 4000 8000

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

end

% BFlist=1000;

% preserve for backward campatibility

method.nonlinCF=BFlist; 

method.dt=1/sampleRate; 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% set  model parameters

%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%  #1 inputStimulus

inputStimulusParams=[];

inputStimulusParams.sampleRate= sampleRate; 

%%  #2 outerMiddleEar

OMEParams=[];  % clear the structure first

% outer ear resonances band pass filter  [gain lp order  hp]

OMEParams.externalResonanceFilters=      [ 10 1 1000 4000];

% highpass stapes filter  

%  Huber gives 2e-9 m at 80 dB and 1 kHz (2e-13 at 0 dB SPL)

OMEParams.OMEstapesLPcutoff= 1000;

OMEParams.stapesScalar=	     45e-9;

% Acoustic reflex: maximum attenuation should be around 25 dB Price (1966)

% i.e. a minimum ratio of 0.056.

% 'spikes' model: AR based on brainstem spiking activity (LSR)

OMEParams.rateToAttenuationFactor=0.006;   % * N(all ICspikes)

% OMEParams.rateToAttenuationFactor=0;   % * N(all ICspikes)

% 'probability model': Ar based on AN firing probabilities (LSR)

OMEParams.rateToAttenuationFactorProb=0.01;% * N(all ANrates)

% OMEParams.rateToAttenuationFactorProb=0;% * N(all ANrates)

% asymptote should be around 100-200 ms

OMEParams.ARtau=.05; % AR smoothing function

% delay must be longer than the segment length

OMEParams.ARdelay=efferentDelay;  %Moss gives 8.5 ms latency

OMEParams.ARrateThreshold=0;

%%  #3 DRNL

DRNLParams=[];  % clear the structure first

DRNLParams.BFlist=BFlist;

% DRNL nonlinear path

DRNLParams.a=5e4;     % DRNL.a=0 means no OHCs (no nonlinear path)

DRNLParams.a=2e4;     % DRNL.a=0 means no OHCs (no nonlinear path)

DRNLParams.b=8e-6;    % *compression threshold raised compression

% DRNLParams.b=1;    % b=1 means no compression

DRNLParams.c=0.2;     % compression exponent

% nonlinear filters

DRNLParams.nonlinCFs=BFlist;

DRNLParams.nonlinOrder=	3;           % order of nonlinear gammatone filters

p=0.2895;   q=170;  % human  (% p=0.14;   q=366;  % cat)

DRNLParams.nlBWs=  p * BFlist + q;

DRNLParams.p=p;   DRNLParams.q=q;   % save p and q for printing only

% DRNL linear path:

DRNLParams.g=100;     % linear path gain factor

% linCF is not necessarily the same as nonlinCF

minLinCF=153.13; coeffLinCF=0.7341;   % linCF>nonlinBF for BF < 1 kHz

DRNLParams.linCFs=minLinCF+coeffLinCF*BFlist;

DRNLParams.linOrder=	3;           % order of linear gammatone filters

minLinBW=100; coeffLinBW=0.6531;

DRNLParams.linBWs=minLinBW + coeffLinBW*BFlist; % bandwidths of linear  filters

% DRNL MOC efferents

DRNLParams.MOCdelay = efferentDelay;            % must be < segment length!

% 'spikes' model: MOC based on brainstem spiking activity (HSR)

DRNLParams.rateToAttenuationFactor = .01;  % strength of MOC

%      DRNLParams.rateToAttenuationFactor = 0;  % strength of MOC

% 'probability' model: MOC based on AN spiking activity (HSR)

DRNLParams.rateToAttenuationFactorProb = .0055;  % strength of MOC

% DRNLParams.rateToAttenuationFactorProb = .0;  % strength of MOC

DRNLParams.MOCrateThresholdProb =70;                % spikes/s probability only

DRNLParams.MOCtau =.1;                         % smoothing for MOC

%% #4 IHC_cilia_RPParams

IHC_cilia_RPParams.tc=	0.0003;   % 0.0003 filter time simulates viscocity

% IHC_cilia_RPParams.tc=	0.0005;   % 0.0003 filter time simulates viscocity

IHC_cilia_RPParams.C=	0.03;      % 0.1 scalar (C_cilia ) 

IHC_cilia_RPParams.u0=	5e-9;       

IHC_cilia_RPParams.s0=	30e-9;

IHC_cilia_RPParams.u1=	1e-9;

IHC_cilia_RPParams.s1=	1e-9;

IHC_cilia_RPParams.Gmax= 6e-9;    % 2.5e-9 maximum conductance (Siemens)

IHC_cilia_RPParams.Ga=	1e-9;  % 4.3e-9 fixed apical membrane conductance

IHC_cilia_RPParams.Ga=	.8e-9;  % 4.3e-9 fixed apical membrane conductance

%  #5 IHC_RP

IHC_cilia_RPParams.Cab=	4e-012;         % IHC capacitance (F)

% IHC_cilia_RPParams.Cab=	1e-012;         % IHC capacitance (F)

IHC_cilia_RPParams.Et=	0.100;          % endocochlear potential (V)

IHC_cilia_RPParams.Gk=	2e-008;         % 1e-8 potassium conductance (S)

IHC_cilia_RPParams.Ek=	-0.08;          % -0.084 K equilibrium potential

IHC_cilia_RPParams.Rpc=	0.04;           % combined resistances

%%  #5 IHCpreSynapse

IHCpreSynapseParams=[];

IHCpreSynapseParams.GmaxCa=	14e-9;% maximum calcium conductance

IHCpreSynapseParams.GmaxCa=	12e-9;% maximum calcium conductance

IHCpreSynapseParams.ECa=	0.066;  % calcium equilibrium potential

IHCpreSynapseParams.beta=	400;	% determine Ca channel opening

IHCpreSynapseParams.gamma=	100;	% determine Ca channel opening

IHCpreSynapseParams.tauM=	0.00005;	% membrane time constant ?0.1ms

IHCpreSynapseParams.power=	3;

% reminder: changing z has a strong effect on HF thresholds (like Et)

IHCpreSynapseParams.z=	    2e42;   % scalar Ca -> vesicle release rate

LSRtauCa=35e-6;            HSRtauCa=85e-6;            % seconds

% LSRtauCa=35e-6;            HSRtauCa=70e-6;            % seconds

IHCpreSynapseParams.tauCa= [ HSRtauCa]; %LSR and HSR fiber

%%  #6 AN_IHCsynapse

% c=kym/(y(l+r)+kl)	(spontaneous rate)

% c=(approx)  ym/l  (saturated rate)

AN_IHCsynapseParams=[];             % clear the structure first

AN_IHCsynapseParams.M=	12;         % maximum vesicles at synapse

AN_IHCsynapseParams.y=	4;          % depleted vesicle replacement rate

AN_IHCsynapseParams.y=	6;          % depleted vesicle replacement rate

AN_IHCsynapseParams.x=	30;         % replenishment from re-uptake store

AN_IHCsynapseParams.x=	60;         % replenishment from re-uptake store

% reduce l to increase saturated rate

AN_IHCsynapseParams.l=	100; % *loss rate of vesicles from the cleft

AN_IHCsynapseParams.l=	250; % *loss rate of vesicles from the cleft

AN_IHCsynapseParams.r=	500; % *reuptake rate from cleft into cell

% AN_IHCsynapseParams.r=	300; % *reuptake rate from cleft into cell

AN_IHCsynapseParams.refractory_period=	0.00075;

% number of AN fibers at each BF (used only for spike generation)

AN_IHCsynapseParams.numFibers=	100; 

AN_IHCsynapseParams.TWdelay=0.004;  % ?delay before stimulus first spike

AN_IHCsynapseParams.ANspeedUpFactor=5; % longer epochs for computing spikes.

%%  #7 MacGregorMulti (first order brainstem neurons)

MacGregorMultiParams=[];

MacGregorMultiType='chopper'; % MacGregorMultiType='primary-like'; %choose

switch MacGregorMultiType

    case 'primary-like'

        MacGregorMultiParams.nNeuronsPerBF=	10;   % N neurons per BF

        MacGregorMultiParams.type = 'primary-like cell';

        MacGregorMultiParams.fibersPerNeuron=4;   % N input fibers

        MacGregorMultiParams.dendriteLPfreq=200;  % dendritic filter

        MacGregorMultiParams.currentPerSpike=0.11e-6; % (A) per spike

        MacGregorMultiParams.Cap=4.55e-9;   % cell capacitance (Siemens)

        MacGregorMultiParams.tauM=5e-4;     % membrane time constant (s)

        MacGregorMultiParams.Ek=-0.01;      % K+ eq. potential (V)

        MacGregorMultiParams.dGkSpike=3.64e-5; % K+ cond.shift on spike,S

        MacGregorMultiParams.tauGk=	0.0012; % K+ conductance tau (s)

        MacGregorMultiParams.Th0=	0.01;   % equilibrium threshold (V)

        MacGregorMultiParams.c=	0.01;       % threshold shift on spike, (V)

        MacGregorMultiParams.tauTh=	0.015;  % variable threshold tau

        MacGregorMultiParams.Er=-0.06;      % resting potential (V)

        MacGregorMultiParams.Eb=0.06;       % spike height (V)

    case 'chopper'

        MacGregorMultiParams.nNeuronsPerBF=	10;   % N neurons per BF

        MacGregorMultiParams.type = 'chopper cell';

        MacGregorMultiParams.fibersPerNeuron=10;  % N input fibers

%         MacGregorMultiParams.fibersPerNeuron=6;  % N input fibers

        MacGregorMultiParams.dendriteLPfreq=50;   % dendritic filter

        MacGregorMultiParams.currentPerSpike=35e-9; % *per spike

%         MacGregorMultiParams.currentPerSpike=30e-9; % *per spike

        MacGregorMultiParams.Cap=1.67e-8; % ??cell capacitance (Siemens)

        MacGregorMultiParams.tauM=0.002;  % membrane time constant (s)

        MacGregorMultiParams.Ek=-0.01;    % K+ eq. potential (V)

        MacGregorMultiParams.dGkSpike=1.33e-4; % K+ cond.shift on spike,S

        MacGregorMultiParams.tauGk=	[0.001 0.0005];% K+ conductance tau (s)

        MacGregorMultiParams.Th0=	0.01; % equilibrium threshold (V)

        MacGregorMultiParams.c=	0;        % threshold shift on spike, (V)

        MacGregorMultiParams.tauTh=	0.02; % variable threshold tau

        MacGregorMultiParams.Er=-0.06;    % resting potential (V)

        MacGregorMultiParams.Eb=0.06;     % spike height (V)

        MacGregorMultiParams.PSTHbinWidth=	1e-4;

end

%%  #8 MacGregor (second-order neuron). Only one per channel

MacGregorParams=[];                 % clear the structure first

MacGregorParams.type = 'chopper cell';

MacGregorParams.fibersPerNeuron=10; % N input fibers

MacGregorParams.dendriteLPfreq=100; % dendritic filter

MacGregorParams.currentPerSpike=120e-9;% *(A) per spike

MacGregorParams.currentPerSpike=40e-9;% *(A) per spike

MacGregorParams.Cap=16.7e-9;        % cell capacitance (Siemens)

MacGregorParams.tauM=0.002;         % membrane time constant (s)

MacGregorParams.Ek=-0.01;           % K+ eq. potential (V)

MacGregorParams.dGkSpike=1.33e-4;   % K+ cond.shift on spike,S

MacGregorParams.tauGk=	0.0005;     % K+ conductance tau (s)

MacGregorParams.Th0=	0.01;       % equilibrium threshold (V)

MacGregorParams.c=	0;              % threshold shift on spike, (V)

MacGregorParams.tauTh=	0.02;       % variable threshold tau

MacGregorParams.Er=-0.06;           % resting potential (V)

MacGregorParams.Eb=0.06;            % spike height (V)

MacGregorParams.debugging=0;        % (special)

% wideband accepts input from all channels (of same fiber type)

% use wideband to create inhibitory units

MacGregorParams.wideband=0;         % special for wideband units

% MacGregorParams.saveAllData=0;

%%  #9 filteredSACF

minPitch=	300; maxPitch=	3000; numPitches=60;    % specify lags

pitches=100*log10(logspace(minPitch/100, maxPitch/100, numPitches));

filteredSACFParams.lags=1./pitches;     % autocorrelation lags vector

filteredSACFParams.acfTau=	.003;       % time constant of running ACF

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

filteredSACFParams.plotFilteredSACF=1;  % 0 plots unfiltered ACFs

filteredSACFParams.plotACFs=0;          % special plot (see code)

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

filteredSACFParams.lagsProcedure=  'useAllLags';

% filteredSACFParams.lagsProcedure=  'useBernsteinLagWeights';

% filteredSACFParams.lagsProcedure=  'omitShortLags';

filteredSACFParams.criterionForOmittingLags=3;

% checks

if AN_IHCsynapseParams.numFibers<MacGregorMultiParams.fibersPerNeuron

    error('MacGregorMulti: too few input fibers for input to MacG unit')

end

%% now accept last minute parameter changes required by the calling program

% paramChanges

if nargin>3 && ~isempty(paramChanges)

    nChanges=length(paramChanges);

    for idx=1:nChanges

        eval(paramChanges{idx})

    end

end

%% write all parameters to the command window

% showParams is currently set at the top of htis function

if showParams

    fprintf('\n %%%%%%%%\n')

    fprintf('\n%s\n', method.parameterSource)

    fprintf('\n')

    nm=UTIL_paramsList(whos);

    for i=1:length(nm)

        %         eval(['UTIL_showStruct(' nm{i} ', ''' nm{i} ''')'])

        if ~strcmp(nm(i), 'method')

            eval(['UTIL_showStructureSummary(' nm{i} ', ''' nm{i} ''', 10)'])

        end

    end

    % highlight parameter changes made locally

    if nargin>3 && ~isempty(paramChanges)

        fprintf('\n Local parameter changes:\n')

        for i=1:length(paramChanges)

            disp(paramChanges{i})

        end

    end

end

% for backward compatibility

experiment.comparisonData=[];

function test_MAP1_14x

% 'x' is a trial file

% 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='NormalIC';

%% #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=0.250;                 % 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.

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

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

% paramChanges={'DRNLParams.rateToAttenuationFactorProb = 0;'};

% paramChanges={'IHCpreSynapseParams.tauCa=86e-6;',

%     'AN_IHCsynapseParams.numFibers=	1000;'};

% fixed MOC attenuation(using negative factor)

paramChanges={'DRNLParams.rateToAttenuationFactorProb=-0.005;'};

%% 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=1;            % 2D plot of spikes histogram

% 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('Computing ...')

MAP1_14(inputSignal, sampleRate, BFlist, ...

    MAPparamsName, AN_spikesOrProbability, paramChanges);

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

UTIL_showMAP(showMapOptions, paramChanges)

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(0.05/dt));

inputSignal= [silence inputSignal silence];

function [globalStimParams, stimComponents]=...

    checkDescriptors(globalStimParams, stimComponents)

sampleRate=globalStimParams.FS;

globalStimParams.nSignalPoints=...

    round(globalStimParams.overallDuration* sampleRate);

            stimComponents(ear,componentNo).endSilence=...

                globalStimParams.overallDuration-...

                stimComponents(ear,componentNo).beginSilence -...

                stimComponents(ear,componentNo).toneDuration;

            endSilenceNpoints=stimComponents(ear,componentNo).endSilence...

                *sampleRate;

        end

        if stimComponents(ear,componentNo).endSilence<0

            globalStimParams

            descriptorError( 'component durations greater than overallDuration', stimComponents, ear, componentNo)

        totalSignalPoints= round(totalDuration* sampleRate);