 <p><strong>This is my first answer in stackOverflow, so please, go easy.</strong></p> <p>If i'm understanding your question right, you're wondering how the math behind an artificial neuron works. The neuron is made up of 5 components shown in the following list. (The subscript i indicates the i-th input or weight.)</p> <ol> <li>A set of inputs, xi.</li> <li>A set of weights, wi.</li> <li>A threshold, u.</li> <li>An activation function, f.</li> <li>A single neuron output, y.</li> </ol> <p>The artificial neuron has a fairly simple structure.</p> <p>Using the unit step activation function, you can determine a set of weights (and threshold value) that will produce the following classification: <a href="https://i.stack.imgur.com/J2apd.png" rel="nofollow noreferrer">Click to view classification</a></p> <p>Looking at number 4. An activation function f. Many different functions can take place with the identity function being the simplest. </p> <p>The neuron output Y, is the result of applying the <strong>activation function</strong> to the <strong>weighted sum</strong> of the <strong>inputs</strong>, less the <strong>threshold</strong>. </p> <p>This value can be discrete or real depending on the activation function used.</p> <p><a href="https://i.stack.imgur.com/4hHb5.png" rel="nofollow noreferrer">Here's</a> an output of Y that holds a specific function F.</p> <p>Once the output has been calculated, it can be passed to another neuron (or group of neurons) or sampled by the external environment. The interpretation of the neuron output depends upon the problem under consideration</p> <p>@Seephore</p> <p>In principle, there is no limit on the number of hidden layers that can be used in an artificial neural network. Such networks can be trained using "stacking" or other techniques from the deep learning literature. Yes, you could have 1000 layers, though I don't know if you'd get much benefit: in deep learning I've more typically seen somewhere between 1-20 hidden layers, rather than 1000 hidden layers. In practice the number of layers is based upon pragmatic concerns, e.g., what will lead to good accuracy with reasonable training time and without overfitting.</p> <p><strong>What you're asking:</strong> Im going to assume you meant to say 100 input and 1000 output? When a input takes in the weighted value, its output gives it to all the other nodes (neurons) in the next layer, but the value is still from the given node. </p> <p>There are many "wish wash" books out their for java, but if you really want to get into it read <a href="https://i.stack.imgur.com/J2apd.png" rel="nofollow noreferrer">This</a></p> <p><a href="https://i.stack.imgur.com/OH3gI.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/OH3gI.png" alt=""></a></p>