diff --git a/demos/octave/CellularAutomata/TeBook2013.m b/demos/octave/CellularAutomata/TeBook2013.m
new file mode 100755
index 0000000..70c5dc1
--- /dev/null
+++ b/demos/octave/CellularAutomata/TeBook2013.m
@@ -0,0 +1,60 @@
+% To recreate the plots in:
+% chapter 5 of T. Bossomaier, L. Barnett, M. Harré and J.T. Lizier, "An Introduction to Transfer Entropy: Information Flow in Complex Systems", Springer, to be published, 2013.
+% These plots were first published in (see DirectedMeasuresChapterDemo2013.m):
+% J. T. Lizier, "Measuring the dynamics of information processing on a local scale in time and space",
+%  accepted for "Directed Information Measures in Neuroscience", edited by M. Wibral, R. Vicente, J. T. Lizier, to be published by Springer, 2013
+
+clear all;
+
+% Set up simulation options:
+cells = 10000;
+timeSteps = 600;
+neighbourhood = 3;
+caStates = 2;
+
+% Set up options for information dynamics analysis, and which segment of the CA to plot
+measureParams.k=16;
+options.saveImages = true;
+options.saveImagesFormat = 'pdf';
+options.plotOptions.scaleColoursToSubsetOfPlot = true;
+scaleColoursToExtremesDefault = false;
+options.plotOptions.scaleColoursToExtremes = scaleColoursToExtremesDefault;
+% Turn up the contrast so that the small values aren't disproportionately visible: (0.15, 0.30 was good, except for separable which was better with 0.15, 0.35)
+options.plotOptions.scalingMainComponent = 0.15;
+options.plotOptions.scalingScdryComponent = 0.30;
+options.plotOptions.gammaPower = 0.5;
+
+%%%%%%%%%
+% Examining rule 54:
+options.plotOptions.plotRows = 35;
+options.plotOptions.plotCols = 35;
+options.plotOptions.plotStartRow = 20+20;
+options.plotOptions.plotStartCol = 1+10;
+options.seed = 3; % Set up the random number generator to give reproducible initial states for all measurements
+if (exist('initialStates/DirectedMeasuresChapterDemo2013-rule54.txt', 'file'))
+	% A file specifying the initial state exists -- this
+	%  ensures that Matlab and Octave use the same initial state
+	%  (otherwise only Octave recreates the same initial state used in our chapter).
+	%  (You can delete/move the initial state file if you want them generated from scratch.)
+	options.initialState = load('initialStates/DirectedMeasuresChapterDemo2013-rule54.txt');
+end
+fprintf('\nStarting rule 54 ...\n');
+fprintf('\nPlotting apparent transfer entropy j = 1 ...\n');
+% Use the full red scale for transfer and separable info, since we need to see the extreme negative values properly
+options.plotOptions.scaleColoursToExtremes = true;
+options.plotOptions.scalingScdryComponent = 0.35;
+options.plotOptions.scalingMainComponent = 0.35; 
+measureParams.j = 1;
+plotLocalInfoMeasureForCA(neighbourhood, caStates, 54, cells, timeSteps, 'transfer', measureParams, options);
+fprintf('\nPress any key when ready for apparent transfer entropy j = -1 ...\n');
+pause
+options.plotRawCa = false;
+measureParams.j = -1;
+plotLocalInfoMeasureForCA(neighbourhood, caStates, 54, cells, timeSteps, 'transfer', measureParams, options);
+
+% If we were going to apply this to another rule:
+% fprintf('\nPress any key when ready to apply to the next rule\n')
+% pause
+options.plotOptions.scaleColoursToExtremes = scaleColoursToExtremesDefault; % return to default value 
+options.plotOptions.scalingScdryComponent = 0.30; % return to previous value
+
diff --git a/demos/octave/CellularAutomata/plotLocalInfoMeasureForCA.m b/demos/octave/CellularAutomata/plotLocalInfoMeasureForCA.m
index 792db93..9d5b2d0 100755
--- a/demos/octave/CellularAutomata/plotLocalInfoMeasureForCA.m
+++ b/demos/octave/CellularAutomata/plotLocalInfoMeasureForCA.m
@@ -119,7 +119,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
 	%============================
 	% Active information storage
 	if ((ischar(measureId) && (strcmpi('active', measureId) || strcmpi('all', measureId))) || ...
-	    ((measureId == 0) || (measureId == -1)))
+	    (not(ischar(measureId)) && ((measureId == 0) || (measureId == -1))))
 		% Compute active information storage
 		activeCalc = javaObject('infodynamics.measures.discrete.ActiveInformationCalculator', base, measureParams.k);
 		activeCalc.initialise();
@@ -149,7 +149,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
 	%============================
 	% Apparent transfer entropy
 	if ((ischar(measureId) && (strcmpi('transfer', measureId) || strcmpi('all', measureId) || strcmpi('apparenttransfer', measureId))) || ...
-	    ((measureId == 1) || (measureId == -1)))
+	    (not(ischar(measureId)) && ((measureId == 1) || (measureId == -1))))
 		% Compute apparent transfer entropy
 		if (measureParams.j == 0)
 			error('Cannot compute transfer entropy from a cell to itself (setting measureParams.j == 0)');
@@ -182,7 +182,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
 	%============================
 	% Complete transfer entropy
 	if ((ischar(measureId) && (strcmpi('transfercomplete', measureId) || strcmpi('completetransfer', measureId) || strcmpi('all', measureId))) || ...
-	    ((measureId == 2) || (measureId == -1)))
+	    (not(ischar(measureId)) && ((measureId == 2) || (measureId == -1))))
 		% Compute complete transfer entropy
 		if (measureParams.j == 0)
 			error('Cannot compute transfer entropy from a cell to itself (setting measureParams.j == 0)');
@@ -218,7 +218,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
 	%============================
 	% Separable information
 	if ((ischar(measureId) && (strcmpi('separable', measureId) || strcmpi('all', measureId))) || ...
-	    ((measureId == 3) || (measureId == -1)))
+	    (not(ischar(measureId)) && ((measureId == 3) || (measureId == -1))))
 		% Compute separable information
 		separableCalc = javaObject('infodynamics.measures.discrete.SeparableInfoCalculator', ...
 			base, measureParams.k, neighbourhood - 1);
@@ -251,7 +251,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
 	%============================
 	% Entropy
 	if ((ischar(measureId) && (strcmpi('entropy', measureId) || strcmpi('all', measureId))) || ...
-	    ((measureId == 4) || (measureId == -1)))
+	    (not(ischar(measureId)) && ((measureId == 4) || (measureId == -1))))
 		% Compute entropy
 		entropyCalc = javaObject('infodynamics.measures.discrete.EntropyCalculator', ...
 			base);
@@ -283,7 +283,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
 	%============================
 	% Entropy rate
 	if ((ischar(measureId) && (strcmpi('entropyrate', measureId) || strcmpi('all', measureId))) || ...
-	    ((measureId == 5) || (measureId == -1)))
+	    (not(ischar(measureId)) && ((measureId == 5) || (measureId == -1))))
 		% Compute entropy rate
 		entRateCalc = javaObject('infodynamics.measures.discrete.EntropyRateCalculator', base, measureParams.k);
 		entRateCalc.initialise();
@@ -313,7 +313,7 @@ function [caStates, localValues] = plotLocalInfoMeasureForCA(neighbourhood, base
 	%============================
 	% Excess entropy
 	if ((ischar(measureId) && (strcmpi('excess', measureId) || strcmpi('all', measureId))) || ...
-	    ((measureId == 6) || (measureId == -1)))
+	    (not(ischar(measureId)) && ((measureId == 6) || (measureId == -1))))
 		% Compute excess entropy
 		excessEntropyCalc = javaObject('infodynamics.measures.discrete.PredictiveInformationCalculator', base, measureParams.k);
 		excessEntropyCalc.initialise();
diff --git a/demos/octave/CellularAutomata/runCA.m b/demos/octave/CellularAutomata/runCA.m
index 8695b93..d7a6c6d 100755
--- a/demos/octave/CellularAutomata/runCA.m
+++ b/demos/octave/CellularAutomata/runCA.m
@@ -49,7 +49,7 @@ function [caStates, ruleTable, executedRules] = runCA(neighbourhood, base, rule,
 		else
 			% User has supplied the initial state for the CA:
 			fprintf('User has supplied initial state for CA\n');
-			if (length(seedOrState) != cells)
+			if (length(seedOrState) ~= cells)
 				error('Supplied initial ca state vector [seedOrState] is not of length [cells]');
 			end
 			ca = seedOrState;