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initial attempt at coactive z swr burst v noburst
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,178 @@ | ||
| %{ | ||
| meant to be an updated version on calcxcorrmeasures | ||
| that is compatible with my changes to singlecellanal (which is actually | ||
| feeding in multiple su).. | ||
| .. i.e. the way data is fed in as a varargin | ||
| also see work i had done for mu xcorr in dfa_perripspikingcorr | ||
| also looks like i did something similar with: dfa_lickBoutSpikeCorr.. | ||
| except that i took out the coactive z stuff. also it looks like i was using | ||
| that to run MU | ||
| %} | ||
|
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| function out = dfa_suCoactiveXcorr(idx, excludetimes, varargin) | ||
| %function out = calcxcorrmeasures(index, excludetimes, spikes, varargin) | ||
| % Calculates the excess correlation and RMS time lag for the specified cell | ||
| % pairs using only spikes not excluded by the excludetimes | ||
| % | ||
| % | ||
| % Options: | ||
| % 'bin', n binsize in sec. Default 0.002 (2 ms) | ||
| % 'tmax', n maximum time for cross correlation. Default 1 sec. | ||
| % 'edgespikes', 0 or 1 | ||
| % 0 will enforce the exclude periods and will thus | ||
| % exclude all spikes outside of the exclude windows. (default) | ||
| % 1 will include all spikes where at least one of the spikes is | ||
| % not excluded | ||
| % 'sw1', n The smaller smoothing width for the excess correlation measure | ||
| % Default 0.005 (5 ms) | ||
| % 'sw2', n The larger smoothing width for the excess correlation measure | ||
| % Default 0.250 (250 ms) | ||
| % 'rmstmax', n The maximum absolute time to be used for the rms calculation | ||
| % Default 0.1 (100 ms) | ||
| % 'rmsmincounts', n The minimum number of counts in the histogram for the rms | ||
| % measurement to be carried out. | ||
| % Default 50. | ||
| % 'calclinfields', 1 or 0 -- set to 1 to calculate linearized place fields | ||
| % 'calctrajxcorr', 1 or 0 -- set to 1 to calculate one cross correlation per | ||
| % trajectory (default 0); | ||
| % | ||
|
|
||
| calclinfields = 0; | ||
| calctrajxcorr = 0; | ||
| appendindex = 0; | ||
| bin = 0.002; | ||
| tmax = 1; | ||
| edgespikes = 0; | ||
| sw1 = 0.005; | ||
| sw2 = 0.250; | ||
| rmstmax = 0.1; | ||
| rmsmincounts = 50; | ||
|
|
||
| if ~isempty(varargin) | ||
| assign(varargin{:}) | ||
| end | ||
|
|
||
| % set the defaults | ||
| out.time = []; | ||
| out.c1vsc2 = []; | ||
| out.ac1 = []; | ||
| out.ac2 = []; | ||
| out.index = idx; | ||
| out.ec = NaN; | ||
| out.rms = NaN; | ||
| out.probcoa = NaN; | ||
| out.coactivez_numsp = NaN; | ||
| out.coactivez = NaN; | ||
| out.lf1 = []; | ||
| out.lf2 = []; | ||
|
|
||
| % for each cell we calculate the cross correlation | ||
| try | ||
| t1 = spikes{idx(1)}{idx(2)}{idx(3)}{idx(4)}.data; | ||
| t2 = spikes{idx(1)}{idx(2)}{idx(5)}{idx(6)}.data; | ||
| catch | ||
| % if either of those produced an error, we return NaNs | ||
| return; | ||
| end | ||
|
|
||
| %apply the exclude rule | ||
| t1inc = []; | ||
| t2inc = []; | ||
| if (length(t1)) | ||
| t1inc = t1(find(~isExcluded(t1(:,1), excludetimes)),1); | ||
| else | ||
| return | ||
| end | ||
| if (length(t2)) | ||
| t2inc = t2(find(~isExcluded(t2(:,1), excludetimes)),1); | ||
| else | ||
| return | ||
| end | ||
|
|
||
| ac1 = spikexcorr(t1inc, t1inc, bin, tmax); | ||
| ac2 = spikexcorr(t2inc, t2inc, bin, tmax); | ||
| out.ac1 = ac1.c1vsc2; | ||
| out.ac2 = ac2.c1vsc2; | ||
| out.time = ac1.time; | ||
|
|
||
| out.index = idx; | ||
| if (~calctrajxcorr) | ||
| xc = spikexcorr(t1inc, t2inc, bin, tmax); | ||
| % if we want to include edge spikes, we need to add in the correlation of | ||
| % the excluded t1 spikes with the included t2spikes | ||
| if ((edgespikes) & (~isempty(xc.time))) | ||
| t1ex = t1(find(isExcluded(t1(:,1), excludetimes))); | ||
| if (~isempty(t1ex)) | ||
| tmpxc = spikexcorr(t1ex, t2inc, bin, tmax); | ||
| % add these values to the original histogram | ||
| xc.c1vsc2 = xc.c1vsc2 + tmpxc.c1vsc2; | ||
| end | ||
| end | ||
| out.c1vsc2 = xc.c1vsc2; | ||
| % compute the excess correlation at 0 lag | ||
| out.ec = excesscorr(xc.time, xc.c1vsc2, xc.nspikes1, xc.nspikes2, sw1, sw2); | ||
| out.rms = xcorrrms(xc.time, xc.c1vsc2, rmstmax, rmsmincounts); | ||
|
|
||
| % get the probability that both were coactive in the included intervals | ||
| n1 = nInInterval(t1inc, excludetimes, 0); | ||
| n2 = nInInterval(t2inc, excludetimes, 0); | ||
| out.probcoa = mean(logical(n1) & logical(n2)); | ||
|
|
||
| % get the zscore for this probabliity of coactivity | ||
| out.coactivez = coactivezscore(n1, n2); | ||
| out.coactivez_numsp = coactivezscore(n1, n2, 'anyspikes', 0); | ||
| else | ||
| % calculated individual trajectory cross correlations | ||
| statematrix = linpos{idx(1)}{idx(2)}.statematrix; | ||
| statevector = statematrix.traj; | ||
| statevector(find(isExcluded(statematrix.time, excludetimes))) = -1; | ||
| % look up the state for each spike | ||
| posindexfield = 7; | ||
| t1 = t1(:,[1 posindexfield]); | ||
| t2 = t2(:,[1 posindexfield]); | ||
| t1(:,3) = statevector(t1(:,2)); | ||
| t2(:,3) = statevector(t2(:,2)); | ||
| % get rid of -1 trajectories and excluded times | ||
| t1 = t1(find(t1(:,3) ~= -1),:); | ||
| t2 = t2(find(t2(:,3) ~= -1),:); | ||
| t2 = t2(find(~isExcluded(t2(:,1), excludetimes)), :); | ||
| for i = 1:max(statevector) | ||
| t1tmp = t1(find(t1(:,3) == i), 1); | ||
| t1inc = t1tmp(find(~isExcluded(t1tmp, excludetimes))); | ||
| t2tmp = t2(find(t2(:,3) == i), 1); | ||
| t2inc = t2tmp(find(~isExcluded(t2tmp, excludetimes))); | ||
| xc = spikexcorr(t1inc, t2inc, bin, tmax); | ||
| if ((edgespikes) & (~isempty(xc.time))) | ||
| t1ex = t1tmp(find(isExcluded(t1tmp, excludetimes))); | ||
| if (~isempty(t1ex)) | ||
| tmpxc = spikexcorr(t1ex, t2inc, bin, tmax); | ||
| % add these values to the original histogram | ||
| xc.c1vsc2 = xc.c1vsc2 + tmpxc.c1vsc2; | ||
| end | ||
| end | ||
| out.c1vsc2{i} = xc.c1vsc2; | ||
| % compute the excess correlation at 0 lag | ||
| out.ec(i) = excesscorr(xc.time, xc.c1vsc2, xc.nspikes1, xc.nspikes2, sw1, sw2); | ||
| out.rms(i) = xcorrrms(xc.time, xc.c1vsc2, rmstmax, rmsmincounts); | ||
|
|
||
| % get the probability that both were coactive in the included intervals | ||
| n1 = nInInterval(t1inc, excludetimes, 0); | ||
| n2 = nInInterval(t2inc, excludetimes, 0); | ||
| out.probcoa(i) = mean(logical(n1) & logical(n2)); | ||
|
|
||
| % get the zscore for this probabliity of coactivity | ||
| out.coactivez(i) = coactivezscore(n1, n2); | ||
| out.coactivez_numsp(i) = coactivezscore(n1, n2, 'anyspikes', 0); | ||
| end | ||
| end | ||
|
|
||
| if (calclinfields) | ||
| % calculate the linear fields | ||
| out.lf1 = filtercalclinfields(idx(1:4), excludetimes, spikes, linpos, 'phasedist', 1); | ||
| out.lf2 = filtercalclinfields([idx(1:2) idx(5:6)], excludetimes, spikes, linpos, 'phasedist', 1); | ||
| end | ||
|
|
||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,65 @@ | ||
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| create_filter = 0; | ||
| run_ff = 0; | ||
| save_ffdata = 0; | ||
| load_ffdata = 1; | ||
|
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| %% get the lickphase for each swr | ||
| % data filter params | ||
| Fp.animals = {'D10'}; %, 'D12', 'D13', 'JZ1', 'JZ2', 'JZ3', 'JZ4'}; %, 'JZ2', 'JZ4'}; | ||
| Fp.filtfunction = 'dfa_suCoactiveXcorr'; | ||
| % condition = 'lickburst'; | ||
| condition = 'noburst'; | ||
| % Fp.params = {'savefigs', 'wtrackdays', 'ripples', 'lickbouts', Fp.filtfunction}; | ||
| Fp.params = {'savefigs', 'wtrackdays', 'ripples', 'nolickbouts', Fp.filtfunction}; | ||
| % FF | ||
| Fp = load_filter_params(Fp); | ||
| if create_filter | ||
| F = createfilter('animal', Fp.animals, 'epochs', Fp.epochfilter, ... | ||
| 'excludetime', Fp.timefilter,'cellpairs', Fp.cellpairfilter, 'iterator',Fp.iterator); | ||
| F = setfilterfunction(F, Fp.filtfunction, Fp.datatypes, Fp.options{:}); | ||
| end | ||
| if run_ff; F = runfilter(F); | ||
| for d = 1:length(F); F(d).datafilter_params = Fp; end | ||
| end | ||
| if save_ffdata | ||
| save_data(F, Fp.paths.filtOutputDirectory, Fp.paths.filenamesave, ... | ||
| 'filetail', sprintf('_%s_%s', Fp.epochEnvironment, condition)); | ||
| end | ||
| if load_ffdata | ||
| Flb = load_data(Fp.paths.filtOutputDirectory, Fp.paths.filenamesave, Fp.animals, ... | ||
| 'filetail', sprintf('_%s_%s', Fp.epochEnvironment, 'lickburst')); | ||
| Fno = load_data(Fp.paths.filtOutputDirectory, Fp.paths.filenamesave, Fp.animals, ... | ||
| 'filetail', sprintf('_%s_%s', Fp.epochEnvironment, 'noburst')); | ||
| end | ||
| %% | ||
| % i need to combine epoch results for each cell pair (take mean?) | ||
| % plot the cumulative coactive z for each condition. run kstest? | ||
| ani = 1; | ||
| Flb_struct = cell2mat(Flb(ani).output{1}'); | ||
| Flb_coZ = [Flb_struct.coactivez]'; | ||
|
|
||
| Fno_struct = cell2mat(Fno(ani).output{1}'); | ||
| Fno_coZ = [Fno_struct.coactivez]'; | ||
| %% | ||
| [lickh, lickb] = histcounts(Flb_coZ,1000,'Normalization', 'cdf'); | ||
| plot(lickb(1:end-1)+diff(lickb) / 2, lickh, 'k') | ||
| hold on; | ||
| [noh, nob] = histcounts(Fno_coZ,1000,'Normalization', 'cdf'); | ||
| plot(nob(1:end-1)+diff(nob) / 2, noh, 'b') | ||
| hold off | ||
| [H, pValue, KSstatistic] = kstest2(Fno_coZ, Flb_coZ); | ||
| %% | ||
|
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| histogram(Flb_coZ, 40, 'Normalization', 'probability') | ||
| hold on; | ||
| histogram(Fno_coZ, 40, 'Normalization', 'probability', 'DisplayStyle','stairs', 'linewidth', 2) | ||
| hold off | ||
| legend({'lickburst swr', 'nonburst swr'}) | ||
| title('coactiveZ ca1') | ||
| save_figure([pconf.andef{4} '/coactiveZburstNoburst/'], 'coactiveZburstNoburst_ca1'); | ||
|
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| % then for each pair get diff of coactiveZ for burst-noburst... and then | ||
| % generate a condition-shuffled distribution of diff coactiveZ.. to | ||
| % compare that to? |