From Neurophysiology to Neural Nets

Abstract

The study of the function of excitable tissues in response to electrical signals constitutes the basis for understanding and reproducing phenomena of specific neural systems. Of course, the collection of vast numbers of experimental data, enriched by experience, helped to derive accurate mathematical models of neural processing, which thereby contributed to the design of artificial models of biological neural networks. The most important milestone regarding this field of study was the seminal work performed by the Nobel prize winners Hodgkin and Huxley in 1952, who, by their work, captured the characteristics of the active behaviours of neural membranes and their threshold dependent nonlinear dynamics. Since then, several outstanding mathematical models of neural signals and their propagation in active and nonlinear mediums have been developed. The term “artificial neural nets” is currently used to mean all the architecture, from software to hardware that is built on biological understandings and devoted to the tasks typically performed by living creatures. These tasks include learning from examples, generalisation ability, pattern recognition, memory and so on. Another increasingly important field is the specific design of biologically inspired neural architectures that are able to solve specific important tasks such as locomotion generation and control. We report here on our recent research activity in this direction.