MAP

function method=MAPparamsNormalTest ...

    (BFlist, sampleRate, showParams)

% 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

global IHC_VResp_VivoParams IHCpreSynapseParams  AN_IHCsynapseParams

global MacGregorParams MacGregorMultiParams  filteredSACFParams

global experiment % used by calls from multiThreshold only

global IHC_cilia_RPParams

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

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

% DRNLParams.b=12e-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=1000;     % 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 = .005;  % 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=	0.01;      % 0.1 scalar (C_cilia ) 

IHC_cilia_RPParams.u0=	5e-10;       

IHC_cilia_RPParams.s0=	10e-10;

IHC_cilia_RPParams.u1=	5e-10;

IHC_cilia_RPParams.s1=	5e-10;

IHC_cilia_RPParams.Gmax= 4e-9;    % 2.5e-9 maximum conductance (Siemens)

IHC_cilia_RPParams.Ga=	1.5e-9;  % 4.3e-9 fixed apical membrane conductance

%  #5 IHC_RP

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

IHC_cilia_RPParams.Cab=	2e-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=	    1e42;   % scalar Ca -> vesicle release rate

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

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

IHCpreSynapseParams.tauCa= [LSRtauCa 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=	10;         % replenishment from re-uptake store

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

% reduce l to increase saturated rate

AN_IHCsynapseParams.l=	150; % *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=	0.0005;% 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=30e-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

%% 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

end

% **********************************************************************  comparison data

% store individual data here for display on the multiThreshold GUI (if used)

% the final value in each vector is an identifier (BF or duration))

if isstruct(experiment)

    switch experiment.paradigm

        case {'IFMC','IFMC_8ms'}

            % based on MPa

            comparisonData=[

                66	51	49	48	46	45	54	250;

                60	54	46	42	39	49	65	500;

                64	51	38	32	33	59	75	1000;

                59	51	36	30	41	81	93	2000;

                71	63	53	44	36	76	95	4000;

                70	64	43	35	35	66	88	6000;

                110	110	110	110	110	110	110	8000;

                ];

            if length(BFlist)==1 && ~isempty(comparisonData)

                availableFrequencies=comparisonData(:,end)';

                findRow= find(BFlist==availableFrequencies);

                if ~isempty (findRow)

                    experiment.comparisonData=comparisonData(findRow,:);

                end

            end

        case {'TMC','TMC_8ms'}

            % based on MPa

            comparisonData=[

                48	58	63	68	75	80	85	92	99	250;

                33	39	40	49	52	61	64	77	79	500;

                39	42	50	81	83	92	96	97	110	1000;

                24	26	32	37	46	51	59	71	78	2000;

                65	68	77	85	91	93	110	110	110	4000;

                20	19	26	44	80	95	96	110	110	6000;

                ];

            if length(BFlist)==1 && ~isempty(comparisonData)

                availableFrequencies=comparisonData(:,end)';

                findRow= find(BFlist==availableFrequencies);

                if ~isempty (findRow)

                    experiment.comparisonData=comparisonData(findRow,:);

                end

            end

        case { 'absThreshold',  'absThreshold_8'}

            % MPa thresholds

            experiment.comparisonData=[

                32	26	16	18	22	22 0.008;

                16	13	6	9	15	11 0.500

                ];

        otherwise

            experiment.comparisonData=[];

    end

end