Any neural network, from simple perceptrons to enormous corporate AI-systems, consists of nodes that imitate the neurons in the human brain. These cells are tightly interconnected. So are the nodes.

A huge neural network like that demands a lot of resources but even then remains prone to overfitting because the large number of parameters enable it to just memorize the dataset.

CNNs use parameter sharing. All neurons in a particular feature map share weights which makes the whole system less computationally intense.