-
Notifications
You must be signed in to change notification settings - Fork 12
/
KilosortConfiguration.m
86 lines (68 loc) · 5.02 KB
/
KilosortConfiguration.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
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