in the next layer will receive connections from some of the input neurons (pixels), but not all, as would be the case in a MLP and in other traditional neural networks. Hence, instead of having each neuron receive connections from all neurons in the previous layer, CNNs use a receptive field-like layout in which each neuron receives connections only from a subset of neurons in the previous (lower) layer. The receptive field of a neuron in one of the lower layers encompasses only a small area of the image, while the receptive field of a neuron in subsequent (higher) layers involves a combination of receptive fields from several (but not all) neurons in the layer before (i. e. a neuron in a higher layer "looks" at a larger portion of the image than does a neuron in a lower layer). In this way, each successive layer is capable of learning increasingly abstract features of the original image. The use of receptive fields in this fashion is thought to give CNNs an advantage in recognizing visual patterns when compared to other types of neural networks. See also Visual system Reflexogenic zone Spatiotemporal receptive field Spectro-temporal receptive field Computer vision Edge detection Convolutional neural network References Kandel E.R., Schwartz, J.H., Jessell, T.M. (2000). Principles of Neural Science, 4th ed., pp. 515–520. McGraw-Hill, New York. External links Receptive Fields Tutorial Sensory systems