diff --git a/src/main/java/BlankLayer.java b/src/main/java/BlankLayer.java
new file mode 100644
index 0000000..2dcfd00
--- /dev/null
+++ b/src/main/java/BlankLayer.java
@@ -0,0 +1,88 @@
+import org.ejml.simple.SimpleMatrix;
+
+import java.util.Random;
+
+/**
+ * Layer initialized with 1 neuron.
+ * Assumes that each new neuron is fully connected to every previous neuron (this will be changed in the future).
+ */
+public class BlankLayer extends Layer {
+    SimpleMatrix weights;
+    SimpleMatrix biases;
+
+    public BlankLayer(int inputSize) {
+        Random random = new Random();
+        this.weights = new SimpleMatrix(inputSize, 1, true,
+                random.doubles(inputSize, -1, 1).toArray());
+        this.biases = new SimpleMatrix(1, 1, true,
+                random.doubles(1, -1, 1).toArray());
+    }
+
+    /**
+     * Updates input size when previous layer has newly added neurons.
+     * @param n amount of new neurons in previous layer
+     */
+    public void updateInputSize(int n) {
+        Random random = new Random();
+
+        // add new weights
+        SimpleMatrix newWeights = new SimpleMatrix(this.weights.numRows() + n, this.weights.numCols());
+        for (int i = 0; i < this.weights.numRows(); i++) {
+            for (int j = 0; j < this.weights.numCols(); j++) {
+                newWeights.set(i, j, this.weights.get(i, j));
+            }
+        }
+        for (int i = 0; i < newWeights.getNumElements(); i++) {
+            if (newWeights.get(i) == 0) {
+                newWeights.set(i, random.nextDouble(-1, 1));
+            }
+        }
+        this.weights = newWeights;
+    }
+
+    /**
+     * Adds new neurons at the end of the layer
+     * @param n amount how many new neurons should be added
+     */
+    public void addNeuron(int n) {
+        Random random = new Random();
+
+        // add new weights
+        SimpleMatrix newWeights = new SimpleMatrix(this.weights.numRows(), this.weights.numCols() + n);
+        for (int i = 0; i < this.weights.numRows(); i++) {
+            for (int j = 0; j < this.weights.numCols(); j++) {
+                newWeights.set(i, j, this.weights.get(i, j));
+            }
+        }
+        for (int i = 0; i < newWeights.getNumElements(); i++) {
+            if (newWeights.get(i) == 0) {
+                newWeights.set(i, random.nextDouble(-1, 1));
+            }
+        }
+        this.weights = newWeights;
+
+        // add new biases
+        SimpleMatrix newBiases = new SimpleMatrix(1, this.biases.numCols() + n);
+        double[] newBiasValues = random.doubles(n, -1, 1).toArray();
+        System.arraycopy(this.biases.getDDRM().data, 0, newBiases.getDDRM().data, 0, this.biases.numCols());
+        System.arraycopy(newBiasValues, 0, newBiases.getDDRM().data, this.biases.numCols(), n);
+        this.biases = newBiases;
+    }
+
+    @Override
+    public SimpleMatrix forwardPropagation(SimpleMatrix inputs) {
+        this.input = inputs;
+        this.output = this.input.mult(this.weights).plus(this.biases);
+        return this.output;
+    }
+
+    @Override
+    public SimpleMatrix backwardPropagation(SimpleMatrix outputError, double learningRate) {
+        SimpleMatrix inputError = outputError.mult(this.weights.transpose());
+        SimpleMatrix weightsError = this.input.transpose().mult(outputError);
+
+        this.weights = this.weights.plus(learningRate, weightsError);
+        this.biases = this.biases.plus(learningRate, outputError);
+        return inputError;
+    }
+}
diff --git a/src/main/java/ExampleXOR.java b/src/main/java/ExampleXOR.java
index c08dde3..8cb612d 100644
--- a/src/main/java/ExampleXOR.java
+++ b/src/main/java/ExampleXOR.java
@@ -12,10 +12,10 @@ public class ExampleXOR {
                 new SimpleMatrix(new double[][]{{0}})};
 
         Network network = new Network();
-        network.add(new FCLayer(2, 3));
-        network.add(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
-        network.add(new FCLayer(3, 1));
-        network.add(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
+        network.addLayer(new FCLayer(2, 3));
+        network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
+        network.addLayer(new FCLayer(3, 1));
+        network.addLayer(new ActivationLayer(ActivationFunctions::tanh, ActivationFunctions::tanhPrime));
 
         network.use(LossFunctions::MSE, LossFunctions::MSEPrime);
         network.fit(X_train, y_train, 1000, 0.1d);
diff --git a/src/main/java/Network.java b/src/main/java/Network.java
index e330de2..565a1c0 100644
--- a/src/main/java/Network.java
+++ b/src/main/java/Network.java
@@ -4,19 +4,34 @@ import java.util.ArrayList;
 import java.util.function.BiFunction;
 
 public class Network {
-
-    ArrayList<Layer> layers;
-    BiFunction<SimpleMatrix, SimpleMatrix, Double> loss;
-    BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime;
+    private ArrayList<Layer> layers;
+    private BiFunction<SimpleMatrix, SimpleMatrix, Double> loss;
+    private BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime;
 
     public Network() {
         layers = new ArrayList<>();
     }
 
-    public void add(Layer layer) {
+    public void addLayer(Layer layer) {
         layers.add(layer);
     }
 
+    /**
+     * Adds n neurons to a specific layer and also updates this and the next layer's weights and biases.
+     * Only works if there are two successive BlankLayers.
+     * @param layer index of layer in the ArrayList layers
+     * @param n amount how many new neurons should be added
+     */
+    public void addNeuron(int layer, int n) {
+        if (!(this.layers.get(layer) instanceof BlankLayer)) {
+            System.out.println("This layer is not a BlankLayer");
+        } else if (!(this.layers.get(layer + 2) instanceof BlankLayer)) {
+            System.out.println("The next layer is not a BlankLayer");
+        }
+        ((BlankLayer) this.layers.get(layer)).addNeuron(n);
+        ((BlankLayer) this.layers.get(layer + 2)).updateInputSize(n);
+    }
+
     public void use(BiFunction<SimpleMatrix, SimpleMatrix, Double> loss, BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime) {
         this.loss = loss;
         this.lossPrime = lossPrime;
@@ -65,4 +80,28 @@ public class Network {
             System.out.println("epoch " + (i+1) + "/" + epochs + "  error=" + err);
         }
     }
+
+    public ArrayList<Layer> getLayers() {
+        return layers;
+    }
+
+    public void setLayers(ArrayList<Layer> layers) {
+        this.layers = layers;
+    }
+
+    public BiFunction<SimpleMatrix, SimpleMatrix, Double> getLoss() {
+        return loss;
+    }
+
+    public void setLoss(BiFunction<SimpleMatrix, SimpleMatrix, Double> loss) {
+        this.loss = loss;
+    }
+
+    public BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> getLossPrime() {
+        return lossPrime;
+    }
+
+    public void setLossPrime(BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime) {
+        this.lossPrime = lossPrime;
+    }
 }
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