Describe an artificial neural network and then discuss how such a network learns by applying Hebb’s rule.
Artificial neural networks in the psychology are defined as the system made up of the neurons, which are interconnected to process the message exchange among the neighbor neurons. Artificial neural networks are considered as the family of models made on the basics of the biological network of the neurons. They are used for the estimation of the function, which depends directly on the unknown inputs.
These connections are assigned in the terms of the numeric values, which can be calculated on the basis of experience and ability of the learning. Famous psychologist Donald Hebb designed the hypothesis in the year 1940. It was formed on the learning process, which was founded on the neural plasticity mechanism. This hypothesis later named as the hebbian learning. His method of learning was considered as the unsupervised learning.
First computational model based on the hebbian learning was named as the calculator. One of the synaptic update mechanisms, which were the part of the hebbian learning, was found associated with the artificial neural network. It was a special artificial neural network as it was consists of the three layers and often called as the power network. Neural network for the recognition of the handwriting was defined under hebbian learning was input neurons are acting like a pixels, which are activated as in the form of the image.