Artificial neurons

The core of a neural network is the artificial neuron. The artificial neuron is the smallest unit in a neural network that we can train to recognize patterns in data. Each artificial neuron inside the neural network has one or more input. Each of the vector input gets a weight:

The artificial neuron inside a neural network works in much the same way, but doesn't use chemical signals. Each artificial neuron inside the neural network has one or more inputs. Each of the vector inputs gets a weight.

The numbers provided for each input of the neuron gets multiplied by this weight. The output of this multiplication is then added up to produce a total activation value for the neuron.

This activation signal is then passed through an activation function. The activation function performs a non-linear transformation on this signal. For example: it uses a rectified linear function to process the input signal:

The rectified linear function will convert negative activation signals to zero but performs an identity (pass-through) transformation on the signal when it is a positive number.

One other popular activation function is the sigmoid function. It behaves slightly different than the rectified linear function in that it transforms negative values to 0 and positive values to 1. There is, however, a slope in the activation between -0.5 and +0.5, where the signal is transformed in a linear fashion.

Activation functions in artificial neurons play an important role in the neural network. It's because of these non-linear transformation functions that the neural network is capable of working with non-linear relationships in the data.