Large data files removed

Author: wqmalik

.gitattributes

@@ -0,0 +1,2 @@
+# Disable LF normalization for all files
+* -text

Analysis.m

@@ -249,8 +249,8 @@
                 elseif(strcmp(fitType,'binomial'))
                     cifObj.lambdaDelta = simplify(exp(beta*cifObj.varIn  + cifObj.histCoeffs*cifObj.histVars)./(1+exp(beta*cifObj.varIn  + cifObj.histCoeffs*cifObj.histVars)));
                     cifObj.lambdaDeltaGamma = simplify(exp(beta*cifObj.varIn  + cifObj.histCoeffVars*cifObj.histVars)./(1+exp(beta*cifObj.varIn  + cifObj.histCoeffVars*cifObj.histVars)));
-                    cifObj.lambdaDeltaFunction = matlabFunction(cifObj.lambdaDelta,'vars',[cifObj.varIn; cifObj.histVars]);
-                    cifObj.lambdaDeltaGammaFunction = matlabFunction(cifObj.lambdaDeltaGamma,'vars',[cifObj.varIn; cifObj.histVars; histCoeffsVarsTrans]);
+                    cifObj.lambdaDeltaFunction = matlabFunction(cifObj.lambdaDelta,'vars',symvar([cifObj.varIn; cifObj.histVars]));
+                    cifObj.lambdaDeltaGammaFunction = matlabFunction(cifObj.lambdaDeltaGamma,'vars',symvar([cifObj.varIn; cifObj.histVars; histCoeffsVarsTrans]));
                 end
 
 

CIF.m

@@ -38,29 +38,29 @@
         %all of the computations for the PPAF are done symbolically based
         %on the CIF object. However, it also means that this version is
         %must slower than the linear version below.
-        function [x_p, W_p, x_u, W_u, x_uT,W_uT,x_pT, W_pT] = PPDecodeFilter(A, Q, dN,lambdaCIFColl,binwidth,x0,Pi0, yT,PiT,estimateTarget)  
+        function [x_p, W_p, x_u, W_u, x_uT,W_uT,x_pT, W_pT] = PPDecodeFilter(A, Q, Px0, dN,lambdaCIFColl,binwidth,x0,Pi0, yT,PiT,estimateTarget)  
             % A can be static or can be a different matrix for each time N
 
             [C,N]   = size(dN); % N time samples, C cells
 
             ns=size(A,1); % number of states
 
 
-            if(nargin<11 || isempty(estimateTarget))
+            if(nargin<12 || isempty(estimateTarget))
                 estimateTarget=0;
             end
 
-            if(nargin<10 || isempty(PiT))
+            if(nargin<11 || isempty(PiT))
                 if(estimateTarget==1)
                     PiT = zeros(size(Q));
                 else
                     PiT = 0*diag(ones(ns,1))*1e-6;
                 end
             end
-            if(nargin<8 || isempty(Pi0))
+            if(nargin<9 || isempty(Pi0))
                 Pi0 = zeros(ns,ns);
             end
-            if(nargin<9 || isempty(yT))
+            if(nargin<10 || isempty(yT))
                 yT=[];
                 Amat = A;
                 Qmat = Q;
@@ -136,11 +136,11 @@
 
             end
 
-            if(nargin<7 || isempty(x0))
+            if(nargin<8 || isempty(x0))
                 x0=zeros(size(A,2),1);
             end
 
-            if(nargin<6)
+            if(nargin<7)
                 binwidth = .001; % in seconds
             end
 

ConfidenceInterval.m

@@ -1294,7 +1294,9 @@ function setMask(sObj, mask)
             %sObjOut is a copy of sObj with the newSampleRate specified;
             if(sObj.sampleRate ~=newSampleRate)
                 sObjOut = sObj.copySignal;
-                sObjOut.resampleMe(newSampleRate);
+                if(or(~isnan(sObjOut.sampleRate),size(sObjOut.data,1)>1))
+                    sObjOut.resampleMe(newSampleRate);
+                end
             else
                 sObjOut = sObj.copySignal;
             end

ConfigColl.m

@@ -6,61 +6,64 @@
    <!--
 This HTML was auto-generated from MATLAB code.
 To make changes, update the MATLAB code and republish this document.
-      --><title>Analysis Examples</title><meta name="generator" content="MATLAB 7.13"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2012-02-29"><meta name="DC.source" content="AnalysisExamples.m"><style type="text/css">
-
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-  background-color: white;
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-
-/* Make the text shrink to fit narrow windows, but not stretch too far in 
-wide windows. */ 
-p,h1,h2,div.content div {
-  max-width: 600px;
-  /* Hack for IE6 */
-  width: auto !important; width: 600px;
-}
-
-pre.codeinput {
-  background: #EEEEEE;
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-p.footer {
-  text-align: right;
-  font-size: xx-small;
-  font-weight: lighter;
-  font-style: italic;
-  color: gray;
-}
+      --><title>Analysis Examples</title><meta name="generator" content="MATLAB 8.2"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2013-11-20"><meta name="DC.source" content="AnalysisExamples.m"><style type="text/css">
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+img { padding:0px; margin:0px 0px 20px; border:none; }
+p img, pre img, tt img, li img { margin-bottom:0px; } 
+
+ul { padding:0px; margin:0px 0px 20px 23px; list-style:square; }
+ul li { padding:0px; margin:0px 0px 7px 0px; }
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+
+@media print { pre.codeinput, pre.codeoutput { word-wrap:break-word; width:100%; } }
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+span.untermstring { color:#B20000 }
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+
+.footer { width:auto; padding:10px 0px; margin:25px 0px 0px; border-top:1px dotted #878787; font-size:0.8em; line-height:140%; font-style:italic; color:#878787; text-align:left; float:none; }
+.footer p { margin:0px; }
+.footer a { color:#878787; }
+.footer a:hover { color:#878787; text-decoration:underline; }
+.footer a:visited { color:#878787; }
+
+table th { padding:7px 5px; text-align:left; vertical-align:middle; border: 1px solid #d6d4d4; font-weight:bold; }
+table td { padding:7px 5px; text-align:left; vertical-align:top; border:1px solid #d6d4d4; }
+
+
+
+
 
   </style></head><body><div class="content"><h1>Analysis Examples</h1><!--introduction--><p>This is an example on the standard approach to fitting GLM models to spike train data. This data set was obtained at the Society For Neuroscience '08 Workshop on <a href="http://www.sfn.org/index.aspx?pagename=ShortCourse3_2008">Workshop on Neural Signal Processing</a> Compare to analysis with <a href="matlab:web('AnalysisExamples2.html','-helpbrowser')">Neural Spike Analysis Toolbox</a></p><!--/introduction--><h2>Contents</h2><div><ul><li><a href="#1">Example 1: Tradition Preliminary Analysis</a></li></ul></div><h2>Example 1: Tradition Preliminary Analysis<a name="1"></a></h2><pre class="codeinput"><span class="comment">% Script glm_part1.m</span>
 <span class="comment">% MATLAB code to visualize data, fit a GLM model of the relation between</span>
@@ -138,8 +141,7 @@
 ylabel(<span class="string">'Empirical CDF of Rescaled ISIs'</span>);
 title(<span class="string">'KS Plot with 95% Confidence Intervals'</span>);
 legend(<span class="string">'Linear'</span>,<span class="string">'Quadratic'</span>);
-</pre><img vspace="5" hspace="5" src="AnalysisExamples_04.png" alt=""> <p class="footer"><br>
-      Published with MATLAB&reg; 7.13<br></p></div><!--
+</pre><img vspace="5" hspace="5" src="AnalysisExamples_04.png" alt=""> <p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2013b</a><br></p></div><!--
 ##### SOURCE BEGIN #####
 %% Analysis Examples 
 % This is an example on the standard approach to fitting GLM models to

CovColl.m

@@ -6,61 +6,64 @@
    <!--
 This HTML was auto-generated from MATLAB code.
 To make changes, update the MATLAB code and republish this document.
-      --><title>AnalysisExamples2</title><meta name="generator" content="MATLAB 7.13"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2012-03-02"><meta name="DC.source" content="AnalysisExamples2.m"><style type="text/css">
+      --><title>AnalysisExamples2</title><meta name="generator" content="MATLAB 8.2"><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/"><meta name="DC.date" content="2013-11-20"><meta name="DC.source" content="AnalysisExamples2.m"><style type="text/css">
+html,body,div,span,applet,object,iframe,h1,h2,h3,h4,h5,h6,p,blockquote,pre,a,abbr,acronym,address,big,cite,code,del,dfn,em,font,img,ins,kbd,q,s,samp,small,strike,strong,sub,sup,tt,var,b,u,i,center,dl,dt,dd,ol,ul,li,fieldset,form,label,legend,table,caption,tbody,tfoot,thead,tr,th,td{margin:0;padding:0;border:0;outline:0;font-size:100%;vertical-align:baseline;background:transparent}body{line-height:1}ol,ul{list-style:none}blockquote,q{quotes:none}blockquote:before,blockquote:after,q:before,q:after{content:'';content:none}:focus{outine:0}ins{text-decoration:none}del{text-decoration:line-through}table{border-collapse:collapse;border-spacing:0}
 
-body {
-  background-color: white;
-  margin:10px;
-}
+html { min-height:100%; margin-bottom:1px; }
+html body { height:100%; margin:0px; font-family:Arial, Helvetica, sans-serif; font-size:10px; color:#000; line-height:140%; background:#fff none; overflow-y:scroll; }
+html body td { vertical-align:top; text-align:left; }
 
-h1 {
-  color: #990000; 
-  font-size: x-large;
-}
+h1 { padding:0px; margin:0px 0px 25px; font-family:Arial, Helvetica, sans-serif; font-size:1.5em; color:#d55000; line-height:100%; font-weight:normal; }
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+h3 { padding:0px; margin:0px 0px 5px; font-family:Arial, Helvetica, sans-serif; font-size:1.1em; color:#000; font-weight:bold; line-height:140%; }
 
-h2 {
-  color: #990000;
-  font-size: medium;
-}
+a { color:#005fce; text-decoration:none; }
+a:hover { color:#005fce; text-decoration:underline; }
+a:visited { color:#004aa0; text-decoration:none; }
 
-/* Make the text shrink to fit narrow windows, but not stretch too far in 
-wide windows. */ 
-p,h1,h2,div.content div {
-  max-width: 600px;
-  /* Hack for IE6 */
-  width: auto !important; width: 600px;
-}
+p { padding:0px; margin:0px 0px 20px; }
+img { padding:0px; margin:0px 0px 20px; border:none; }
+p img, pre img, tt img, li img { margin-bottom:0px; } 
 
-pre.codeinput {
-  background: #EEEEEE;
-  padding: 10px;
-}
-@media print {
-  pre.codeinput {word-wrap:break-word; width:100%;}
-} 
+ul { padding:0px; margin:0px 0px 20px 23px; list-style:square; }
+ul li { padding:0px; margin:0px 0px 7px 0px; }
+ul li ul { padding:5px 0px 0px; margin:0px 0px 7px 23px; }
+ul li ol li { list-style:decimal; }
+ol { padding:0px; margin:0px 0px 20px 0px; list-style:decimal; }
+ol li { padding:0px; margin:0px 0px 7px 23px; list-style-type:decimal; }
+ol li ol { padding:5px 0px 0px; margin:0px 0px 7px 0px; }
+ol li ol li { list-style-type:lower-alpha; }
+ol li ul { padding-top:7px; }
+ol li ul li { list-style:square; }
+
+.content { font-size:1.2em; line-height:140%; padding: 20px; }
+
+pre, tt, code { font-size:12px; }
+pre { margin:0px 0px 20px; }
+pre.error { color:red; }
+pre.codeinput { padding:10px; border:1px solid #d3d3d3; background:#f7f7f7; }
+pre.codeoutput { padding:10px 11px; margin:0px 0px 20px; color:#4c4c4c; }
+
+@media print { pre.codeinput, pre.codeoutput { word-wrap:break-word; width:100%; } }
+
+span.keyword { color:#0000FF }
+span.comment { color:#228B22 }
+span.string { color:#A020F0 }
+span.untermstring { color:#B20000 }
+span.syscmd { color:#B28C00 }
+
+.footer { width:auto; padding:10px 0px; margin:25px 0px 0px; border-top:1px dotted #878787; font-size:0.8em; line-height:140%; font-style:italic; color:#878787; text-align:left; float:none; }
+.footer p { margin:0px; }
+.footer a { color:#878787; }
+.footer a:hover { color:#878787; text-decoration:underline; }
+.footer a:visited { color:#878787; }
+
+table th { padding:7px 5px; text-align:left; vertical-align:middle; border: 1px solid #d6d4d4; font-weight:bold; }
+table td { padding:7px 5px; text-align:left; vertical-align:top; border:1px solid #d6d4d4; }
 
-span.keyword {color: #0000FF}
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   </style></head><body><div class="content"><h2>Contents</h2><div><ul><li><a href="#1">Analysis Examples 2</a></li><li><a href="#8">Toolbox vs. Standard GLM comparison</a></li><li><a href="#9">Compute the history effect</a></li></ul></div><h2>Analysis Examples 2<a name="1"></a></h2><p>Compare with traditional Neural Spike Train Analysis <a href="matlab:web('AnalysisExamples.html','-helpbrowser')">here</a></p><pre class="codeinput"><span class="comment">% load the rat trajectory and spiking data;</span>
 close <span class="string">all</span>;
@@ -86,7 +89,7 @@
 covarColl = CovColl({baseline,radial});
 trial     = Trial(spikeColl,covarColl);
 clear <span class="string">tc</span>;
-sampleRate=30;
+sampleRate=1000;
 <span class="comment">% tcObj=TrialConfig(covMask,sampleRate, history,minTime,maxTime)</span>
 tc{1} = TrialConfig({{<span class="string">'Baseline'</span>,<span class="string">'mu'</span>},{<span class="string">'Radial'</span>,<span class="string">'x'</span>,<span class="string">'y'</span>}},sampleRate,[]); tc{1}.setName(<span class="string">'Linear'</span>);
 tc{2} = TrialConfig({{<span class="string">'Baseline'</span>,<span class="string">'mu'</span>},{<span class="string">'Radial'</span>,<span class="string">'x'</span>,<span class="string">'y'</span>,<span class="string">'x^2'</span>,<span class="string">'y^2'</span>,<span class="string">'x*y'</span>}},sampleRate,[]); tc{2}.setName(<span class="string">'Quadratic'</span>);
@@ -129,27 +132,32 @@
 </pre><pre class="codeoutput">
 ans =
 
-   -0.0011
-    0.0050
-    0.0034
-    0.0069
-    0.0077
-    0.0165
+    3.5041
+    0.0099
+    0.0102
+    0.0210
+    0.0215
+    0.0172
 
-</pre><h2>Compute the history effect<a name="9"></a></h2><pre class="codeinput">sampleRate=30;  makePlot=1; neuronNum = 1;
-covLabels = {{<span class="string">'Baseline'</span>,<span class="string">'mu'</span>}};
+</pre><h2>Compute the history effect<a name="9"></a></h2><pre class="codeinput">sampleRate=1000;  makePlot=1; neuronNum = 1;
+covLabels = {{<span class="string">'Baseline'</span>,<span class="string">'mu'</span>},{<span class="string">'Radial'</span>,<span class="string">'x'</span>,<span class="string">'y'</span>,<span class="string">'x^2'</span>,<span class="string">'y^2'</span>,<span class="string">'x*y'</span>}};
 Algorithm = <span class="string">'GLM'</span>;
 batchMode=0;
-windowTimes =(0:2:10)./sampleRate;
+windowTimes =(0:1:10)./sampleRate;
+<span class="comment">% [fitResults,tcc] = computeHistLag(tObj,neuronNum,windowTimes,CovLabels,Algorithm,batchMode,sampleRate,makePlot,histMinTimes,histMaxTimes)</span>
 [fitResults,tcc] = Analysis.computeHistLag(trial,neuronNum,windowTimes,covLabels,Algorithm,batchMode,sampleRate,makePlot);
 </pre><pre class="codeoutput">Analyzing Configuration #1: Neuron #1
 Analyzing Configuration #2: Neuron #1
 Analyzing Configuration #3: Neuron #1
 Analyzing Configuration #4: Neuron #1
 Analyzing Configuration #5: Neuron #1
 Analyzing Configuration #6: Neuron #1
-</pre><img vspace="5" hspace="5" src="AnalysisExamples2_04.png" alt=""> <p class="footer"><br>
-      Published with MATLAB&reg; 7.13<br></p></div><!--
+Analyzing Configuration #7: Neuron #1
+Analyzing Configuration #8: Neuron #1
+Analyzing Configuration #9: Neuron #1
+Analyzing Configuration #10: Neuron #1
+Analyzing Configuration #11: Neuron #1
+</pre><img vspace="5" hspace="5" src="AnalysisExamples2_04.png" alt=""> <p class="footer"><br><a href="http://www.mathworks.com/products/matlab/">Published with MATLAB&reg; R2013b</a><br></p></div><!--
 ##### SOURCE BEGIN #####
 %% Analysis Examples 2
 % Compare with traditional Neural Spike Train Analysis
@@ -196,7 +204,7 @@
 covarColl = CovColl({baseline,radial});
 trial     = Trial(spikeColl,covarColl);
 clear tc;
-sampleRate=30;
+sampleRate=1000;
 % tcObj=TrialConfig(covMask,sampleRate, history,minTime,maxTime)
 tc{1} = TrialConfig({{'Baseline','mu'},{'Radial','x','y'}},sampleRate,[]); tc{1}.setName('Linear');
 tc{2} = TrialConfig({{'Baseline','mu'},{'Radial','x','y','x^2','y^2','x*y'}},sampleRate,[]); tc{2}.setName('Quadratic');
@@ -247,11 +255,12 @@
 
 
 %% Compute the history effect
-sampleRate=30;  makePlot=1; neuronNum = 1;
-covLabels = {{'Baseline','mu'}};
+sampleRate=1000;  makePlot=1; neuronNum = 1;
+covLabels = {{'Baseline','mu'},{'Radial','x','y','x^2','y^2','x*y'}};
 Algorithm = 'GLM';
 batchMode=0;
-windowTimes =(0:2:10)./sampleRate;
+windowTimes =(0:1:10)./sampleRate;
+% [fitResults,tcc] = computeHistLag(tObj,neuronNum,windowTimes,CovLabels,Algorithm,batchMode,sampleRate,makePlot,histMinTimes,histMaxTimes)
 [fitResults,tcc] = Analysis.computeHistLag(trial,neuronNum,windowTimes,covLabels,Algorithm,batchMode,sampleRate,makePlot);
 
 ##### SOURCE END #####