diff --git a/java/source/infodynamics/measures/spiking/integration/TransferEntropyCalculatorSpikingIntegration.java b/java/source/infodynamics/measures/spiking/integration/TransferEntropyCalculatorSpikingIntegration.java
index 89094d8..ede39a4 100644
--- a/java/source/infodynamics/measures/spiking/integration/TransferEntropyCalculatorSpikingIntegration.java
+++ b/java/source/infodynamics/measures/spiking/integration/TransferEntropyCalculatorSpikingIntegration.java
@@ -1,10 +1,6 @@
 package infodynamics.measures.spiking.integration;
 
 import java.util.Arrays;
-import java.util.Iterator;
-import java.util.PriorityQueue;
-import java.util.Vector;
-
 import java.util.Collections;
 import java.util.Iterator;
 import java.util.PriorityQueue;
@@ -22,7 +18,6 @@ import infodynamics.utils.MatrixUtils;
 import infodynamics.utils.NeighbourNodeData;
 import infodynamics.utils.FirstIndexComparatorDouble;
 import infodynamics.utils.UnivariateNearestNeighbourSearcher;
-
 import infodynamics.utils.EuclideanUtils;
 import infodynamics.utils.ParsedProperties;
 
@@ -78,11 +73,9 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
 	 */
 	protected int Knns = 4;
 
-
 	/**
 	 * Storage for source observations supplied via
 	 * {@link #addObservations(double[], double[])} etc.
-
 	 */
 	protected Vector<double[]> vectorOfSourceSpikeTimes = null;
 
@@ -163,6 +156,7 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
 	public TransferEntropyCalculatorSpikingIntegration() {
 		super();
 	}
+	
 
 	/*
 	 * (non-Javadoc)
@@ -296,6 +290,7 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
 		}
 	}
 
+
 	public void appendConditionalIntervals(int[] intervals) throws Exception{
 		for (int interval : intervals) {
 			if (interval < 1) {
@@ -445,10 +440,10 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
 		// Initialise the starting points of all the tracking variables
 		int embeddingPointIndex = indexOfFirstPointToUse;
 		int mostRecentDestIndex = destPastIntervals[destPastIntervals.length - 1];
-		int mostRecentSourceIndex = sourcePastIntervals[sourcePastIntervals.length - 1];
+		int mostRecentSourceIndex = sourcePastIntervals[sourcePastIntervals.length - 1] - 1;
 		int[] mostRecentConditioningIndices = new int[vectorOfCondPastIntervals.size()];
 		for (int i = 0; i < vectorOfCondPastIntervals.size(); i++) {
-			mostRecentConditioningIndices[i] = vectorOfCondPastIntervals.elementAt(i).length;
+			mostRecentConditioningIndices[i] = vectorOfCondPastIntervals.elementAt(i)[vectorOfCondPastIntervals.elementAt(i).length - 1] - 1;
 		}
 
 
@@ -542,11 +537,14 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
 			// Add Gaussian noise, if necessary
 			if (addNoise) {
 				for (int i = 0; i < conditioningPast.length; i++) {
-					//conditioningPast[i] = Math.exp(-conditioningPast[i]);
+					conditioningPast[i] = Math.log(conditioningPast[i] + 1.1);
 					conditioningPast[i] += random.nextGaussian() * noiseLevel;
 				}
 				for (int i = 0; i < jointPast.length; i++) {
-					//jointPast[i] = Math.exp(-jointPast[i]);
+					if (jointPast[i] < 0) {
+						System.out.println("NEGATIVE");
+					}
+					jointPast[i] = Math.log(jointPast[i] + 1.1);
 					jointPast[i] += random.nextGaussian() * noiseLevel;
 				}
 			}
@@ -568,7 +566,7 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
 		int firstTargetIndexOfEmbedding = destPastIntervals[destPastIntervals.length - 1];
 
 		int furthestInterval = sourcePastIntervals[sourcePastIntervals.length - 1];
-		while (destSpikeTimes[firstTargetIndexOfEmbedding] <= sourceSpikeTimes[furthestInterval - 1]) {
+		while (destSpikeTimes[firstTargetIndexOfEmbedding] < sourceSpikeTimes[furthestInterval - 1]) {
 			firstTargetIndexOfEmbedding++;
 		}
 		if (conditionalSpikeTimes.length != vectorOfCondPastIntervals.size()) {
@@ -597,11 +595,27 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
 		int num_samples = (int) Math.round(actualNumSamplesMultiplier * (destSpikeTimes.length - firstTargetIndexOfEmbedding + 1));
 		double[] randomSampleTimes = new double[num_samples];
 		Random rand = new Random();
-		for (int i = 0; i < randomSampleTimes.length; i++) {
-			randomSampleTimes[i] = sampleLowerBound + rand.nextDouble() * (sampleUpperBound - sampleLowerBound);
+		boolean doCellCulture = true;
+		if (doCellCulture) {
+			for (int i = 0; i < randomSampleTimes.length; i++) {
+				randomSampleTimes[i] = destSpikeTimes[firstTargetIndexOfEmbedding + (i % (destSpikeTimes.length - firstTargetIndexOfEmbedding - 1))]
+					+ 200 * (rand.nextDouble() - 0.5);
+				//randomSampleTimes[i] = -1.0;
+				if ((randomSampleTimes[i] > sampleUpperBound) || (randomSampleTimes[i] < sampleLowerBound)) {
+					randomSampleTimes[i] = sampleLowerBound + rand.nextDouble() * (sampleUpperBound - sampleLowerBound);
+				}
+			}
+		} else {
+			for (int i = 0; i < randomSampleTimes.length; i++) {
+				randomSampleTimes[i] = sampleLowerBound + rand.nextDouble() * (sampleUpperBound - sampleLowerBound);
+			}
 		}
 		Arrays.sort(randomSampleTimes);
-
+		/*System.out.println(sampleLowerBound + " " + sampleUpperBound);
+		for (int i = 0; i < randomSampleTimes.length; i += 100) {
+			System.out.print(randomSampleTimes[i] + " ");			
+		}
+		System.out.println("\n\n\n");*/
 		return randomSampleTimes;
 	}
 	
@@ -774,6 +788,7 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
 			 * squared euclidean distance is not a distance metric. We get around this by just taking the square root here, but it might be better to 
 			 * fix this in the KdTree class. 
 			 */
+			double tempRadiusJointSamples = radiusJointSamples;
 			if (normType == EuclideanUtils.NORM_EUCLIDEAN) {
 				radiusJointSpikes = Math.sqrt(radiusJointSpikes);
 				radiusJointSamples = Math.sqrt(radiusJointSamples);
@@ -786,7 +801,10 @@ public class TransferEntropyCalculatorSpikingIntegration implements TransferEntr
 				       MathsUtils.digamma(kConditioningSpikes) + MathsUtils.digamma(kConditioningSamples) + 
 				       + ((numDestPastIntervals + numCondPastIntervals) * (Math.log(radiusConditioningSpikes) - Math.log(radiusConditioningSamples))));
 			if (Double.isNaN(currentSum)) {
-				throw new Exception("NaNs in TE clac");
+				for (double[] embed : jointEmbeddingsFromSamples) {
+					System.out.println(Arrays.toString(embed));
+				}
+				throw new Exception("NaNs in TE clac " + radiusJointSpikes + " " + radiusJointSamples + " " + tempRadiusJointSamples);
 			}
 		}
 		// Normalise by time