KilosortConfiguration.m

function ops = KilosortConfiguration(XMLfile)

% Loads xml parameters (Neuroscope)
xml = LoadXml(XMLfile);
% Define rootpath
rootpath = fileparts(XMLfile);

ops.GPU                 = 1; % whether to run this code on an Nvidia GPU (much faster, mexGPUall first)
ops.parfor              = 1; % whether to use parfor to accelerate some parts of the algorithm
ops.verbose             = 1; % whether to print command line progress
ops.showfigures         = 0; % whether to plot figures during optimization
ops.datatype            = 'dat';  % binary ('dat', 'bin') or 'openEphys'
ops.fbinary             = [XMLfile(1:end-3) 'dat']; % will be created for 'openEphys'

ops.root                = rootpath; % 'openEphys' only: where raw files are
ops.fs                  = xml.SampleRate;        % sampling rate

load(fullfile(rootpath,'chanMap.mat'))
ops.NchanTOT            = length(connected); % total number of channels

ops.Nchan = sum(connected>1e-6); % number of active channels

templatemultiplier = 8; % 8 times more templates created than Nchan
ops.Nfilt = ops.Nchan*templatemultiplier - mod(ops.Nchan*templatemultiplier,32); % number of filters to use (2-4 times more than Nchan, should be a multiple of 32)

ops.nt0 = round(1.6*ops.fs/1000); % window width in samples. 1.6ms at 20kH corresponds to 32 samples

ops.nNeighPC            = min([16 ops.Nchan]); % visualization only (Phy): number of channnels to mask the PCs, leave empty to skip (12)
ops.nNeigh              = min([16 ops.Nchan]); % visualization only (Phy): number of neighboring templates to retain projections of (16)

% options for channel whitening
ops.whitening           = 'full'; % type of whitening (default 'full', for 'noSpikes' set options for spike detection below)
ops.nSkipCov            = 1; % compute whitening matrix from every N-th batch (1)
ops.whiteningRange      = min([64 ops.Nchan]); % how many channels to whiten together (Inf for whole probe whitening, should be fine if Nchan<=32)

% define the channel map as a filename (string) or simply an array
ops.chanMap             = fullfile(rootpath,'chanMap.mat'); % make this file using createChannelMapFile.m
ops.criterionNoiseChannels = 0.00001; % fraction of "noise" templates allowed to span all channel groups (see createChannelMapFile for more info).

% other options for controlling the model and optimization
ops.Nrank            = 3;    % matrix rank of spike template model (3)
ops.nfullpasses      = 6;    % number of complete passes through data during optimization (6)
ops.maxFR            = 40000;  % maximum number of spikes to extract per batch (20000)
ops.fshigh           = 500;   % frequency for high pass filtering
ops.fslow            = 8000;   % frequency for low pass filtering (optional)
ops.ntbuff           = 64;    % samples of symmetrical buffer for whitening and spike detection
ops.scaleproc        = 200;   % int16 scaling of whitened data
ops.NT               = 32*1028+ ops.ntbuff;% this is the batch size (try decreasing if out of memory) for GPU should be multiple of 32  + ntbuff

% the following options can improve/deteriorate results.
% when multiple values are provided for an option, the first two are beginning and ending anneal values,
% the third is the value used in the final pass.
ops.Th               = [6 10 10];    % threshold for detecting spikes on template-filtered data ([6 12 12])
ops.lam              = [12 40 40];   % large means amplitudes are forced around the mean ([10 30 30])
ops.nannealpasses    = 4;            % should be less than nfullpasses (4)
ops.momentum         = 1./[20 800];  % start with high momentum and anneal (1./[20 1000])
ops.shuffle_clusters = 1;            % allow merges and splits during optimization (1)
ops.mergeT           = .1;           % upper threshold for merging (.1)
ops.splitT           = .1;           % lower threshold for splitting (.1)

% options for initializing spikes from data
ops.initialize      = 'fromData';    %'fromData' or 'no'
ops.spkTh           = -4;      % spike threshold in standard deviations (4)
ops.loc_range       = [3  1];  % ranges to detect peaks; plus/minus in time and channel ([3 1])
ops.long_range      = [30  6]; % ranges to detect isolated peaks ([30 6])
ops.maskMaxChannels = 8;       % how many channels to mask up/down ([5])
ops.crit            = .65;     % upper criterion for discarding spike repeates (0.65)
ops.nFiltMax        = 80000;   % maximum "unique" spikes to consider (10000)

% load predefined principal components (visualization only (Phy): used for features)
dd                  = load('PCspikes2.mat'); % you might want to recompute this from your own data
ops.wPCA            = dd.Wi(:,1:7); % PCs

% options for posthoc merges (under construction)
ops.fracse  = 0.1; % binning step along discriminant axis for posthoc merges (in units of sd)
ops.epu     = Inf;
ops.ForceMaxRAMforDat   = 15000000000; % maximum RAM the algorithm will try to use; on Windows it will autodetect.

% Saving xml content to ops strucuture
ops.xml = xml;

% Specify if the output should be exported to Phy and/or Neurosuite
ops.export.phy = 1;
ops.export.neurosuite = 0;

end