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Reviewer: Peter C. Patton

This book is a worthy sequel to Introduction to VLSI systems by Mead and Conway [1]. Mead is not only an outstanding researcher, he is also a consummate teacher willing to take readers ignorant of electronics and lead them clearly but crisply to his own research frontiers. After a brief introduction, chapter 2 explains the basic elements of electronics: resistance, conductance, capacitance, charge, thermal motion, and the Boltzmann distribution. Chapter 3 explains transistor physics using an analogy employing “Boltzmann hydraulics”; it closes with the circuit properties of semiconductors and the basic circuit of the current mirror, the fundamental building block of CMOS analog circuits. Chapter 4 explains the microanatomy of the biological neuron and describes its function in electrochemical terms with reference to an equivalent (electric) circuit. Chapter 5 returns to analog electronics to develop the circuit of a transconductance amplifier that computes the tanh function as the CMOS analog to the gain characteristic of the neural axon. Chapter 6 supplements the reader's analog circuit repertoire with circuits to perform addition, subtraction, absolute value, and multiplication, and finishes with exponentiation, logarithms, and the square root. Having developed building block circuit elements that can emulate the functions of natural neurons, Mead turns to the emulation of dendritic functions in chapter 7. He shows how to aggregate signals within the much lower fan-in and fan-out capabilities of electronic circuits as opposed to biological neural networks. The chapter ends with an analysis of the hexagonal two-dimensional resistive network that will be the basis of the artificial retina developed in chapter 15. Chapter 8 introduces the time-varying qualities of analog circuits starting with the RC time constant, complex numbers, and transfer response to input functions. The next three chapters build on this basis to develop CMOS circuits for differentiation and integration and for the analysis of both small and large signal behavior. Chapter 12 completes the expository part of the book with the development of an artificial (silicon) neuron. The final four chapters of the book are devoted to examples of work done by Mead and his graduate students. Chapter 13 describes the SeeHear chip, which transforms visual events into acoustic events. Chapter 14 describes an optical motion sensor and chapter 15 presents the silicon retina. Chapter 16 describes the silicon cochlea. The book has four appendices. The first is a description of CMOS fabrication taken from Mead and Conway [1], the second is entitled “Fine points of transistor physics,” the third presents more detail on the resistive networks that are basic to Mead's approach to silicon emulation of neural network functionality, and the fourth is entitled “Complexity in neural systems.” I recommend this book for all computer scientists and engineers. Lack of prior training or experience in analog circuits or VLSI technology is no excuse to avoid it. The book is self-contained and starts by assuming only that you can read. Everything else is supplied: basic physics by analogy, fascination, motivation to continue, and applications. The book is reasonably priced but you probably will not find it (yet) in the “computers” section at B. Dalton.