computational function of visual receptive fields is given in. It is described how idealised models of receptive fields similar to the biological receptive fields found in the retina, the LGN and the primary visual cortex can be derived from structural properties of the environment in combination with internal consistency to guarantee consistent representation of image structures over multiple spatial and temporal scales. It is also described how the receptive fields in the primary visual cortex, which are tuned to different sizes, orientations and directions in the image domain, enable the visual system to handle the influence of natural image transformations and to compute invariant image representations at higher levels in the visual hierarchy. In the context of neural networks The term receptive field is also used in the context of artificial neural networks, most often in relation to convolutional neural networks (CNNs). So, in a neural network context, the receptive field is defined as the size of the region in the input that produces the feature. Basically, it is a measure of association of an output feature (of any layer) to the input region (patch). It is important to note that the idea of receptive fields applies to local operations (i.e. convolution, pooling). As an example, in motion-based tasks, like video prediction and optical flow estimation, large motions need to be captured (displacements of pixels in a 2D grid), so an adequate receptive field is required. Specifically, the receptive field should be sufficient if it is larger than the largest flow magnitude of the dataset. There are a lot of ways that one can increase the receptive field on a CNN. When used in this sense, the term adopts a meaning reminiscent of receptive fields in actual biological nervous systems. CNNs have a distinct architecture, designed to mimic the way in which real animal brains are understood to function; instead of having every neuron in each layer connect to all neurons in the next layer (Multilayer perceptron), the neurons are arranged in a 3-dimensional structure in such a way as to take into account the spatial relationships between different neurons with respect to the original data. Since CNNs are used primarily in the field of computer vision, the data that the neurons represent is typically an image; each input neuron represents one pixel from the original image. The first layer of neurons is composed of all the input neurons; neurons