```
function [spikeRatePerNeuron,spikeRatePerBin] = ...
kelvasa2015_anbinning(APvec,sigLengthSec,...
varargin)
%KELVASA2015_ANBINNING AN and time binning from Kelvasa and Dietz 2015 binaural model
% Usage: [spikeRatePerNeuron,spikeRatePerBin] = ...
% kelvasa2015_anbinning(APvec,sigLengthSec);
%
% Input parameters:
% APvec : N x 2 matrix of AN spikes with Nx1 holding indices
% of the spiking neuron and Nx2 holding corresponding
% spike time in seconds.
%
% sigLengthSec : length of input signal in seconds
%
% Output parameters:
% spikeRatePerNeuron: N x M matrix of AN spike rates in spikes/second
% with N being the number of user defined AN
% fibers and M being the number of time windows.
%
% spikeRatePerBin : N x M matrix of AN spike rates in spikes/second
% with N being the number of user defined AN fibe
% bands and M being the number of time windows.
%
% KELVASA2015_anbinning(APvec,sigLengthSec,varargin) bins auditory nerve
% spike times over a given population of AN fibers into user defined AN
% frequency bands and time bins as detailed in (Kelvasa & Dietz (2015))
%
% References:
% D. Kelvasa and M. Dietz. Auditory model-based sound direction
% estimation with bilateral cochlear implants. Trends in Hearing,
% 19:2331216515616378, 2015.
%
%
% Url: http://amtoolbox.org/amt-1.3.0/doc/modelstages/kelvasa2015_anbinning.php
% #StatusDoc: Good
% #StatusCode: Good
% #Verification: Unknown
% #Requirements: MATLAB M-Signal M-Stats
% #Author: Daryl Kelvasa (2016)
% #Author: Mathias Dietz (2016)
% #Author: Clara Hollomey (2022)
% 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.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Check input paramters
if nargin<3
error('%s: Too few input parameters.',upper(mfilename));
end;
%Retrieve and compute model paramters
definput.import={'kelvasa2015'};
[~,kv] = ltfatarghelper({},definput,varargin);
%% Main code
%initialize variables
numWin = ceil(sigLengthSec/kv.timeWindowSec);
winEdges = linspace(0,sigLengthSec,numWin+1);
spikeRatePerNeuron = zeros(kv.N_nervecells,numWin);
spikeRatePerBin = zeros(kv.numBin,numWin);
if ~isempty(APvec)
[~,ind] = histc(APvec(:,2),winEdges);
ind(ind==numWin+1) = numWin;
for win = 1 : numWin
APwin = APvec(ind == win,:);
[H, ~] = histc(APwin(:,1),1:kv.N_nervecells);
clear APwin
spkRate = H./kv.timeWindowSec;
spikeRatePerNeuron(:,win) = spkRate;
spikeRatePerBin(:,win) = mean(spkRate(kv.binPosInd),2);
end
end
end
```