TY - GEN ID - cogprints1108 UR - http://cogprints.org/1108/ A1 - Spratling, Michael Y1 - 1999/// N2 - Unsupervised learning is an important property of the brain and of many artificial neural networks. A large variety of unsupervised learning algorithms have been proposed. This paper takes a different approach in considering the architecture of the neural network rather than the learning algorithm. It is shown that a self-organising neural network architecture using pre-synaptic lateral inhibition enables a single learning algorithm to find distributed, local, and topological representations as appropriate to the structure of the input data received. It is argued that such an architecture not only has computational advantages but is a better model of cortical self-organisation. KW - neural networks KW - lateral inhibition KW - self-organisation KW - unsupervised learning KW - neural coding KW - factorial coding TI - Presynaptic lateral inhibition provides a better architecture for self-organising neural networks SP - 285 AV - public EP - 301 ER -