The field of artificial neural networks is extremely complicated and readily evolving. In order to understand neural networks and how they process information, it is critical to examine how these networks function and the basic models that are used in such a process.

What are artificial neural networks?

Artificial neural networks are parallel computational models (unlike our computers, which have a single processor to collect and display information). These networks are commonly made up of multiple simple processors which are able to act in parallel alongside one another to model changing systems. This parallel computing process also enables faster processing and computation of solutions. Neural networks follow a dynamic computational structure, and do not abide by a simple process to derive a desired output.

The basis for these networks originated from the biological neuron and neural structures – every neuron takes in multiple unique inputs and produces one output. Similarly, in neural networks, different inputs are processed and modified by a weight, or a sort of equation that changes the original value. The network then combines these different weighted inputs with reference to a certain threshold and activation function and gives out the final value.

How do neural networks operate?

Artificial neural networks are organized into layers of parallel computing processes. For every processor in a layer, each of the number of inputs is multiplied by an originally established weight, resulting in what is called the internal value of the operation. This value is further changed by an originally created threshold value and sent to an activation function to map its output. The output of that function is then sent as the input for another layer, or as the final response of a network should the layer be the last. The weights and the threshold values are most commonly modified to produce the correct and most accurate value.

The learning mechanisms of a neural network

Looking at an analogy may be useful in understanding the mechanisms of a neural network. Learning in a neural network is closely related to how we learn in our regular lives and activities – we perform an action and are either accepted…

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