In biological networks, the information consists of action potentials (neuron membrane rapid depolarizations) propagating through the network. In artificial ones, the information consists of tensors (multidimensional arrays) of weights and biases: each unit passes a weighted sum of an input tensor with an additional—possibly weighted—bias through an activation function before passing on the output tensor to the next layer of units.
Artificial neural networks are a series of layered units mimicking the concept of biological neurons: inputs are received by every unit of a layer, computed, then transmitted to units of the next layer. In the process of learning, experience strengthens some connections between units and weakens others.
While biological neurons are connected in extremely intricate patterns, artificial ones follow a layered structure. Another difference in complexity is in the number of units: the human brain has 65–90 billion neurons. ANN have much fewer units.