MAP

    options.showACF=0;

    options.showEfferent=1;

    options.surfProbability=0;

    UTIL_showMAP(options)

end

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

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

efferentDelay=0.010;

method.segmentDuration=efferentDelay;

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

% i.e. a minimum ratio of 0.056.

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

%     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

DRNLParams.BFlist=BFlist;

% DRNL nonlinear path

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

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

% DRNL MOC efferents

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

%      DRNLParams.rateToAttenuationFactor = 0;  % strength of MOC

%% #4 IHC_cilia_RPParams

IHC_cilia_RPParams.u1=	1e-9;

IHC_cilia_RPParams.s1=	1e-9;

%  #5 IHC_RP

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

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

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

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

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

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

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

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

% 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;   % * N(all ICspikes)

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

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

% asymptote should be around 100-200 ms

DRNLParams.BFlist=BFlist;

% DRNL nonlinear path

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

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

% DRNL MOC efferents

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

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

%      DRNLParams.rateToAttenuationFactor = 0;  % strength of MOC

%% #4 IHC_cilia_RPParams

%  #5 IHC_RP

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

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

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

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

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

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

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

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

% MAPdemo runs the MATLAB auditory periphery model (MAP1_14) as far as

%  the AN (probabilities) or IC (spikes) with graphical output

restorePath=path;

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

    ['..' filesep 'utilities'])

%% Generate stimuli

switch signalType

    case 'tones'

        inputSignal=createMultiTone(sampleRate, toneFrequency, ...

%% Generate stimuli

switch signalType

    case 'tones'

        inputSignal=createMultiTone(sampleRate, toneFrequency, ...

%

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

restorePath=path;

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

    ['..' filesep 'utilities'])

% or

% NB probabilities are not corrected for refractory effects

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

signalType= 'tones';

sampleRate= 100000;

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

rampDuration=.005;              % seconds

% or

%% #4 rms level

% signal details

%% #5 number of channels in the model

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

%   or specify your own channel BFs

% BFlist=toneFrequency;

%  *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;'};

%% 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 

if strcmp(AN_spikesOrProbability, 'spikes')

    % avoid nonsensical options

    showMapOptions.surfProbability=0;

    showMapOptions.showACF=0;

end

if strcmp(signalType, 'file')

    % needed for labeling plot

%% Generate stimuli

switch signalType

    case 'tones'

        inputSignal=createMultiTone(sampleRate, toneFrequency, ...

inputSignal=inputSignal.*ramp;

% add 10 ms silence

inputSignal= [silence inputSignal silence];

% UTIL_showMAP produces summaries of the output from MAP's mmost recent run

%  All MAP outputs are stored in global variables and UTIL_showMAP

%  simply assumes that they are in place.

%

dbstop if warning

addpath ( ['..' filesep 'utilities'], ['..' filesep 'parameterStore'])

if nargin<1

end

    % Read parameters from MAPparams<***> file in 'parameterStore' folder

    %  and print out all parameters

    cmd=['MAPparams' saveMAPparamsName ...

    eval(cmd);

end

    %% print summary firing rates

    fprintf('\n\n')

    disp('summary')

%% figure (99) summarises main model output

    plotInstructions.figureNo=99;

    signalRMS=mean(savedInputSignal.^2)^0.5;

    signalRMSdb=20*log10(signalRMS/20e-6);

    end

end

    %% surface plot of probability

    figure(97), clf

    % select only HSR fibers at the bottom of the matrix

    zlabel('spike rate')

    view([-20 60])

    %     view([0 90])

end

%% plot efferent control values as dB

    plotInstructions=[];

    plotInstructions.figureNo=98;

    figure(98), clf

end

%% ACF plot if required

    tic

    method.dt=dt;

    method.segmentNo=1;