Lecture 8 Artificial neural networks: Unsupervised learning ■ Introduction ■ Hebbian learning ■ Generalised Hebbian learning algorithm ■ Competitive learning ■ Self-organising computational map: Kohonen network ■ Summary  Negnevitsky, Pearson Education, 2002 Introduction The main property of a neural network is an ability to learn from its environment, and to improve its performance through learning. So far we have considered supervised or active learning − learning with an external “teacher” or a supervisor who presents a training set to the network. But another type of learning also exists: unsupervised learning.  Negnevitsky, Pearson Education, 2002 ■ In contrast to supervised learning, unsupervised or self-organised learning does not require an external teacher. During the training session, the neural network receives a number of different input patterns, discovers significant features in these patterns and learns how to classify input data into appropriate categories. Unsupervised learning tends to follow the neuro-biological organisation of the brain. ■ Unsupervised learning algorithms aim to learn rapidly and can be used in real-time.  Negnevitsky, Pearson Education, 2002 Hebbian learning In 1949, Donald Hebb proposed one of the key ideas in biological learning, commonly known as Hebb’s Law. Hebb’s Law states that if neuron i is near enough to excite neuron j and repeatedly participates in its activation, the synaptic connection between these two neurons is strengthened and neuron j becomes more sensitive to stimuli from neuron i.  Negnevitsky, Pearson Education, 2002 Hebb’s Law can be represented in the form of two rules: 1. If two neurons on either side of a connection are activated synchronously, then the weight of that connection is increased. 2. If two neurons on either side of a connection are activated asynchronously, then the weight of that connection is decreased. Hebb’s Law provides the basis for learning without a teacher. L