Hardware inaccuracy and imprecision are important factors when implementing neural algorithms. This text presents a study of synaptic weight noise as a typical fault model for analogue VLSI realizations of MLP neural networks, and examines the implications for learning and network performance.

Reflecting the Society for Chaos Theory in Psychology and the Life Sciences as a whole, this text consists of applications of nonlinear methodology in psychophysics, neurophysiology, business and social science, as well as applications of the nonlinear paradigm.