THE AUDITORY MODELING TOOLBOX
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DATA_HEERINGA2024
Single-unit responses from the auditory nerve of gerbils
Program code:
function data = data_heeringa2024(varargin)
%DATA_HEERINGA2024 Single-unit responses from the auditory nerve of gerbils
% Usage: data = data_heeringa2024(flag);
% data = data_heeringa2024(flag, animalID);
% data = data_heeringa2024(.., 'unit', unit);
%
% DATA_HEERINGA2024(flag) plots various figures
% from Heeringa (20204) or displays tables.
%
% data = DATA_HEERINGA2024(flag) returns the corresponding data to a variable.
%
% The following flags can be provided:
%
% 'list' Returns a cell array with available animal IDs. The animal
% ID can be used as animalID to retrieve data and info
% and plot specific figures.
%
% 'info' Returns the information about an animal. Provide animalID*
% to specify the animal (default: G220922). For more details,
% see exp.info in https://datadryad.org/stash/downloads/file_stream/3070542.
%
% 'data' Returns the responses from an animal. Provide animalID*
% to specify the animal (default: G220922). The output
% data is structure containing all the data and metadata
% structured by units and recording types.
% Note that data of some animals are stored in multiple batches
% to keep the file sizes below 1 GB. All data from all batches
% are returned. For more details,
% see exp.data in https://datadryad.org/stash/downloads/file_stream/3070542.
%
% 'fig3' Reproduce Fig. 3:
% Plot the animal's hearing sensitivity as derived from the
% ABR to chirps as a function of the animal's age in months.
% Males and females are plotted separately.
%
% 'fig4' Reproduce Fig. 4:
% Plot the waveform shapes of a single unit. Default is
% the animal G220922 and unit 12 (3p_607).
%
% 'fig5a' Reproduce Fig. 5a:
% Plot all rate-level functions from an animal. Default is the
% animal G220922.
%
% 'fig5b' Reproduce Fig. 5b:
% Plot a PSTH from the animal G220908 and unit 23
% (3p_181) at the test level of 20 dB above threshold.
%
% 'fig6' Reproduce Fig. 6:
% Plot BF vs threshold (Fig. 6a), BF distribution in young-
% adult gerbils (Fig. 6b), BF vs spontaneous rate (Fig. 6c)
% and BF vs vector strength to pure tones at BF (Fig. 6d) based
% on the full dataset.
%
% 'fig7' Reproduce Fig. 7:
% Fig. 7a: Characteristic frequencies as a function of the
% fibre’s BF and as a histogram.
% Fig. 7b: Click latencies as a function of the BF with their
% histogram. Fig. 7c: Spontaneous rates (SR) as a function of
% fibre's BF. Fig. 7d, 7e, and 7f: Thresholds for the complex stimuli
% recordings NOISE, SPS, and CVC, respectively.
%
% 'tab1' Returns the Tab. 1 from Heeringa (20204).
%
% 'summary' Returns a summary table with the metadata of the dataset.
%
%
% DATA_HEERINGA2024(.., 'unit', unit) plots and/or provides data for
% a specific unit number. Default is unit 1, except for Fig. 4, where default
% is unit 12 to be consistent with Heeringa (2004).
%
%
% Examples:
% ---------
%
% To display Fig. 3 use :
%
% data_heeringa2024('fig3');
%
% To display Fig. 4 use :
%
% data_heeringa2024('fig4');
%
% To display Fig. 5a use :
%
% data_heeringa2024('fig5a');
%
% To display Fig. 5b use :
%
% data_heeringa2024('fig5b');
%
% To display Fig. 6 use :
%
% data_heeringa2024('fig6');
%
% To display Fig. 7 use :
%
% data_heeringa2024('fig7');
%
% To display Tab. 1 use :
%
% data_heeringa2024('tab1');
%
% To display the summary of all data use :
%
% data_heeringa2024('summary');
%
%
% References:
% A. N. Heeringa, F. Teske, G. Ashida, and C. Köppl. Cochlear aging
% disrupts the correlation between spontaneous rate and sound-level
% coding in auditory nerve fibers. Journal of Neurophysiology,
% 130(3):736--750, 2023. PMID: 37584075.
%
% A. N. Heeringa and C. Köppl. Auditory nerve fiber discrimination and
% representation of naturally-spoken vowels in noise. eNeuro, 9(1), 2022.
%
% F. Steenken, A. N. Heeringa, R. Beutelmann, L. Zhang, S. Bovee, G. M.
% Klump, and C. Köppl. Age-related decline in cochlear ribbon synapses
% and its relation to different metrics of auditory-nerve activity.
% Neurobiology of Aging, 108:133--145, 2021.
%
% A. N. Heeringa, L. Zhang, G. Ashida, R. Beutelmann, F. Steenken, and
% C. Köppl. Temporal coding of single auditory nerve fibers is not
% degraded in aging gerbils. Journal of Neuroscience, 40(2):343--354,
% 2020.
%
% A. N. Heeringa, C. Jüchter, R. Beutelmann, G. M. Klump, and C. Köppl.
% Altered neural encoding of vowels in noise does not affect behavioral
% vowel discrimination in gerbils with age-related hearing loss.
% Frontiers in Neuroscience, 17, 2023.
%
% F. Steenken, H. Oetjen, R. Beutelmann, L. H. Carney, C. Koeppl, and
% G. M. Klump. Neural processing and perception of schroeder-phase
% harmonic tone complexes in the gerbil: Relating single-unit
% neurophysiology to behavior. European Journal of Neuroscience,
% 56(3):4060--4085, 2022.
%
% A. Heeringa. Single-unit auditory nerve fibre responses of young-adult
% and aging gerbils, 2024.
%
% A. N. Heeringa. Single-unit data for sensory neuroscience: Responses
% from the auditory nerve of young-adult and aging gerbils. Scientific
% Data, 11:411, Apr. 2024.
%
%
% See also: plot_heeringa2024
%
% Url: http://amtoolbox.org/amt-1.6.0/doc/data/data_heeringa2024.php
% #StatusDoc: Perfect
% #StatusCode: Perfect
% #Verification: Verified
% #Author: Amarins Heeringa (2024): Original version provided for the AMT.
% #Author: Piotr Majdak(2024): Integration in the AMT 1.6.
% This file is licensed unter the GNU General Public License (GPL) either
% version 3 of the license, or any later version as published by the Free Software
% Foundation. Details of the GPLv3 can be found in the AMT directory "licences" and
% at <https://www.gnu.org/licenses/gpl-3.0.html>.
% You can redistribute this file and/or modify it under the terms of the GPLv3.
% This file is distributed without any warranty; without even the implied warranty
% of merchantability or fitness for a particular purpose.
definput.flags.type = {'missingflag', 'list','data', 'info', 'fig3', 'fig4', 'fig5a', 'fig5b', 'fig6', 'fig7', 'tab1', 'summary'};
% definput.flags.plot = {'plot', 'no_plot'};
% definput.keyvals.unit = {};
definput.import={'data_heeringa2024'}; % import from arg_data_heeringa2024
[flags,kv] = ltfatarghelper({},definput,varargin);
if flags.do_missingflag
flagnames=[sprintf('%s, ',definput.flags.type{2:end-2}),...
sprintf('%s or %s',definput.flags.type{end-1},...
definput.flags.type{end})];
error('%s: You must specify one of the following flags: %s.', ...
upper(mfilename),flagnames);
end
% Create the list of all animalIDs
animalIDs = kv.animalIDs(:,1);
% If a list is requested, to the list and exit
if flags.do_list
data = animalIDs;
return;
end
animalID = flags.animalID;
unit = kv.unit;
% determine the correct default animal ID and unit - depending on the figure to plot
switch flags.type
case 'fig5b'
if strcmp(flags.animalID,'default_animalID')
animalID = 'G220908'; % Default animalID is G220908 for Fig. 5b
end
case 'fig4'
if strcmp(flags.animalID,'default_animalID')
animalID = 'G220922'; % Default animalID is G220922 for Fig 4
if isempty(unit), unit = 12; end % If nothing specified, the default unit is 12
else
if isempty(unit), unit = 1; end % If animalID specified, the default unit is 1
end
otherwise
if strcmp(flags.animalID,'default_animalID'), animalID = 'G220922'; end
if isempty(unit), unit = 1; end
end
% calculate the number of batches for the requested animal
batches = cell2mat(kv.animalIDs(:,2));
batch_num=batches(find(contains(animalIDs, animalID),1));
%% Get the info
if flags.do_info
data=amt_load('heeringa2024',[animalID '_1.mat'], 'info'); % infos are identical in all batches, thus we load only the first one
data=data.info;
end
%% Get the data and/or info
if flags.do_data
data = [];
for ii=1:batch_num % load all batches
data_ii = amt_load('heeringa2024',[animalID '_' num2str(ii) '.mat'],'data');
data = [data data_ii.data];
end
end
%% ------ Prepare data for Fig. 3, Tab. 1 and the Summary -------
if flags.do_fig3 || flags.do_tab1 || flags.do_summary
% load the all_exp struct
all_exp = amt_load('heeringa2024','all_exp.mat');
all_exp = all_exp.all_exp;
% pre-allocate variables that will store the metadata per animal
ages = nan(1,length(all_exp));
sex = nan(1,length(all_exp));
abr = nan(1,length(all_exp));
weights = nan(1,length(all_exp));
nunit = nan(1,length(all_exp));
bfsR = nan(2,length(all_exp));
oxy = nan(1,length(all_exp));
noise = nan(1,length(all_exp));
sps = nan(1,length(all_exp));
cvc = nan(1,length(all_exp));
% loop through the animals and store the animal specific metadata
for i = 1:length(all_exp)
ages(i) = all_exp(i).info.age;
if strcmp(all_exp(i).info.sex,'F')
sex(i) = 0;
elseif strcmp(all_exp(i).info.sex,'M')
sex(i) = 1;
end
abr(i) = all_exp(i).info.ABR.threshold;
weights(i) = all_exp(i).info.weight;
nunit(i) = length(all_exp(i).data);
oxy(i) = all_exp(i).info.anesthesia.oxygen;
bfs = []; noiT = []; spsT = []; cvcT = [];
for j = 1:length(all_exp(i).data)
if ~isempty(all_exp(i).data(j).BF)
bfs = [bfs all_exp(i).data(j).BF.analysis.bf];
elseif ~isempty(all_exp(i).data(j).CF)
bfs = [bfs all_exp(i).data(j).CF.analysis.cf];
end
if ~isempty(all_exp(i).data(j).NOISE)
noiT = [noiT j];
end
if ~isempty(all_exp(i).data(j).SPS)
spsT = [spsT j];
end
if ~isempty(all_exp(i).data(j).CVC)
cvcT = [cvcT j];
end
end
bfsR(1,i) = min(bfs);
bfsR(2,i) = max(bfs);
noise(i) = length(noiT);
sps(i) = length(spsT);
cvc(i) = length(cvcT);
end
% find the indices for young, middle-aged and old animals
yo = find(ages <= 365);
ma = find(ages > 365 & ages <= 3*365);
ol = find(ages > 3*365);
% find the indices for male and female animals
F = find(sex == 0);
M = find(sex == 1);
end
%% ====== Plot Figure 3 ======
if flags.do_fig3
% define settings
mo = 365/12; % average length of a month in days
sz = 70; % marker size
f_col = [11 154 51]./256; % green
m_col = [111 32 130]./256; % purple
fs = 22; % fontsize
xl = [0 45]; % x-axis limits
figure;
scatter(ages(F)./mo, abr(F), sz, f_col, 'filled')
hold on
scatter(ages(M)./mo, abr(M), sz, m_col, 'filled')
xlabel('Age (months)')
ylabel('ABR threshold (dB SPL)')
set(gca,'FontSize',fs,'XTick',[0 6 12 18 24 30 36 42],'XTickLabel',{'0','','12','','24','','36',''})
legend('Female','Male', 'location','northwest')
xlim(xl)
end
%% ====== Input for Table 1 ======
if flags.do_tab1
Parameter = { 'Total number of animals'; 'Number of female animals'; ...
'Age in months (mean)'; 'Age in months (std. dev)'; ...
'Age in months (min)'; 'Age in months (max)'; ...
'ABR threshold in dB SPL (mean)'; 'ABR threshold in dB SPL (std. dev.)'; ...
'ABR threshold in dB SPL (min)'; 'ABR threshold in dB SPL (max)'; ...
'Weight in grams (mean)'; 'Weight in grams (std. dev)'; ...
'Weight in grams (min)'; 'Weight in grams (max)'; ...
'Total number of single units'; ...
'Range of best frequency in Hz (min)'; 'Range of best frequency in Hz (max)'};
mo = 365/12;
% young-adult animals
Young_adult = [length(yo); length(yo)-sum(sex(yo)); ...
nanmean(ages(yo))/mo; nanstd(ages(yo))/mo; min(ages(yo))/mo; max(ages(yo))/mo; ...
nanmean(abr(yo)); nanstd(abr(yo)); min(abr(yo)); max(abr(yo)); ...
nanmean(weights(yo)); nanstd(weights(yo)); min(weights(yo)); max(weights(yo)); ...
sum(nunit(yo)); ...
min(bfsR(1,yo)); max(bfsR(2,yo))];
% middle-aged animals
Middle_aged = [length(ma); length(ma)-sum(sex(ma)); ...
nanmean(ages(ma))/mo; nanstd(ages(ma))/mo; min(ages(ma))/mo; max(ages(ma))/mo; ...
nanmean(abr(ma)); nanstd(abr(ma)); min(abr(ma)); max(abr(ma)); ...
nanmean(weights(ma)); nanstd(weights(ma)); min(weights(ma)); max(weights(ma)); ...
sum(nunit(ma)); ...
min(bfsR(1,ma)); max(bfsR(2,ma))];
% quiet-aged animals
Quiet_aged = [length(ol); length(ol)-sum(sex(ol)); ...
nanmean(ages(ol))/mo; nanstd(ages(ol))/mo; min(ages(ol))/mo; max(ages(ol))/mo; ...
nanmean(abr(ol)); nanstd(abr(ol)); min(abr(ol)); max(abr(ol)); ...
nanmean(weights(ol)); nanstd(weights(ol)); min(weights(ol)); max(weights(ol)); ...
sum(nunit(ol)); ...
min(bfsR(1,ol)); max(bfsR(2,ol))];
data = table(Young_adult, Middle_aged, Quiet_aged,'RowNames',Parameter);
if nargout == 0, amt_disp(data,'documentation'); end % display the table
end
%% ====== make the summary of metadata ======
if flags.do_summary
T = table;
W=warning; warning('off'); % switch off warnings
for i = 1:length(all_exp)
T.animalID{i} = all_exp(i).animalID;
T.age_days(i) = all_exp(i).info.age;
T.sex{i} = all_exp(i).info.sex;
T.ABR_threshold(i) = all_exp(i).info.ABR.threshold;
T.n_units(i) = length(all_exp(i).data);
nbf = 0; bfs = []; ncf = 0; nph = 0; nclick = 0; nrlf = 0; nsr = 0; nnoise = 0; nsps = 0; ncvc = 0; % ntfs1 = 0; nvcv = 0;
for j = 1:length(all_exp(i).data)
if ~isempty(all_exp(i).data(j).BF)
nbf = nbf + 1;
bfs = [bfs all_exp(i).data(j).BF.analysis.bf];
end
if ~isempty(all_exp(i).data(j).CF)
ncf = ncf + 1;
end
if ~isempty(all_exp(i).data(j).PH)
nph = nph + 1;
end
if ~isempty(all_exp(i).data(j).CLICK)
nclick = nclick + 1;
end
if ~isempty(all_exp(i).data(j).RLF)
nrlf = nrlf + 1;
end
if ~isempty(all_exp(i).data(j).SR)
nsr = nsr + 1;
end
if ~isempty(all_exp(i).data(j).NOISE)
nnoise = nnoise + 1;
end
if ~isempty(all_exp(i).data(j).SPS)
nsps = nsps + 1;
end
if ~isempty(all_exp(i).data(j).CVC)
ncvc = ncvc + 1;
end
end
T.BF_range{i} = [num2str(min(bfs)) ' - ' num2str(max(bfs))];
T.n_BF(i) = nbf;
T.n_CF(i) = ncf;
T.n_PH(i) = nph;
T.n_CLICK(i) = nclick;
T.n_RLF(i) = nrlf;
T.n_SR(i) = nsr;
T.n_NOISE(i) = nnoise;
T.n_SPS(i) = nsps;
T.n_CVC(i) = ncvc;
end
warning(W); % restore warnings
if nargout > 0, data = T; else, amt_disp(T,'documentation'); end % display the table
end
%% ----------- Make the plots of Figure 4 ---------------
if flags.do_fig4
data = [];
for ii=1:batch_num % load all batches
data_ii = amt_load('heeringa2024',[animalID '_' num2str(ii) '.mat'],'data');
data = [data data_ii.data];
end
if unit > length(data)
error('Requested unit not available');
end
pretitle = [animalID ' (unit ' num2str(unit) ')'];
plot_heeringa2024('waveform',data(unit).BF.curvedata, data(unit).BF.curveresp, ...
data(unit).BF.curvesettings, pretitle);
end
%% ----------- Make the plots of Figure 5a ---------------
if flags.do_fig5a
data = [];
for ii=1:batch_num % load all batches
data_ii = amt_load('heeringa2024',[animalID '_' num2str(ii) '.mat'],'data');
data = [data data_ii.data];
end
% set the axes limitations
xl = [5 85];
% plot the data
figure;
for i = 1:length(data) % loop through all the units
if ~isempty(data(i).RLF) % only proceed if there is RLF data
% get the rate, stds, and levels of the unit
rate = data(i).RLF.analysis.rates;
std = data(i).RLF.analysis.stdevs;
levs = data(i).RLF.analysis.levels;
% errorbar(levs,rate,std,'LineWidth',1.5)
plot(levs,rate,'LineWidth',1.5)
hold on
end
end
% beautify
set(gca,'FontSize',22)
xlabel('Level (dB SPL)')
ylabel('Rate (spikes/s)')
xlim(xl)
title(animalID);
box off
end
%% ----------- Make the plots of Figure 5b ---------------
if flags.do_fig5b
exp = amt_load('heeringa2024','G220908_1.mat');
unit = 23; % '3p_181'
TestLevel = 20; % 'in dB above threshold'
% plot
plot_heeringa2024('psth',exp,unit,TestLevel);
end
%% ----------- Prepare the data for Figures 6 and 7 ---------------
if flags.do_fig6 || flags.do_fig7
% load the struct
all_exp = amt_load('heeringa2024','all_exp.mat');
all_exp = all_exp.all_exp;
% pre-allocate variables that will store the metadata per animal
largeNumber = 2000; % make sure this is larger than the total number of units
ages = nan(1,largeNumber);
sex = nan(1,largeNumber);
abr = nan(1,largeNumber);
bfs = nan(1,largeNumber);
thr = nan(1,largeNumber);
srsT = nan(5,largeNumber);
clk = nan(4,largeNumber);
vss = nan(1,largeNumber);
cfs = nan(2,largeNumber);
cmx = nan(3,largeNumber);
% initialize a counter for the units
cnt = 1;
% loop through all animals and store the animal specific metadata
for i = 1:length(all_exp)
% loop through all units of an animal and store their characteristics
for j = 1:length(all_exp(i).data)
if ~isempty(all_exp(i).data(j).BF)
bfs(1,cnt) = all_exp(i).data(j).BF.analysis.bf;
srsT(4,cnt) = all_exp(i).data(j).BF.analysis.sr;
end
if ~isempty(all_exp(i).data(j).RLF)
thr(1,cnt) = all_exp(i).data(j).RLF.analysis.threshold;
srsT(3,cnt) = all_exp(i).data(j).RLF.analysis.sr;
end
if ~isempty(all_exp(i).data(j).CLICK)
if length(all_exp(i).data(j).CLICK) == 1
clk(1,cnt) = all_exp(i).data(j).CLICK.analysis.fsl_mean;
clk(2,cnt) = all_exp(i).data(j).CLICK.analysis.fsl_median;
clk(3,cnt) = all_exp(i).data(j).CLICK.analysis.latency_2bins;
clk(4,cnt) = all_exp(i).data(j).CLICK.analysis.latency_poisson;
end
end
if ~isempty(all_exp(i).data(j).SR)
srsT(1,cnt) = all_exp(i).data(j).SR.analysis.sr;
end
if ~isempty(all_exp(i).data(j).PH)
srsT(2,cnt) = all_exp(i).data(j).PH.analysis.sr;
tmp = find(~isnan(all_exp(i).data(j).PH.analysis.prob) & all_exp(i).data(j).PH.analysis.prob<0.001);
if ~isempty(tmp)
vss(1,cnt) = max(all_exp(i).data(j).PH.analysis.vs(tmp));
end
end
if ~isempty(all_exp(i).data(j).CF)
cfs(1,cnt) = all_exp(i).data(j).CF.analysis.cf;
cfs(2,cnt) = all_exp(i).data(j).CF.analysis.q10;
end
if ~isempty(all_exp(i).data(j).NOISE)
cmx(1,cnt) = 1;
end
if ~isempty(all_exp(i).data(j).SPS)
cmx(2,cnt) = 1;
end
if ~isempty(all_exp(i).data(j).CVC)
cmx(3,cnt) = 1;
end
ages(1,cnt) = all_exp(i).info.age;
if strcmp(all_exp(i).info.sex,'F')
sex(1,cnt) = 0;
elseif strcmp(all_exp(i).info.sex,'M')
sex(1,cnt) = 1;
end
abr(1,cnt) = all_exp(i).info.ABR.threshold;
cnt = cnt + 1;
end
end
% find the indices for young, middle-aged and old animals
yo = find(ages <= 365);
ma = find(ages > 365 & ages <= 3*365);
ol = find(ages > 3*365);
% find the indices for male and female animals
F = find(sex == 0);
M = find(sex == 1);
% get the best estimate of spontaneous rate
srs = nan(1,largeNumber);
for i = 1:largeNumber
if ~isnan(srsT(1,i))
srs(1,i) = srsT(1,i);
elseif ~isnan(srsT(2,i))
srs(1,i) = srsT(2,i);
elseif ~isnan(srsT(3,i))
srs(1,i) = srsT(3,i);
elseif ~isnan(srsT(4,i))
srs(1,i) = srsT(4,i);
end
end
% settings for the plots
fs = 22; % fontsize
yo_sym = 'o'; yo_col = 'b'; % symbol and color to plot data of young-adult gerbils
ma_sym = '^'; ma_col = [251 186 0]./256; % symbol and color to plot data of middle-aged gerbils
ol_sym = 's'; ol_col = 'r'; % symbol and color to plot data of quiet_aged gerbils
sz = 70; % markersize
% ====== BF vs threshold - Fig 6A ======
% set limitations and ticks of the two axes
xl = [0.3 20];
yl = [0 100];
xt = [0.2 0.5 1 2 5 10 20];
% make running average
logvec = logspace(log10(xl(1)),log10(xl(2)),25);
YLra = nan(3,length(logvec)-1);
BFra = nan(1,length(logvec)-1);
for Age = 1:3 % loop through the age groups
if Age == 1; ag = yo;
elseif Age == 2; ag = ma;
else; ag = ol;
end
for i = 1:length(logvec)-1 % loop through the logarithmically spaced vector
idx = find(bfs(ag)/1000>logvec(i) & bfs(ag)/1000<=logvec(i+1));
data = thr(ag);
YLra(Age,i) = nanmean(data(idx));
BFra(1,i) = logspace(log10(logvec(i)),log10(logvec(i+1)),1);
end
end
end
%% Plot figure 6
if flags.do_fig6
% ====== Fig 6A - Thresholds ======
figure;
scatter(bfs(yo)/1000,thr(yo),sz,yo_col,yo_sym)
hold on
scatter(bfs(ma)/1000,thr(ma),sz,ma_col,ma_sym)
scatter(bfs(ol)/1000,thr(ol),sz,ol_col,ol_sym)
plot(BFra,YLra(1,:),yo_col,'LineWidth',2)
plot(BFra,YLra(2,:),'color',ma_col,'LineWidth',2)
plot(BFra,YLra(3,:),ol_col,'LineWidth',2)
xlabel('Best frequency (kHz)')
ylabel('Threshold (dB SPL)')
set(gca,'FontSize',fs,'XScale','log','XTick',xt)
xlim(xl); ylim(yl);
legend('Young adult','Middle aged','Old','Location','northwest')
% ====== Fig 6B - BF distribution ======
edges = logspace(log10(200),log10(20000),25); % some settings for the histogram
xl = [300 20000];
xt = [200 500 1000 2000 5000 10000 20000];
figure;
histogram(bfs(yo),edges,'FaceColor','k','EdgeColor','w')
set(gca,'XScale','log','FontSize',fs,'XTick',xt,'XTickLabel',{'0.2','0.5','1','2','5','10','20'})
xlim(xl)
ylabel('Unit count')
xlabel('Best frequency (kHz)')
box off
% ====== Fig 6C - SR vs BF ======
xl = [0.3 20]; % some settings
yl = [0 200];
xt = [0.2 0.5 1 2 5 10 20];
figure
plot([3.5 3.5],yl,'--','LineWidth',3,'Color',[0,78,158]/256)
hold on
scatter(bfs(yo)/1000,srs(yo),sz,'k',yo_sym)
text(3.9,190,'BF > 3.5 kHz','Color',[0,78,158]/256,'FontSize',19)
set(gca,'FontSize',fs,'XScale','log','XTick',xt)
xlim(xl)
ylabel('Spontaneous rate (spikes/s)')
xlabel('Best frequency (kHz)')
% ====== Fig 6D - BF vs phase locking ======
xl = [0.3 6]; % some settings
yl = [0 1];
xt = [0.2 0.5 1 2 5 10 20];
% differentiate between low- and high-SR fibers, find the indices
srl = find(srs<18);
srh = find(srs>=18);
ylo = intersect(yo,srl);
yhi = intersect(yo,srh);
mlo = intersect(ma,srl);
mhi = intersect(ma,srh);
olo = intersect(ol,srl);
ohi = intersect(ol,srh);
figure
scatter(bfs(yhi)/1000,vss(yhi),sz,yo_col,yo_sym,'filled')
hold on
scatter(bfs(ylo)/1000,vss(ylo),sz,yo_col,yo_sym,'LineWidth',1)
scatter(bfs(mhi)/1000,vss(mhi),sz,ma_col,ma_sym,'filled')
scatter(bfs(mlo)/1000,vss(mlo),sz,ma_col,ma_sym,'LineWidth',1)
scatter(bfs(ohi)/1000,vss(ohi),sz,ol_col,ol_sym,'filled')
scatter(bfs(olo)/1000,vss(olo),sz,ol_col,ol_sym,'LineWidth',1)
set(gca,'FontSize',fs,'XScale','log','XTick',xt)
xlim(xl)
ylim(yl)
ylabel('Max. vector strength')
xlabel('Best frequency (kHz)')
% plot the legend
x1 = 2.7; x2 = 3.4; x3 = 4;
y1 = 0.92; y2 = 0.86; y3 = 0.8;
fs2 = fs - 6;
scatter(x1,y1,sz,yo_col,yo_sym,'filled')
scatter(x2,y1,sz,yo_col,yo_sym,'LineWidth',1)
scatter(x1,y2,sz,ma_col,ma_sym,'filled')
scatter(x2,y2,sz,ma_col,ma_sym,'LineWidth',1)
scatter(x1,y3,sz,ol_col,ol_sym,'filled')
scatter(x2,y3,sz,ol_col,ol_sym,'LineWidth',1)
text(x3,y1,'Young adult','FontSize',fs2)
text(x3,y2,'Middle aged','FontSize',fs2)
text(x3,y3,'Old','FontSize',fs2)
text(x1,y1+0.04,'high SR','Rotation',45,'FontSize',fs2)
text(x2,y1+0.04,'low SR','Rotation',45,'FontSize',fs2)
end
%% ----------- Make the plots of Figure 7 ---------------
if flags.do_fig7
% ====== Figure 7A -- BF vs CF
figure;
xl = [0.3 20];
xt = [0.2 0.5 1 2 5 10 20];
% plot the main plot
subplot(1, 5, 1:4)
scatter(bfs(yo)/1000,cfs(1,yo)/1000,sz,yo_col,yo_sym,'LineWidth',1.2)
hold on
scatter(bfs(ma)/1000,cfs(1,ma)/1000,sz,ma_col,ma_sym,'LineWidth',1.2)
scatter(bfs(ol)/1000,cfs(1,ol)/1000,sz,ol_col,ol_sym,'LineWidth',1.2)
plot(xl,xl,'--k','LineWidth',1)
xlabel('Best frequency (kHz)')
ylabel('Characteristic frequency (kHz)')
title('Units with CF recording')
set(gca,'FontSize',fs,'XScale','log','XTick',xt,'YScale','log','YTick',xt)
xlim(xl); ylim(xl);
legend('Young adult','Middle aged','Old','Location','northwest')
% plot the histogram of the CF
subplot(1,5,5)
edges = logspace(log10(200),log10(20000),25);
xl = [300 20000];
xt = [200 500 1000 2000 5000 10000 20000];
histogram(cfs(1,:),edges,'FaceColor','k','EdgeColor','w')
set(gca,'XScale','log','XTick',xt,'XTickLabel',{'','','','','','',''},'view',[90 -90],'YTick',[0 5 10],'YTickLabel',{'','',''})
xlim(xl)
box off
% output the total number of units with a CF recording
amt_disp(['Number of units with CF recording: ' num2str(sum(~isnan(cfs(1,:))))],'documentation');
% ====== Figure 7B -- BF vs Click latency
figure;
xl = [0.3 20];
yl = [0 3.5];
xt = [0.2 0.5 1 2 5 10 20];
% select the type of click latency analysis to be plotted
type = 3; % 1 = mean fsl, 2 = median fsl, 3 = 2-bins method, 4 = poisson method
% differentiate between low- and high-SR fibers, find the indices
srl = find(srs<18);
srh = find(srs>=18);
ylo = intersect(yo,srl);
yhi = intersect(yo,srh);
mlo = intersect(ma,srl);
mhi = intersect(ma,srh);
olo = intersect(ol,srl);
ohi = intersect(ol,srh);
% plot the main plot: latencies
subplot(5, 1, 1:4)
scatter(bfs(yhi)/1000,clk(type,yhi)*1000,sz,yo_col,yo_sym,'filled')
hold on
scatter(bfs(ylo)/1000,clk(type,ylo)*1000,sz,yo_col,yo_sym,'LineWidth',1)
scatter(bfs(mhi)/1000,clk(type,mhi)*1000,sz,ma_col,ma_sym,'filled')
scatter(bfs(mlo)/1000,clk(type,mlo)*1000,sz,ma_col,ma_sym,'LineWidth',1)
scatter(bfs(ohi)/1000,clk(type,ohi)*1000,sz,ol_col,ol_sym,'filled')
scatter(bfs(olo)/1000,clk(type,olo)*1000,sz,ol_col,ol_sym,'LineWidth',1)
ylabel('Click latency (ms)')
title('Units with CLICK recording')
set(gca,'FontSize',fs,'XScale','log','XTick',xt,'XTickLabel',{'','','','','','',''})
xlim(xl); ylim(yl);
% create the legend
x1 = 5; x2 = 6.5; x3 = 8; % x-coordinates legend
y1 = 2.8; y2 = 2.55; y3 = 2.3; % y-coordinates legend
fs2 = fs - 6; % fontsize legend
scatter(x1,y1,sz,yo_col,yo_sym,'filled')
scatter(x2,y1,sz,yo_col,yo_sym,'LineWidth',1)
scatter(x1,y2,sz,ma_col,ma_sym,'filled')
scatter(x2,y2,sz,ma_col,ma_sym,'LineWidth',1)
scatter(x1,y3,sz,ol_col,ol_sym,'filled')
scatter(x2,y3,sz,ol_col,ol_sym,'LineWidth',1)
text(x3,y1,'Young adult','FontSize',fs2)
text(x3,y2,'Middle aged','FontSize',fs2)
text(x3,y3,'Old','FontSize',fs2)
text(x1,y1+0.15,'high SR','Rotation',45,'FontSize',fs2)
text(x2,y1+0.15,'low SR','Rotation',45,'FontSize',fs2)
% plot the histogram of the CF distribtion
subplot(5,1,5);
edges = logspace(log10(200),log10(20000),25);
xl = [300 20000];
xt = [200 500 1000 2000 5000 10000 20000];
clki = find(~isnan(clk(type,:)));
histogram(bfs(clki),edges,'FaceColor','k','EdgeColor','w')
set(gca,'FontSize',fs,'XScale','log','XTick',xt,'XTickLabel',{'0.2','0.5','1','2','5','10','20'},'YTick',0,'YTickLabel',{''})
xlim(xl)
xlabel('Best frequency (kHz)')
box off
% output the total number of units with a CLICK recording
amt_disp(['Number of units with CLICK recording: ' num2str(length(clki))],'documentation');
% ===== Figure 7C -- BF vs SR
xl = [0.3 20];
xt = [0.2 0.5 1 2 5 10 20];
yl = [0 150];
% plot the main plot
figure;
scatter(bfs(yo)/1000,srsT(1,yo),sz,yo_col,yo_sym,'LineWidth',2)
hold on
scatter(bfs(ma)/1000,srsT(1,ma),sz,ma_col,ma_sym,'LineWidth',2)
scatter(bfs(ol)/1000,srsT(1,ol),sz,ol_col,ol_sym,'LineWidth',2)
xlabel('BF (kHz)')
ylabel('SR (spikes/s)')
title('Units w/ SR')
set(gca,'FontSize',fs+10,'XScale','log','XTick',xt);%,'YScale','log','YTick',xt)
xlim(xl); ylim(yl);
% output the total number of units with a SR recording
amt_disp(['Number of units with SR recording: ' num2str(sum(~isnan(srsT(1,:))))],'documentation');
% ==== Figure 7D -- BF vs threshold, NOISE
xl = [0.3 20];
xt = [1 10];
yl = [0 90];
yt = [0 40 80];
lw = 2;
noise = find(cmx(1,:) == 1);
yoi = intersect(yo,noise);
mai = intersect(ma,noise);
oli = intersect(ol,noise);
% plot the data
figure;
scatter(bfs(yoi)/1000,thr(yoi),sz,yo_col,yo_sym,'LineWidth',lw)
hold on
scatter(bfs(mai)/1000,thr(mai),sz,ma_col,ma_sym,'LineWidth',lw)
scatter(bfs(oli)/1000,thr(oli),sz,ol_col,ol_sym,'LineWidth',lw)
xlabel('BF (kHz)')
ylabel('Threshold (dB SPL)')
title('Units w/ NOISE')
set(gca,'FontSize',fs+10,'XScale','log','XTick',xt,'XTickLabel',{'1','10'},'YTick',yt)
xlim(xl); ylim(yl);
% output the total number of units with a NOISE recording
amt_disp(['Number of units with NOISE recording: ' num2str(length(noise))],'documentation');
% ==== Figure 7E -- BF vs threshold, SPS
sps = find(cmx(2,:) == 1);
yoi = intersect(yo,sps);
mai = intersect(ma,sps);
oli = intersect(ol,sps);
% plot the data
figure;
scatter(bfs(yoi)/1000,thr(yoi),sz,yo_col,yo_sym,'LineWidth',lw)
hold on
scatter(bfs(mai)/1000,thr(mai),sz,ma_col,ma_sym,'LineWidth',lw)
scatter(bfs(oli)/1000,thr(oli),sz,ol_col,ol_sym,'LineWidth',lw)
xlabel('BF (kHz)')
title('Units w/ SPS')
set(gca,'FontSize',fs+10,'XScale','log','XTick',xt,'XTickLabel',{'1','10'},'YTick',yt)
xlim(xl); ylim(yl);
% output the total number of units with a SPS recording
amt_disp(['Number of units with SPS recording: ' num2str(length(sps))],'documentation');
% === Figure 7F -- BF vs threshold, CVC
cvc = find(cmx(3,:) == 1);
yoi = intersect(yo,cvc);
mai = intersect(ma,cvc);
oli = intersect(ol,cvc);
% plot the data
figure;
scatter(bfs(yoi)/1000,thr(yoi),sz,yo_col,yo_sym,'LineWidth',lw)
hold on
scatter(bfs(mai)/1000,thr(mai),sz,ma_col,ma_sym,'LineWidth',lw)
scatter(bfs(oli)/1000,thr(oli),sz,ol_col,ol_sym,'LineWidth',lw)
xlabel('BF (kHz)')
title('Units w/ CVC')
set(gca,'FontSize',fs+10,'XScale','log','XTick',xt,'XTickLabel',{'1','10'},'YTick',yt)
xlim(xl); ylim(yl);
% output the total number of units with a CVC recording
amt_disp(['Number of units with CVC recording: ' num2str(length(cvc))],'documentation');
end
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