Table 6.

MATLAB code for finding burst-pause patterns using RGS output

function [BSPB, BDPB] = BurstPausePatternDetector(Bursts,Pauses,dt)

%% BURSTPAUSEPATTERNDETECTOR help

%

% Identifies patterns in bursting and pausing using outputs from

% RGSDetect.

%

% INPUTS:

% Bursts: Structure containing burst information obtained from RGSDetect.

% Pauses: Structure containing pause information obtained from RGSDetect.

% dt: Minimum threshold connecting burst and pause events. Argument can

% take single threshold or vector of thresholds.

%

% OUTPUTS:

% NOTE: Each structure element of both outputs have L cells containing

% NxM matrices, where L is the length of vector dt, N is the number

% of hits and M is the length of the pattern. Each element of the cell

% contains the results of its respective threshold in dt.

% NOTE: Thresholds dynamically attenuated so they do not fall past

% more than one event.

%

% BSPB: Structure containing patterns associated with string pauses.

% BSPB.BSPHits (s): Cell of 2 column matrices. The matrices

% contain string pauses falling within the threshold past bursts.

% Each row is one pattern where the first column contains burst

% start times, and the second column contains pause start times.

% BSPB.SPBHits (s): Cell of 2 column matrices. The matrices

% contain bursts falling within the threshold past string pauses.

% Each row is one pattern where the first column contains string

% pause start times, and the second column contains burst start times.

% BSPB.BSPBHits (s): Cell of 3 column matrices. The matrices

% contain bursts falling within the threshold past string pauses.

% Each row is one pattern where the first column contains string

% pause start times, and the second column contains burst start times.

% BDPB: Structure containing patterns associated with discrete

% pauses.

% BDPB.BDPHits (s): Cell of 2 column matrices. The matrices

% contain discrete pauses falling within the threshold past bursts.

% Each row is one pattern where the first column contains burst

% start times, and the second column contains pause start times.

% BDPB.DPBHits (s): Cell of 2 column matrices. The matrices

% contain bursts falling within the threshold past discrete pauses.

% Each row is one pattern where the first column contains

% discrete pause start times, and the second column contains

% burst start times.

% BDPB.BDPBHits (s): Cell of 3 column matrices. The matrices

% contain bursts falling within the threshold past discrete pauses.

% Each row is one pattern where the first column contains

% discrete pause start times, and the second column contains

% burst start times.

%

% EXAMPLE values used in this paper:

% Spikes = (Spike times go here);

% N_min = 2;

% Steps = -3:0.005:1.5;

% p = 0.05;

% alpha = 0.05;

% [Bursts, Pauses] = RGSDetect(Spikes, N_min, Steps, p, alpha);

% dt = [3 6 9 12 15 18 21];

% [BSPB, BDPB] = BurstPausePatternDetector(Bursts,Pauses,dt);

%

%% Initialization

BWindows = Bursts.Windows;

BurstStarts = BWindows(:,1);

BurstEnds = BWindows(:,2);

PWindows = Pauses.Windows;

PauseStarts = PWindows(:,1);

PauseEnds = PWindows(:,2);

DPStarts = Pauses.AllSpikes;

DPEnds = Pauses.AllSpikes + Pauses.AllLengths;

%% **Burst-String Pause-Burst** Search

%B-SP Sub-search

BSPHits = cell(length(dt),1);

for i = 1:length(dt)

%Add threshold to end of all burst events.

edges = [BurstEnds BurstEnds+dt(i)/60]';

edges = edges(:)';

%Fix ranges that overlap.

edges(diff(edges) <= 0) = edges(logical([0 diff(edges)<=0])) - 10^(-8);

%Histogram count the string pauses using ranges (edges).

[N,∼,bin] = histcounts(PauseStarts,edges);

%Take only the first string pause after each burst.

bin(logical([0;diff(bin) == 0]')) = 0;

%Use the results from even bins.

bin(mod(bin,2) == 0) = 0;

bin(bin ∼= 0) = 1;

N(2:2:end) = [];

%Assign results to BSPHits.

BSPHits(i) = {[BurstStarts(N∼=0) PauseStarts(logical(bin))]};

end

BSPB.BSPHits = BSPHits;

%SP-B Sub-search

SPBHits = cell(length(dt),1);

for i = 1:length(dt)

edges = [PauseEnds PauseEnds+dt(i)/60]';

edges = edges(:)';

edges(diff(edges) <= 0) = edges(logical([0 diff(edges)<=0])) - 10^(-8);

[N,∼,bin] = histcounts(BurstStarts,edges);

bin(logical([0;diff(bin) == 0]')) = 0;

bin(mod(bin,2) == 0) = 0;

bin(bin ∼= 0) = 1;

N(2:2:end) = [];

SPBHits(i) = {[PauseStarts(N∼=0) BurstStarts(logical(bin))]};

end

BSPB.SPBHits = SPBHits;

%B-SP-B Concatenation

BSPBHits = cell(length(dt),1);

for i = 1:length(dt)

%Obtain results from BSP and SPB

BSPi = BSPHits{i}; SPBi = SPBHits{i};

%Find common start times of string pauses.

[hits,ia,ib] = intersect(BSPi(:,2),SPBi(:,1));

%Concatenate the common BSP and SPB patterns into a 3 event pattern.

BSPBHits(i) = {[BSPi(ia,1) hits SPBi(ib,2)]};

end

BSPB.BSPBHits = BSPBHits;

%% **Burst-Discrete Pause-Burst** Search

%B-DP Sub-search

BDPHits = cell(length(dt),1);

for i = 1:length(dt)

edges = [BurstEnds BurstEnds+dt(i)/60]';

edges = edges(:)';

edges(diff(edges) <= 0) = edges(logical([0 diff(edges)<=0])) - 10^(-8);

[N,∼,bin] = histcounts(DPStarts,edges);

bin(logical([0;diff(bin) == 0]')) = 0;

bin(mod(bin,2) == 0) = 0;

bin(bin ∼= 0) = 1;

N(2:2:end) = [];

BDPHits(i) = {[BurstStarts(N∼=0) DPStarts(logical(bin))]};

end

BDPB.BDPHits = BDPHits;

%DP-B Sub-search

DPBHits = cell(length(dt),1);

for i = 1:length(dt)

edges = [DPEnds DPEnds+dt(i)/60]';

edges = edges(:)';

edges(diff(edges) <= 0) = edges(logical([0 diff(edges)<=0])) - 10^(-8);

[N,∼,bin] = histcounts(BurstStarts,edges);

bin(logical([0;diff(bin) == 0]')) = 0;

bin(mod(bin,2) == 0) = 0;

bin(bin ∼= 0) = 1;

N(2:2:end) = [];

DPBHits(i) = {[DPStarts(N∼=0) BurstStarts(logical(bin))]};

end

BDPB.DPBHits = DPBHits;

%B-DP-B Concatenation

BDPBHits = cell(length(dt),1);

for i = 1:length(dt)

BDPi = BDPHits{i}; DPBi = DPBHits{i};

[hits,ia,ib] = intersect(BDPi(:,2),DPBi(:,1));

BDPBHits(i) = {[BDPi(ia,1) hits DPBi(ib,2)]};

end

BDPB.BDPBHits = BDPBHits;

end