The architectural concept of receptive fields and convolutional kernels in deep learning draws heavy inspiration from neurophysiology, particularly studies on the mammalian visual cortex. Foundational biological research discovered that lower-level neurons naturally respond to simple visual stimuli like edges and specific shapes, a mechanism that is remarkably mirrored by how convolutional neural networks learn spatial representations. This biological link was further illustrated when convolutional kernels were shown to mimic these cortical responses on natural images, a phenomenon that persists even in the features extracted by the deeper layers of modern image classification networks.

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For any given element $$x$$ within a layer of a convolutional neural network, its receptive field consists of all the elements from all preceding layers that can potentially influence the calculation of $$x$$ during forward propagation. Because a single output element aggregates information from a window of inputs, its receptive field may encompass an area significantly larger than its own dimensions.

Receptive Field in Convolutional Neural Networks

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Neurophysiological Inspiration of CNN Receptive Fields

When an element in a feature map requires a larger receptive field to detect input features over a broader area, a deeper network architecture can be utilized. For instance, applying an initial $$2 	imes 2$$ convolution kernel produces an output matrix $$\mathbf{Y}$$ where each element has a receptive field of four input elements. If an additional $$2 	imes 2$$ convolutional layer takes $$\mathbf{Y}$$ as its input to compute a single element $$z$$, the receptive field of $$z$$ on the intermediate layer $$\mathbf{Y}$$ consists of four elements, but its receptive field on the original input expands to encompass nine elements.

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