Neuroscience: Pattern Recognition with Neural Networks

Neuroscience: Pattern Recognition with Neural Networks

Retinal ganglion cells, that is, the output cells of the retina, have concentric on-center or off-center receptive fields. The on-center receptive field consists of an excitatory circular region surrounded by an inhibitory annular region, while the off-center receptive field has the opposite structure. These cells are supposed to extract contrast components from patterns projected onto the retina. Photoreceptors and ganglion cells are not directly connected in an actual biological retina. There are several kinds of cells in between and the information is processed by the interactions of such cells. Engineers in pattern recognition often classify the process of self-organization into supervised learning (or learning-with-a-teacher) and unsupervised learning (or learning-without-a-teacher). A famous example of a classical network that can be trained by supervised learning is the three-layered perceptron. Although the perceptron has a three-layered hierarchical structure, it is only in the highest stage that the cells have variable input connections.