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I have decided to play around with some simple concepts involving neural networks in Java, and in adapting somewhat useless code I found on a forum, I have been able to create a very simple model for the typical beginner's XOR simulation: public class MainApp { public static void main (String [] args) { Neuron xor = new Neuron(0.5f); Neuron left = new Neuron(1.5f); Neuron right = new Neuron(0.5f); left.setWeight(-1.0f); right.setWeight(1.0f); xor.connect(left, right); for (String val : args) { Neuron op = new Neuron(0.0f); op.setWeight(Boolean.parseBoolean(val)); left.connect(op); right.connect(op); } xor.fire(); System.out.println("Result: " + xor.isFired()); } } public class Neuron { private ArrayList inputs; private float weight; private float threshhold; private boolean fired; public Neuron (float t) { threshhold = t; fired = false; inputs = new ArrayList(); } public void connect (Neuron ... ns) { for (Neuron n : ns) inputs.add(n); } public void setWeight (float newWeight) { weight = newWeight; } public void setWeight (boolean newWeight) { weight = newWeight ? 1.0f : 0.0f; } public float getWeight () { return weight; } public float fire () { if (inputs.size() > 0) { float totalWeight = 0.0f; for (Neuron n : inputs) { n.fire(); totalWeight += (n.isFired()) ? n.getWeight() : 0.0f; } fired = totalWeight > threshhold; return totalWeight; } else if (weight != 0.0f) { fired = weight > threshhold; return weight; } else { return 0.0f; } } public boolean isFired () { return fire

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