The concept of receptive field can be traced back to early research on biological visual systems. Researchers borrowed this concept when they began to build artificial neural networks (especially convolutional neural networks CNN) that simulate biological vision.

When a receptor is stimulated and excited, nerve impulses (various sensory information) are transmitted to the higher central nervous system through afferent neurons in the sensory organ. The stimulation area that a neuron responds to (controls) is called the neuron's receptive field. In convolutional neural networks, the receptive field is defined as the size of the region on the original image mapped by the pixels on the feature map output by each layer of the convolutional neural network.