The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …

This paper surveys many circuit complexity results for networks of perceptrons, focusing on
those presented over the last ten years. The first part reviews general theoretical results …

This paper aims to examine the circuit complexity of sigmoid activation feedforward artificial
neural networks by placing them amongst several classic Boolean and threshold gate circuit …

This section considers some of the alternative approaches towards modeling biological
functions by digital circuits. It starts by introducing some circuit complexity issues and …

Limited precision neural networks are better suited for hardware implementations. Several
researchers have proposed various algorithms which are able to train neural networks with …

The paper overviews recent developments concerning optimal (from the point of view of size
and depth) implementations of comparison using threshold gates. We detail a class of …

The starting points of this paper are two size-optimal solutions:(i) one for implementing
arbitrary Boolean functions [1]; and (ii) another one for implementing certain sub-classes of …

The paper presents a theoretical proof revealing an intrinsic limitation of digital VLSI
technology: its inability to cope with highly connected structures (eg neural networks). We …

The paper overviews recent developments of a VLSI-friendly, constructive algorithm as well
as detailing two extensions. The problem is to construct a neural network when m examples …

In this paper we consider binary neurons having a threshold nonlinear transfer function and
detail a novel direct design algorithm as an alternative to the classical learning algorithms …