Articles by Alessandro Londei

Understanding the world around us is usually a hard task. All dynamically evolving phenomena in the natural world are produced by a strong interaction among a great number of causes and, often, only a few amounts of them are visible or measurable. Moreover, the phenomena may be so widely distributed over space and time, like the weather evolution, that only a small number of measurements can be taken, making the understanding of the overall system difficult and approximated. Some characteristics of systems can produce a very strange behaviour, even when the elements constituting the system are a small number. All these elements and their mutual interaction can produce the so-called complexity. Artificial neural networks (ANNs) form an interesting class of dynamic systems, as a paradigm of natural and spontaneous computation. ANNs are founded on bases inspired by the neurophysiological nature of neurons and their mutual connectivity. In this paper the historical reasons that led to the former mathematical models of neuron and connectionist topologies will be detailed. Over time, they have evolved through the feed-forward systems, Self-Organizing Maps, the associative memories up to the latest models in artificial cerebral cortex.